Machine-learning models can fail when they try to make predictions for individuals who were underrepresented in the datasets they were trained on.For instance, a model that predicts the best treatment option for someone with a chronic disease may be trained using a dataset that contains mostly male patients. That model might make incorrect predictions for female patients when deployed in a hospital.To improve outcomes, engineers can try balancing the training dataset by removing data points until all subgroups are represented equally. While dataset balancing is promising, it often requires removing large amount of data, hurting the model’s overall performance.MIT researchers developed a new technique that identifies and removes specific points in a training dataset that contribute most to a model’s failures on minority subgroups. By removing far fewer datapoints than other approaches, this technique maintains the overall accuracy of the model while improving its performance regarding underrepresented groups.In addition, the technique can identify hidden sources of bias in a training dataset that lacks labels. Unlabeled data are far more prevalent than labeled data for many applications.This method could also be combined with other approaches to improve the fairness of machine-learning models deployed in high-stakes situations. For example, it might someday help ensure underrepresented patients aren’t misdiagnosed due to a biased AI model.“Many other algorithms that try to address this issue assume each datapoint matters as much as every other datapoint. In this paper, we are showing that assumption is not true. There are specific points in our dataset that are contributing to this bias, and we can find those data points, remove them, and get better performance,” says Kimia Hamidieh, an electrical engineering and computer science (EECS) graduate student at MIT and co-lead author of a&nbsp;<a href="https://arxiv.org/pdf/2406.16846" target="_blank">paper on this technique</a>.She wrote the paper with co-lead authors Saachi Jain PhD ’24 and fellow EECS graduate student Kristian Georgiev; Andrew Ilyas MEng ’18, PhD ’23, a Stein Fellow at Stanford University; and senior authors Marzyeh Ghassemi, an associate professor in EECS and a member of the Institute of Medical Engineering Sciences and the Laboratory for Information and Decision Systems, and Aleksander Madry, the Cadence Design Systems Professor at MIT. The research will be presented at the Conference on Neural Information Processing Systems.<h2>Removing bad examples</h2>Often, machine-learning models are trained using huge datasets gathered from many sources across the internet. These datasets are far too large to be carefully curated by hand, so they may contain bad examples that hurt model performance.Scientists also know that some data points impact a model’s performance on certain downstream tasks more than others.The MIT researchers combined these two ideas into an approach that identifies and removes these problematic datapoints. They seek to solve a problem known as worst-group error, which occurs when a model underperforms on minority subgroups in a training dataset.The researchers’ new technique is driven by prior work in which they introduced a method, called&nbsp;<a href="https://arxiv.org/pdf/2303.14186">TRAK</a>, that identifies the most important training examples for a specific model output.For this new technique, they take incorrect predictions the model made about minority subgroups and use TRAK to identify which training examples contributed the most to that incorrect prediction.“By aggregating this information across bad test predictions in the right way, we are able to find the specific parts of the training that are driving worst-group accuracy down overall,” Ilyas explains.Then they remove those specific samples and retrain the model on the remaining data.Since having more data usually yields better overall performance, removing just the samples that drive worst-group failures maintains the model’s overall accuracy while boosting its performance on minority subgroups.<h2>A more accessible approach</h2>Across three machine-learning datasets, their method outperformed multiple techniques. In one instance, it boosted worst-group accuracy while removing about 20,000 fewer training samples than a conventional data balancing method. Their technique also achieved higher accuracy than methods that require making changes to the inner workings of a model.Because the MIT method involves changing a dataset instead, it would be easier for a practitioner to use and can be applied to many types of models.It can also be utilized when bias is unknown because subgroups in a training dataset are not labeled. By identifying datapoints that contribute most to a feature the model is learning, they can understand the variables it is using to make a prediction.“This is a tool anyone can use when they are training a machine-learning model. They can look at those datapoints and see whether they are aligned with the capability they are trying to teach the model,” says Hamidieh.Using the technique to detect unknown subgroup bias would require intuition about which groups to look for, so the researchers hope to validate it and explore it more fully through future human studies.They also want to improve the performance and reliability of their technique and ensure the method is accessible and easy-to-use for practitioners who could someday deploy it in real-world environments.“When you have tools that let you critically look at the data and figure out which datapoints are going to lead to bias or other undesirable behavior, it gives you a first step toward building models that are going to be more fair and more reliable,” Ilyas says.

MIT researchers developed an AI debiasing technique that improves the fairness of a machine-learning model by boosting its performance for subgroups that are underrepresented in its training data, while maintaining its overall accuracy. Credit: José-Luis Olivares, iStock

A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.

Researchers reduce bias in AI models while preserving or improving accuracy

The Global Photonics Engineering Contest has captured the imagination of engineers worldwide, inviting innovative minds to showcase their ideas in the ever-evolving field of Integrated photonics. As part of our spotlight on the contest’s jury members, we had the privilege of speaking with Jörn Epping, Epiphany's co-founder and an integrated photonics expert.&nbsp;<a href="https://www.wevolver.com/article/global-photonics-engineering-contest?utm_source=photon+delta&utm_medium=articles&utm_campaign=photonics"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1734529434210-1734529434210.png" class="fr-fic fr-dii"></a><a href="https://www.wevolver.com/article/global-photonics-engineering-contest?utm_source=photon+delta&utm_medium=articles&utm_campaign=photonics" class="button-main-action">Learn more about the challenge here.</a>Jörn Epping brings a wealth of experience to the jury, with an impressive career spanning academic research, engineering design, and entrepreneurial leadership. With a background in physics from the University of Münster and a PhD from the University of Twente, his career has been deeply rooted in photonics innovation. After roles at LioniX and a stint as Managing director at QuiX Quantum GmbH, Epping co-founded Epiphany, a design house focused on integrated photonics and commercialization.Epping shared valuable insights on what makes a strong submission and how engineers can maximize their chances of success in this prestigious challenge.<blockquote>“What excites me most about this contest is seeing ideas come to life,” he explained. “I love witnessing how new concepts translate into designs and eventually into chips.”</blockquote><h3 dir="ltr">What Makes a Submission Stand Out?</h3>When evaluating contest submissions, Epping emphasizes two primary qualities: creativity and feasibility.<ul><li dir="ltr">Creativity: “Is it unique? Does it push the boundaries of what we expect?” Epping believes that engineering contests are an opportunity to think outside the box, unbound by the constraints of existing markets.</li><li dir="ltr">Feasibility: “Is the idea practical and producible with today’s technology? Can it be manufactured?” Submissions should demonstrate an understanding of current technological capabilities and scalability, whether the project involves mass production or niche applications.</li></ul>Epping also highlighted the importance of aligning the proposed solution with a clear use case. “There are two key paths: either creating a cost-effective product for mass production or designing a specialized, high-value solution that only chips can achieve, such as for satellites or harsh environments.”<h3 dir="ltr">Tips for Crafting a Strong Submission</h3>To make your entry shine, Epping recommends following a structured design journey:<ol><li dir="ltr">Sketch Your Idea: Begin with a clear schematic outlining the concept and its intended functionality.</li><li dir="ltr">Leverage Available Technologies: Identify components or technologies that can bring your concept to life. Consider whether using existing building blocks is feasible or if additional development is required.</li><li dir="ltr">Provide Evidence: Where possible, include simulations, calculations, or performance estimates to substantiate your idea.</li><li dir="ltr">Plan for Manufacturing: Consider the entire development chain, including packaging and testing. “If you don’t plan for these during the design phase, it may be too late to address them later,” Epping cautioned.</li></ol><h3 dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1734347233028-AIM+Photonics+-+Photonic+Integrated+Circuits+1+%28PIC1%29+1-48+screenshot.png" width="602" height="339" class="fr-fic fr-dii"></h3>Image credit: AimPhotonics<h3 dir="ltr">Encouraging Future Innovators</h3>The Global Photonics Engineering Contest is not just about competition; it’s about fostering creativity and innovation in a field poised to transform industries. Epping encourages participants to explore new application areas for integrated photonics rather than competing in highly saturated markets like telecommunications.For aspiring engineers or those looking to pivot into photonics, Epping’s advice is simple: “Stay curious. Focus on developing a solid understanding of the fundamentals, and don’t be afraid to explore uncharted territories.”<h3 dir="ltr">Why This Contest Stands Out</h3><blockquote>“What makes this contest special is the opportunity to see truly out-of-the-box ideas,” Epping said. “It’s about sparking creativity and bridging the gap between concept and commercialization.”</blockquote>With insights like these, participants are well-equipped to craft compelling submissions that showcase not only their technical prowess but also their ability to innovate and solve real-world problems. As Epping and his fellow jury members review the entries, they’ll be looking for projects that embody the transformative potential of photonics.<h3 dir="ltr">Final tips for entrants</h3><ol data-stringify-type="ordered-list" data-list-tree="true" data-indent="0" data-border="0"><li data-stringify-indent="0" data-stringify-border="0">Apply before December 31 to receive personalized feedback from the PhotonDelta team.</li><li data-stringify-indent="0" data-stringify-border="0">Ensure your entry is legible and conveys your ideas as succinctly and clearly as possible.</li><li data-stringify-indent="0" data-stringify-border="0">Don’t be afraid to reach out. We are here to answer any of your content questions. Just email us at:&nbsp;<a target="_blank" data-stringify-link="mailto:challenges@wevolver.com" data-sk="tooltip_parent" href="mailto:challenges@wevolver.com" rel="noopener noreferrer" id="isPasted">challenges@wevolver.com</a></li></ol>Good luck to all participants, and may your ideas shine as brightly as the photonic innovations they inspire!<h1 dir="ltr">Global Photonics Engineering Contest</h1><a href="https://www.wevolver.com/article/global-photonics-engineering-contest?utm_source=photon+delta&utm_medium=articles&utm_campaign=photonics"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1734529513573-1734529513573.png" class="fr-fic fr-dii"></a>The Global Photonics Engineering Contest invites engineers, innovators, and startups to push the boundaries of Photonic Integrated Circuits (PIC) and create applications that address complex engineering challenges and push the boundaries of the industry. Whether you’re in semiconductors, data &amp; telecom, medtech, or agritech, we want to see your breakthrough idea that can harness the power of PICs to change the world.<ul><li dir="ltr">Prize:&nbsp;€50,000 worth of services from the PhotonDelta ecosystem to to make your idea market-ready + a chance to receive up to €2 million in pre-seed funding.</li><li dir="ltr">Audience: Open to all engineers, researchers, OEMs, hardware manufacturers, startups/scale-ups, research teams, and students working on innovative PIC applications.</li><li dir="ltr">Early Bird Opportunity: All submissions received by 31st December 2024, will receive&nbsp;personalized feedback from PhotonDelta engineers.</li><li dir="ltr">End date: March 3rd 2025</li></ul><a href="https://www.wevolver.com/article/global-photonics-engineering-contest?utm_source=photon+delta&utm_medium=articles&utm_campaign=photonics" class="button-main-action">Submit now!</a>

As part of our spotlight on the contest’s jury members, we had the privilege of speaking with Jörn Epping, Epiphany's co-founder and an integrated photonics expert. 

Tips and Insights from the Global Photonics Engineering Contest Jury

Photonic integrated circuits (PICs) are emerging as transformative tools in industries ranging from telecommunications to healthcare. With applications spanning telecom, healthcare, computing, and beyond, PICs hold the potential to revolutionize industries and solve some of society’s most pressing challenges.&nbsp;To inspire engineers and innovators, the&nbsp;Global Photonics Engineering Contest, in collaboration with Photon Delta, is offering a platform to showcase groundbreaking ideas.Wevolver spoke with Twan Korthorst, CEO of <a href="https://neworigin.eu/en/">New Origin</a> and a passionate advocate for photonic technologies, to provide deeper insight into the impact of PICs, the journey into the industry, and advice for succeeding in the challenge.<h2 dir="ltr">Unlocking new possibilities and innovative applications with PICs</h2>Integrated photonic chips have the potential to significantly impact multiple industries, building on their foundational role in telecommunications. As Twan Korthorst explains, the integrated photonics journey began with long-haul telecom applications, where optical fibers replaced copper links and satellite communication, enabling high-bandwidth, low-energy data transport. The industry has since matured, transitioning from traditional telecom to datacom, where optical fibers now connect data centers and even chips, such as CPUs and GPUs, addressing challenges like bandwidth, latency, and energy consumption.&nbsp;Beyond telecommunications, the advancements in photonic ICs have unlocked creativity in new applications. From shrinking bulky LiDAR systems for autonomous vehicles and industrial robots to developing sensitive biosensors and miniaturized light engines for AR/VR devices, the possibilities are vast.&nbsp;Investments in datacom have driven the maturity of photonic technology, with improved design tools and broader access to manufacturing facilities. As Korthorst highlights, this momentum is paving the way for further innovation, lower costs, and a significant expansion of photonic IC applications in the coming decade.<iframe width="640" height="360" src="https://www.youtube.com/embed/rCuJEx6kmyQ?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;</iframe><h2 dir="ltr">Challenges in Scaling Photonic Innovation</h2>Despite their promise, PICs face hurdles that must be addressed to accelerate adoption. According to Korthorst, two primary challenges stand out:<ol><li dir="ltr">Cost and Scalability The cost of prototyping and scaling remains high due to limited manufacturing facilities. Multiproject wafer (MPW) runs, where multiple designs share fabrication costs, offer an accessible entry point for smaller organizations or first-time designers. However, scaling beyond prototypes to higher volumes continues to be challenging.“New Origin and other initiatives aim to address this by creating scalable manufacturing solutions,” Korthorst added. “This will drive down costs and enable broader industry adoption.”</li><li dir="ltr">Education and Talent Development The field of photonics demands specialized knowledge. While engineers from physics, chemistry, or electrical backgrounds bring valuable expertise, many lack experience in chip design and manufacturing. “Education is key,” Korthorst emphasized. “Training programs and collaborations between universities, research institutions, and design houses are helping bridge this gap.”</li></ol><h2 dir="ltr">How to Break into the Photonics Industry</h2>For engineers aspiring to enter the photonics industry, Korthorst highlighted two primary paths:<ul><li dir="ltr">Theory Experts: Engineers with backgrounds in optics, chemical sensing, or physics have a solid theoretical understanding. However, they can benefit from training programs offered by organizations like AIM Photonics (U.S.) and JePPIX (Europe) to improve their knowledge of chip design. Partnering with design houses like Bright Photonics or Epiphany can also help engineers begin to translate big ideas into PIC designs.</li><li dir="ltr">Chip Designers: Engineers experienced in analog or RF chip design can leverage their existing knowledge to transition into integrated photonics. Because they have the foundation knowledge, they can explore a shorter path to the PIC industry by collaborating with foundries like SMART Photonics or LioniX to gain direct access to the tools and expertise needed to validate their concepts.</li></ul>Regardless of their background, Korthorst encouraged engineers to engage with the ecosystem, attend training sessions, and explore opportunities to enter the industry.<h2 dir="ltr">Crafting a Winning Submission for the Challenge</h2>The&nbsp;Global Photonics Engineering Contest&nbsp;offers a unique opportunity for engineers to bring their ideas to life. With a prize that includes resources to design, prototype, and manufacture a PIC, participants can focus on innovation rather than logistical hurdles. Korthorst shared his advice for crafting a standout submission:<ol><li dir="ltr">Define the Problem Clearly articulate the challenge you aim to address. Explain how PICs can enhance current solutions or unlock new possibilities.</li><li dir="ltr">Showcase Impact Highlight the societal or industrial importance of your idea. Whether it’s advancing healthcare or improving energy efficiency, focus on the broader implications.</li><li dir="ltr">Leverage Collaboration Don’t worry about mastering the technical details alone. The contest’s partners, including leading foundries and design houses, are there to guide you.</li></ol><blockquote>“Set the jury on fire with a great idea,” Korthorst advised. “Focus on the problem and the bigger picture. If your vision excites others, the technical support will follow.”</blockquote><h2 dir="ltr">Your Invitation to Innovate</h2>The Global Photonics Engineering Contest is not just a competition; it’s an invitation to engage with a vibrant ecosystem and contribute to the future of technology. With the contest closing in March, now is the time to submit your ideas and join a community of innovators shaping the next wave of photonic advancements.<a href="https://www.wevolver.com/article/global-photonics-engineering-contest?utm_source=photon+delta&utm_medium=articles&utm_campaign=photonics"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzM0MzQxMTAzNzE5LXBob3RvbiBkZWx0YSAtIE5PVyBMSVZFICgxOTIwIHggNTAwIHB4KSAoMSkucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"></a><blockquote>“There’s so much promise in this technology,” Korthorst concluded. “It’s an exciting time to get involved and make an impact.”</blockquote>Whether you’re an industry veteran or a curious newcomer, the challenge is your chance to explore the potential of PICs and turn your ideas into reality.The Global Photonics Engineering Contest invites engineers, innovators, and startups to push the boundaries of Photonic Integrated Circuits (PIC) and create applications that address complex engineering challenges and push the boundaries of the industry. Whether you’re in semiconductors, data &amp; telecom, medtech, or agritech, we want to see your breakthrough idea that can harness the power of PICs to change the world.<ul><li dir="ltr">Prize: €50,000 worth of services from the PhotonDelta ecosystem to to make your idea market-ready + a chance to receive up to €2 million in pre-seed funding.</li><li dir="ltr">Audience: Open to all engineers, researchers, OEMs, hardware manufacturers, startups/scale-ups, research teams, and students working on innovative PIC applications.</li><li dir="ltr">Early Bird Opportunity:&nbsp;All submissions received by 31st December 2024, will receive personalized feedback from PhotonDelta engineers.</li><li dir="ltr">End date: March 3rd 2025</li></ul><a href="https://www.wevolver.com/article/global-photonics-engineering-contest?utm_source=photon+delta&utm_medium=articles&utm_campaign=photonics" class="button-main-action">Find out more here!</a>

Twan Korthorst, CEO of New Origin, highlights their transformative potential in a conversation with Wevolver about the Global Photonics Engineering Contest, hosted in collaboration with PhotonDelta.

The Role of Photonic Integrated Circuits, Breaking into the Industry, and Crafting a Winning Submission

<h3 data-pm-slice="1 1 []" id="isPasted">The Sphera Simulation: A Supreme Tool in Drone and Robotics Development</h3><h4>Revolutionizing Testing, Modeling, and Optimization in Complex Engineering Environments</h4>The Sphera Simulation stands as a revolutionary platform in the field of drone and robotics development, delivering unmatched capabilities for modeling, testing, and optimization. Unlike conventional simulation systems, which often suffer from limited customization or generic dynamics, Sphera integrates advanced photorealistic visualization, precise physical modeling, and highly adaptable sensor configurations. These features empower engineers to craft high-fidelity digital twins that mirror the intricate behaviors of real-world systems, thus minimizing reliance on costly and time-intensive hardware trials.Unparalleled Algorithm Development and EfficiencySphera transcends standard simulation platforms by providing a robust and versatile environment for algorithm development. Developed by Dr. Yam Geva, who holds a PhD in Mechanical Engineering, and Omri Eitan, an accomplished software engineer, Sphera facilitates the rapid iteration of advanced algorithms. Compared to traditional testing frameworks, which often require significant hardware deployment or field trials, Sphera accelerates the transition from conceptual design to operational implementation. This streamlined workflow not only reduces development timelines but also significantly mitigates financial and operational risks, setting Sphera apart as a premier tool for algorithm refinement.While other alternative systems offer basic functionalities, they lack the depth of customization and photorealistic capabilities that define Sphera. For example, Sphera’s ability to simulate intricate environmental conditions, such as sea motion, wind dynamics, and variable visibility, ensures that it meets the rigorous demands of cutting-edge drone navigation and control algorithms. These features render Sphera particularly advantageous for developers seeking precision and reliability in complex scenarios. <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1734329607553-Sphera+by+Robotican+-+Weather+change+control.jpg" style="width: 100%px;" class="fr-fic fr-dib"> Enhanced Presentation and Stakeholder EngagementBeyond its technical prowess, Sphera is an invaluable asset for external engagement. The platform’s capacity to generate photorealistic simulations and elaborate operational scenarios allows organizations to present their technologies with clarity and impact. Stakeholders, including clients, investors, and collaborators, can experience a visually immersive demonstration of system performance under real-world conditions. This level of engagement not only builds trust but also fosters deeper collaboration and alignment on project objectives. Compared to generic simulation tools, Sphera’s advanced visualization capabilities significantly elevate the quality of stakeholder presentations. <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1734329323628-photo_9_2024-10-14_17-04-54.jpg" style="width: 100%px;" class="fr-fic fr-dib"> Daily Integration in Advanced WorkflowsThe Sphera simulation platform has become an indispensable tool in daily engineering workflows. Its exceptional image quality and dynamic fidelity provide unparalleled input for image-processing algorithms, ensuring high accuracy in system evaluation. Unlike conventional platforms, Sphera’s seamless integration with widely used development tools, such as Visual Studio (VS), enables engineers of all experience levels to adopt it with ease. Furthermore, Robotican’s dedicated technical support enhances the platform’s reliability, ensuring uninterrupted operations in demanding applications.Adaptability for Specialized RequirementsSphera’s flexibility is another key factor that distinguishes it from competing platforms. When unique operational needs arise, such as those encountered by DroneLander, the Sphera framework can be tailored to meet specific requirements. Robotican’s responsive and professional support ensures that the platform evolves alongside user innovations. This adaptability underscores Sphera’s commitment to serving as a versatile and forward-looking tool for diverse engineering challenges. <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1734329359319-%D7%9B%D7%9C%D7%91+%D7%A1%D7%A4%D7%A8%D7%94+-+frame+at+0m14s.jpg" style="width: 100%px;" class="fr-fic fr-dib"> Advanced Environmental Modeling for Complex ScenariosOne of Sphera’s most critical strengths lies in its ability to simulate complex environmental conditions with exceptional accuracy. By incorporating dynamic factors such as sea motion, wind effects, and lighting variability, the platform enables thorough testing of image-processing and navigation algorithms under highly realistic scenarios. This capability is particularly valuable for missions involving drone landings on maritime platforms, where environmental unpredictability poses significant challenges. Compared to other simulation tools, Sphera’s comprehensive environmental modeling provides an unmatched level of realism, reducing the likelihood of costly errors during physical testing.Moreover, Sphera’s continuous availability ensures that it remains an integral component of daily development workflows. Whether used for minute-by-minute testing or long-term project evaluations, the platform delivers consistent performance, enabling engineers to refine their designs without disruption.ConclusionThe Sphera Simulation is a transformative tool that sets a new standard for drone and robotics development. By offering a fully customizable and photorealistic environment for testing, it empowers organizations to innovate with unprecedented efficiency and confidence. In comparison to other systems, Sphera’s advanced modeling capabilities, adaptability, and robust support infrastructure firmly establish it as the supreme solution for addressing the complexities of modern engineering challenges. For any organization seeking to push the boundaries of technology, Sphera is not merely a tool but a catalyst for innovation.

Revolutionizing Testing, Modeling, and Optimization in Complex Engineering Environments

The Sphera Simulation: A Supreme Tool in Drone and Robotics Development

If someone advises you to “know your limits,” they’re likely suggesting you do things like exercise in moderation. To a robot, though, the motto represents learning constraints, or limitations of a specific task within the machine’s environment, to do chores safely and correctly.For instance, imagine asking a robot to clean your kitchen when it doesn’t understand the physics of its surroundings. How can the machine generate a practical multistep plan to ensure the room is spotless? Large language models (LLMs) can get them close, but if the model is only trained on text, it’s likely to miss out on key specifics about the robot’s physical constraints, like how far it can reach or whether there are nearby obstacles to avoid. Stick to LLMs alone, and you’re likely to end up cleaning pasta stains out of your floorboards.To guide robots in executing these open-ended tasks, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) used vision models to see what’s near the machine and model its constraints. The team’s strategy involves an LLM sketching up a plan that’s checked in a simulator to ensure it’s safe and realistic. If that sequence of actions is infeasible, the language model will generate a new plan, until it arrives at one that the robot can execute.This trial-and-error method, which the researchers call “Planning for Robots via Code for Continuous Constraint Satisfaction” (PRoC3S), tests long-horizon plans to ensure they satisfy all constraints, and enables a robot to perform such diverse tasks as writing individual letters, drawing a star, and sorting and placing blocks in different positions. In the future, PRoC3S could help robots complete more intricate chores in dynamic environments like houses, where they may be prompted to do a general chore composed of many steps (like “make me breakfast”).“LLMs and classical robotics systems like task and motion planners can’t execute these kinds of tasks on their own, but together, their synergy makes open-ended problem-solving possible,” says PhD student Nishanth Kumar SM ’24, co-lead author of a new paper about PRoC3S. “We’re creating a simulation on-the-fly of what’s around the robot and trying out many possible action plans. Vision models help us create a very realistic digital world that enables the robot to reason about feasible actions for each step of a long-horizon plan.”The team’s work was presented this past month in a paper shown at the Conference on Robot Learning (CoRL) in Munich, Germany.<iframe width="640" height="360" src="https://www.youtube.com/embed/hBq0EeFSsPo?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>Teaching a robot its limits for open-ended chores. MIT CSAILThe researchers’ method uses an LLM pre-trained on text from across the internet. Before asking PRoC3S to do a task, the team provided their language model with a sample task (like drawing a square) that’s related to the target one (drawing a star). The sample task includes a description of the activity, a long-horizon plan, and relevant details about the robot’s environment.But how did these plans fare in practice? In simulations, PRoC3S successfully drew stars and letters eight out of 10 times each. It also could stack digital blocks in pyramids and lines, and place items with accuracy, like fruits on a plate. Across each of these digital demos, the CSAIL method completed the requested task more consistently than comparable approaches like <a href="https://arxiv.org/pdf/2403.11552">“LLM3”</a> and&nbsp;<a href="https://arxiv.org/pdf/2209.07753">“Code as Policies”</a>. The CSAIL engineers next brought their approach to the real world. Their method developed and executed plans on a robotic arm, teaching it to put blocks in straight lines. PRoC3S also enabled the machine to place blue and red blocks into matching bowls and move all objects near the center of a table.Kumar and co-lead author Aidan Curtis SM ’23, who’s also a PhD student working in CSAIL, say these findings indicate how an LLM can develop safer plans that humans can trust to work in practice. The researchers envision a home robot that can be given a more general request (like “bring me some chips”) and reliably figure out the specific steps needed to execute it. PRoC3S could help a robot test out plans in an identical digital environment to find a working course of action — and more importantly, bring you a tasty snack.For future work, the researchers aim to improve results using a more advanced physics simulator and to expand to more elaborate longer-horizon tasks via more scalable data-search techniques. Moreover, they plan to apply PRoC3S to mobile robots such as a quadruped for tasks that include walking and scanning surroundings.“Using foundation models like ChatGPT to control robot actions can lead to unsafe or incorrect behaviors due to hallucinations,” says The AI Institute researcher Eric Rosen, who isn’t involved in the research. “PRoC3S tackles this issue by leveraging foundation models for high-level task guidance, while employing AI techniques that explicitly reason about the world to ensure verifiably safe and correct actions. This combination of planning-based and data-driven approaches may be key to developing robots capable of understanding and reliably performing a broader range of tasks than currently possible.”Kumar and Curtis’ co-authors are also CSAIL affiliates: MIT undergraduate researcher Jing Cao and MIT Department of Electrical Engineering and Computer Science professors Leslie Pack Kaelbling and Tomás Lozano-Pérez. Their work was supported, in part, by the National Science Foundation, the Air Force Office of Scientific Research, the Office of Naval Research, the Army Research Office, MIT Quest for Intelligence, and The AI Institute.

PhD students Aidan Curtis (left) and Nishanth Kumar. To help robots execute open-ended tasks safely, the researchers used vision models to see what’s near the machine and model its constraints. Their “PRoC3S” strategy has an LLM sketch up an action plan that’s checked in a simulator to ensure it will work in the real world. Credits: Mike Grimmett/MIT CSAIL

The “PRoC3S” method helps an LLM create a viable action plan by testing each step in a simulation. This strategy could eventually aid in-home robots to complete more ambiguous chore requests.

Teaching a robot its limits, to complete open-ended tasks safely

Even the smallest design flaw or mold imperfection can lead to big quality issues in injection molding—warping, sink marks, or weak structural integrity. High-quality parts require more than just a well-thought-out design; they demand a proactive approach to ensure manufacturability. This is where Mold Flow Analysis (MFA) steps in, providing critical insights to refine designs, reduce defects, and pave the way for efficient production.<h2 dir="ltr">What Is Mold Flow Analysis?</h2>Mold Flow Analysis is a simulation technique that predicts the flow of molten material within a mold cavity during injection molding. Using advanced software tools, it evaluates critical parameters such as flow patterns, cooling efficiency, pressure distribution, and potential defects like warping or sink marks. This process helps engineers optimize designs and material selection, significantly reducing the need for physical prototypes and minimizing risks during production.<h2 dir="ltr">When Should Mold Flow Analysis Be Used?</h2>For projects requiring precision or complex geometries, Mold Flow Analysis is highly recommended. It provides crucial insights to ensure the design is manufacturable, minimizes defects, and helps save time and costs in the long run. By identifying potential issues early, it reduces trial-and-error cycles, ensuring smoother production and better-quality parts.<h2 dir="ltr">How Mold Flow Analysis Supports Design for Manufacturability (DFM)</h2>Mold Flow Analysis plays a pivotal role in optimizing the design and manufacturing process by addressing potential challenges early. Here are keyways MFA contributes to&nbsp;Design for Manufacturability (DFM):<ul><li dir="ltr"><h3>Refining Gate Placement</h3></li></ul>Proper gate placement is critical for ensuring a smooth and consistent material flow within the mold. By simulating different gate positions, MFA identifies the optimal placement to minimize weld lines, air traps, and other flow-related defects. This not only improves part quality but also enhances the structural integrity and appearance of the final product. For example, poor gate placement can lead to uneven filling, which may weaken specific areas of the part or result in visible imperfections.<ul><li dir="ltr" style="font-weight: bold;">Balancing Material Distribution</li></ul>Mold Flow Analysis evaluates how material fills the mold cavity, identifying regions where uneven distribution might occur. If one section fills faster than another, it can lead to defects like overpacking or underfilling. MFA allows engineers to adjust runner and gate designs to balance the flow, ensuring the material is distributed evenly throughout the cavity. This step is essential for achieving consistent wall thickness, preventing sink marks, and reducing the risk of part failure under stress.<ul><li dir="ltr" style="font-weight: bold;">Improving Cooling Strategies</li></ul>The cooling phase is one of the most critical stages in injection molding, as uneven cooling can lead to warping or dimensional inaccuracies. MFA simulates cooling patterns, helping engineers design efficient cooling channels that ensure uniform temperature distribution. By reducing cooling-related defects, MFA not only enhances part quality but also shortens the overall cycle time, improving production efficiency and lowering costs.<ul><li dir="ltr" style="font-weight: bold;">Predicting Shrinkage and Warpage</li></ul>Different materials shrink at different rates during cooling, which can distort the final part dimensions. Mold Flow Analysis predicts these shrinkage patterns and highlights areas where warpage might occur. By accounting for these factors in the design phase, engineers can make necessary adjustments, such as altering the mold dimensions or modifying material choices, to ensure parts meet exact specifications.<ul><li dir="ltr" style="font-weight: bold;">Reducing Cycle Times</li></ul>By analyzing filling, packing, and cooling phases, MFA provides insights into optimizing the entire molding process. For instance, it can identify opportunities to speed up material flow without causing defects or to streamline cooling times by improving channel designs. These optimizations not only increase production speed but also lower manufacturing costs, making the process more efficient.By incorporating Mold Flow Analysis early in the design process, manufacturers can significantly enhance the manufacturability of their parts, ensuring smoother production, higher quality, and fewer delays caused by defects or design inefficiencies. This proactive approach is a key driver of successful injection molding projects.<h2 dir="ltr">RPWORLD: Comprehensive Injection Molding Solutions</h2>At RPWORLD, we go beyond Mold Flow Analysis to offer a full range of injection molding services tailored to your needs:<ul><li dir="ltr">Lead Times as Fast as 7 Days:&nbsp;Delivering production-ready parts quickly to accelerate your product to market.</li><li dir="ltr">100+ Material Options:&nbsp;From engineering-grade plastics to specialized materials such as medical-grade materials, food-grade materials and more, we provide flexibility to meet diverse application requirements.</li><li dir="ltr">In-House Mold Design &amp; Fabrication:&nbsp;Expertly designed molds to match your project specifications, reducing lead times and improving precision.</li><li dir="ltr">Rigorous Quality Assurance:&nbsp;Advanced inspection tools combining with stringent quality controls ensure every part meets exact specifications, from first article inspection to final quality checks.</li><li dir="ltr">Extensive Finishing Options:&nbsp;Choose from surface finishing, painting, texturing, and more to achieve the perfect part appearance and functionality.</li></ul><hr><h3 dir="ltr">Ready to Optimize Your Injection Molding Project?</h3>With over 20 years of experience, RPWORLD delivers customized manufacturing solutions from prototyping and production, helping you achieve consistent high-quality parts within fastest time, launching products to markets soon.<a href="https://www.rpworld.com/en/get-a-quote/">&nbsp;Contact Us</a> for a free consultation and discover how our advanced injection molding services, including Mold Flow Analysis, can bring your designs to life!

Mold Flow Analysis is a simulation technique that predicts the flow of molten material within a mold cavity during injection molding.

Unlocking Quality in Injection Molding: The Role of Mold Flow Analysis

Integrated Photonics, the technology that uses light to power and transmit information, is transforming industries from telecommunications to healthcare. As the demand for high-speed, energy-efficient devices grows, Photonic Integrated Circuits (PICs), also known as photonic chips, are paving the way for a new era in tech. Similar to electronic circuits but with photons instead of electrons, PICs enable the miniaturisation and acceleration of processes across sectors, making them a cornerstone of future innovations.<a href="https://www.fortunebusinessinsights.com/photonic-integrated-circuit-market-107051#:~:text=The%20global%20photonic%20integrated%20circuit,20.8%25%20during%20the%20forecast%20period.">With a global PIC market poised to reach 65 billion by 2030,</a> there’s a pressing need for skilled professionals who are passionate about contributing to this field. Whether you’re a student, a recent graduate, or an experienced engineer looking for a career pivot, opportunities in PICs span across design, research, development, packaging, and manufacturing.<h2 dir="ltr">Understanding Photonic Integrated Circuits (PICs)</h2>Photonic Integrated Circuits combine multiple optical functions—like lasers, detectors, and modulators—onto a single chip. This integration results in smaller, faster, and more efficient devices, opening new possibilities in applications such as high-speed internet, advanced medical diagnostics, and data processing.In this article, we discuss the pathways to a career in integrated photonics, whether you are a bachelor's student or an established engineer. We highlight the work PhotonDelta is doing to help create a robust PIC industry.&nbsp;<a href="https://www.wevolver.com/article/an-introduction-to-integrated-photonics">Read more about PICs here.</a><h1 dir="ltr"><a href="https://www.wevolver.com/article/global-photonics-engineering-contest?utm_source=photon+delta&utm_medium=articles&utm_campaign=photonics"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzMzNzQ0MzQzNzc3LXBob3RvbiBkZWx0YSAtIE5PVyBMSVZFICgxOTIwIHggNTAwIHB4KS5wbmciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 100%px;" class="fr-fic fr-dib"></a></h1><h1 dir="ltr">Why start a career in PICs:</h1>Building a career in PICS represents a chance to make a mark in a relatively new industry. It offers the opportunity to make a significant impact on sustainable and energy-efficient technologies and solutions. This booming sector has tremendous growth potential across various application areas. Furthermore, you'll get to work in an interdisciplinary and innovative environment.<h2 dir="ltr">Finding your path to a career in Integrated Photonics as a student</h2>A recommended route into PICs is through degrees in engineering, optics, or telecommunications. Programs in electrical engineering, applied physics, and optical sciences offer a strong foundation, with many universities now offering specialised courses in photonics. Further,&nbsp;Mechanical Engineering or Materials Engineering backgrounds are strong favoured in the PIC industry because topics like packaging and assembly of the PIC are very important.For engineers who have completed bachelor's degrees, pursuing a master's in a Photonics program is a solid way to establish a degree in photonics.&nbsp;PhotonDelta and Optics Netherlands recently launched the '<a href="https://www.tue.nl/en/education/graduate-school/masterplus-optics-photonics#:~:text=A%20Master%20with%20an%20additional%20Plus%20in%20Optics%20%26%20Photonics&text=The%20MasterPlus%20offers%20an%20extra,driving%20scientific%20and%20societal%20change.">MasterPlus Programme in Optics and Photonics' (MPOP).</a> This initiative, in collaboration with Delft University of Technology (TU Delft), Eindhoven University of Technology (TU/e), and the University of Twente (UT), introduces a specialisation within existing master's programs in Applied Physics and Electrical Engineering. MPOP aims to bridge academic expertise with industry needs, offering students the flexibility to attend courses across different campuses and engage in inter-university activities.&nbsp;Participants will have opportunities for internships with leading companies like ASML, Signify, SMART Photonics, and LioniX International, as well as collaborations with research institutes such as SRON and imec. The program seeks to address the growing demand for skilled professionals in the optics and photonics sector, contributing to the Netherlands' position as a global innovation hub in this field. Learn more about the program <a href="https://www.mpop-programme.com/">here</a>.For those already in the workforce, there are various reskilling programs supported by the industry, allowing professionals from other technical backgrounds to leverage their existing skills while gaining new, PIC-specific knowledge.<h2 dir="ltr">Exploring Career Roles in PICs</h2>Whether coming through traditional education routes or via reskilling programs PIC careers offer diverse roles for innovators and problem-solvers alike:<ul><li dir="ltr">Photonics Engineers&nbsp;design and develop the optical components that power PICs.</li><li dir="ltr">Research Scientists&nbsp;push the boundaries of photonics knowledge through experimentation.</li><li dir="ltr">System Designers&nbsp;integrate PICs into broader applications, such as telecom systems.</li><li dir="ltr">Product Managers and Application Engineers&nbsp;work closely with clients to adapt PIC solutions to specific needs.</li></ul><h1 dir="ltr">PhotonDelta’s Role in Shaping Careers in PICs</h1>PhotonDelta supports a global end-to-end ecosystem of photonic chip companies that designs, develops, and manufactures innovative solutions that contribute to a better world.They promote the global adoption of photonic integrated chips by engaging with a diverse range of industries and the academic community.&nbsp;PhotonDelta's Human Capital Program aims to bridge the gap between current and future talent and the needs of the integrated photonics industry. The program focuses on two main objectives:<ol><li dir="ltr">Increasing the attractiveness of a career in integrated photonics: This involves creating a strong industry brand, raising awareness among students at various educational levels, building a community of photonic chip enthusiasts, and implementing marketing activities to attract both national and international talent.</li><li dir="ltr">Expanding the quality and size of the integrated photonics talent pool: PhotonDelta supports educational institutions and training providers to offer comprehensive study programs and reskilling courses. Initiatives include summer schools, challenge-based learning, specialised master's programs, hackathons, and online training sessions.</li><li dir="ltr">Creating employee engagement and loyalty, contributing to talent retention within the industry</li><li dir="ltr">By creating an environment where professionals can learn, collaborate, and grow, the industry sets the foundation for long-term success and positions itself as a leader in the global photonics sector. PhotonDelta initiates expert communities and industry partner meetings to ensure that employees feel welcome and appreciated, keeping talents in the industry.&nbsp;</li></ol>To achieve these goals, PhotonDelta curates and creates pivotal talent market data to inform strategic initiatives. It serves as a connector within its ecosystem, facilitating collaboration among companies, research institutes, and educational institutions. Through strategic investments, PhotonDelta aims to accelerate scalable initiatives that attract and retain talent in the integrated photonics industry.&nbsp;They endeavour to&nbsp;foster a strong photonics community and promote career development in PICs through several key initiatives:<ul><li dir="ltr">Collaborations with Academic Institutions: PhotonDelta partners with universities and technical schools to develop curricula and summer schools tailored to integrated photonics, ensuring students receive industry-relevant education.</li><li dir="ltr">Career Opportunities through PhotonJobs: The<a href="http://www.photonjobs.nl">&nbsp;PhotonJobs platform</a> connects job seekers with leading photonics companies looking for skilled engineers, researchers, and developers.</li><li dir="ltr">Talent outreach activities: PhotonDelta organizes and participates in several activities to introduce talent with a career in the Photonic Chip Industry such as career events, supporting student teams and study associations and offering an introduction to the world of (integrated) photonics already at a young age. &nbsp; &nbsp;</li></ul><h3 dir="ltr">Building a Sustainable Future in Photonics</h3>PhotonDelta’s long-term goal is to create a sustainable talent pipeline for the photonic chip ecosystem by developing comprehensive programs across all educational levels. Through partnerships with industry leaders and educational institutions, PhotonDelta supports the growth of a skilled workforce ready to meet the demands of the evolving photonics landscape.By joining this exciting field, you can play a role in transforming how the world uses light to shape the future of technology. Whether through PhotonDelta's training programs, industry events, or job platform, there are many pathways to a fulfilling career in photonics. Take the first step today and become part of the photonic chip revolution.<hr><h2 id="isPasted">The Global Photonics Engineering Contest is now live!</h2>The Global Photonics Engineering Contest invites engineers, innovators, and startups to push the boundaries of Photonic Integrated Circuits (PIC) and create applications that address complex engineering challenges and push the boundaries of the industry. Whether you’re in semiconductors, data &amp; telecom, medtech, or agritech, we want to see your breakthrough idea that can harness the power of PICs to change the world.<a href="https://www.wevolver.com/article/global-photonics-engineering-contest?utm_source=photon+delta&utm_medium=articles&utm_campaign=photonics"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzMzNzQ0Mzc5NTIxLTE3MzM3NDQzNzk1MjEucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" class="fr-fic fr-dii"></a><ul><li dir="ltr">Prize: €50,000 worth of services from the PhotonDelta ecosystem to to make your idea market-ready + a chance to receive up to €2 million in pre-seed funding.</li><li dir="ltr">Audience: Open to all engineers, researchers, OEMs, hardware manufacturers, startups/scale-ups, research teams, and students working on innovative PIC applications.</li><li dir="ltr">Early Bird Opportunity:&nbsp;All submissions received by 31st December 2024, will receive personalized feedback from PhotonDelta engineers.</li><li dir="ltr">End date: March 3rd 2025</li></ul><a href="https://www.wevolver.com/article/global-photonics-engineering-contest?utm_source=photon+delta&utm_medium=articles&utm_campaign=photonics" class="button-main-action">ENTER THE CHALLENGE NOW</a>

Explore pathways to a career in Photonic Integrated Circuits (PICs) for both students and engineers looking to reskill. 

Illuminating Your Path: Building a Career in Integrated Photonics

Photonic Integrated Circuits (PICs) are optical microchips that combine multiple optical functions to create an optical circuit. These chips can generate, manipulate, and detect light. Today, PICs are the backbone of high-speed internet as these chips offer high-speed optical connectivity with minimal energy consumption. There has been a growing interest in research and commercial applications for PICs for various markets, including medical technology (MedTech), as they offer compact optical solutions at a low cost when mass-produced.&nbsp;<h2 dir="ltr">Benefits for MedTech</h2>The most significant benefits of PICs for the development and deployment of innovative MedTech systems in healthcare include:<ul><li dir="ltr">High Sensitivity and Reliability: PIC-based biosensors exhibit high sensitivity and reliability. These features enable the deployment in areas such as point-of-care diagnostics, where accurate and rapid results are required for effective patient management.</li><li dir="ltr">Compactness and Miniaturization: PICs enable the development of compact and miniaturized devices, which is particularly advantageous for wearable health devices and lab-on-chip applications. This miniaturization results from the integration of complex optical functions into small form factor chips, which is key for the development of advanced diagnostic and monitoring technologies that are more user-friendly and more accessible.</li><li dir="ltr">Cost-Effectiveness: The integration of photonic components onto a single chip and ability to manufacture them on a large scale, significantly reduces the cost of a chip. Thus, use of PICs makes advanced medical technologies more affordable for widespread use.</li><li dir="ltr">Accelerated Performance: PICs offer improved performance characteristics such as low propagation losses, high power handling, and efficient integration of active components. These features are vital for the development, deployment and operation of high-performance imaging and sensing solutions in healthcare. The latter are enabling more precise diagnostics and better patient outcomes.</li><li dir="ltr">Versatility in Applications: PICs are very versatile and can be used in a wide range of MedTech applications, from biosensing and imaging to environmental monitoring in healthcare-related settings. This adaptability makes PICs capable of addressing diverse medical challenges.</li></ul>Overall, PICs are transforming MedTech based on innovative solutions that enhance the accuracy, efficiency, and accessibility of healthcare technologies. Their ability to integrate multiple functions into single microsystems and devices makes them suitable for a wide range of advanced medical diagnostics and treatment applications.<a href="https://www.wevolver.com/article/global-photonics-engineering-contest?utm_source=photon+delta&utm_medium=articles&utm_campaign=photonics"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1733744077828-photon+delta+-+NOW+LIVE+%281920+x+500+px%29.png" style="width: 100%px;" class="fr-fic fr-dib"></a><h2 dir="ltr">PIC-Enabled Transformative Applications in MedTech</h2>Some of the most prominent MedTech applications that are enabled by PICs include:<h3 dir="ltr">Advanced Diagnostics</h3>PICs are enhancing diagnostic capabilities through technologies like Optical Coherence Tomography (OCT), which is a contactless imaging technique that is used for examining living tissue. OCT is a very useful tool for medical diagnostics thanks to its non-invasive imaging capabilities, which enable high-resolution images of biological tissues. PICs can be used to miniaturize OCT systems, making them more accessible and portable for widespread clinical use. For instance, one of the popular applications of OCT is the detection of tumors or eye disorders. Existing OCT equipment is however costly and bulky in size, which prevents the adoption and use of the technology outside hospitals. PICs can alleviate this limitation based on a significant reduction of the size and cost of the devices. Specifically, PICs enable the development of handheld OCT probes, which are based on lasers and detectors that are directly integrated onto the photonic chip.&nbsp;<h3 dir="ltr">Wearable Health Devices</h3>The miniaturization and integration capabilities of PICs make them ideal for the development and integration of wearable health devices. These devices can continuously monitor vital signs such as heart rate, oxygen levels, and glucose levels with high precision. The compact nature of PICs allows for the development of lightweight and comfortable wearables that provide real-time health monitoring. Commercial companies have demonstrated &nbsp;PIC-enabled systems for spectroscopic breath analysis. It can reveal diseases or metabolic disorders based on the detection of trace gases like methane and nitric oxide. In this direction, laser-based absorption spectroscopy is used to combine high sensitivity and high temporal resolution. The miniaturization and mass production offered by PICs can adopt this spectroscopic technique into handheld devices. In this context, commercial companies are attempting to demonstrate a sensor that can be mounted in a smartwatch to enable the monitoring of a range of different biomarkers.&nbsp;<h3 dir="ltr">Point of Care (PoC) Diagnostics</h3>As already outlined, PIC-enabled biosensors can be very efficient in detecting pathogens based on the measurement of changes in the refractive index of their waveguides. The main advantage of such methods is that the resulting measurements can be very accurate. Moreover, based on such devices it is possible to detect different pathogens using a single chip with only a single sample. As a prominent example, <a href="https://www.phix.com/">PHIX</a> provides the packaging for a PoC diagnostics biosensing platform, which is enabled by a PIC that comprises Vertical-Cavity Surface-Emitting Lasers (VCSELs) and photodiodes. The latter are integrated within a Printed Circuit Board (PCB), along with protective layers. This packaging leads to a cost-effective disposable cartridge that can be integrated within a portable diagnostics device.As another example, <a href="https://bialoom.com/">Bialoom</a> integrates PICs in their proprietary silicon plasmo-photonic biosensor technology, which combines silicon photonics and plasmonics. This technology is used to create biochips embedded in disposable cartridges, designed for rapid and sensitive detection of biomarkers from blood samples. The primary application of Biabloom’s biochips is the diagnosis and treatment of severe infections, which provides a point-of-care diagnostic solution that enables quick and evidence-based medical decisions.<h3 dir="ltr">Optical Sensors in Minimally Invasive Surgeries&nbsp;</h3>Minimally invasive surgeries require precision and real-time feedback. PICs enable the development of sophisticated optical sensors that provide surgeons with critical information during procedures. For example, application-specific photonic integrated circuits (ASPICs) can offer haptic feedback to surgeons, which enhances their ability to perform delicate operations with greater accuracy.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1733744489308-robot+1.jpg" style="width: 100%px;" class="fr-fic fr-dib">The above-listed applications illustrate that the integration of PICs into MedTech goes beyond mere technological improvements. It enables a paradigm shift that is enabled by a combination of hardware, software and process related innovations (e.g., innovations in diagnostic operations). PIC-enabled solutions are more efficient, cost-effective, and scalable, which enables innovative healthcare solution integration.&nbsp;<h2 dir="ltr">Industry Leaders Insights on the Future of PIC-based MedTech&nbsp;</h2>Recent research initiatives have demonstrated the future of integrating PIC microsystems within MedTech devices and applications. For instance, Maarten Buijs, the CEO of <a href="https://www.surfixdx.com/">Surfix Diagnostics</a> envisions that <a href="https://www.phix.com/markets/medical-devices/">future photonic biochips will integrate both light sources and signal detectors to enable the next generation of point of care devices</a>.&nbsp;In the near future, PICs could be widely used to provide rapid laboratory testing results from simple samples (e.g., urine), which will boost timely diagnostics and more effective treatments. Likewise, PICs will reduce the cost of complexity of blood tests, which could transfer laboratory-level diagnostics at the point of care. As a prominent example, <a href="https://siphoxhealth.com/">SiPhox Health</a> has already developed a silicon photonic chip for <a href="https://www.prnewswire.com/news-releases/siphox-health-introduces-worlds-first-lab-quality-home-blood-testing-platform-at-ces-301714545.html">home-based blood testing</a>, which enables an innovative approach to providing high-quality blood test results quickly and efficiently at home. The company’s solution can measure up to fifty biomarkers from home, which enables patients to gain insights on their heart health, check for inflammation, and access information about the functioning of their thyroid, metabolism, and hormones.There are also PICs applications in biological research beyond PoC diagnostics. As a prominent example, <a href="https://www.imec-int.com/en/articles/integrated-photonics-guiding-light-next-gen-medtech-tools">IMEC highlights the use of silicon nitride-based photonic platforms in medical applications</a>. These platforms could get a boost by the integration of III-V light sources into photonic circuits, which will enable medical tools such as fluorescence microscopy used in DNA sequencing and biological research. Relevant research focuses on the use of photonics on chips to create light interference patterns for very high-resolution <a href="https://www.olympus-lifescience.com/en/microscope-resource/primer/lightandcolor/fluoroexcitation/">fluorescent excitation</a>.&nbsp;Overall, recent advances in PICs and their integration in MedTech applications demonstrate the potential of this technology to transform healthcare. Nowadays, almost 27 years after the development of the<a href="https://www.sciencedirect.com/science/article/abs/pii/S0925400597802547">&nbsp;first SiN PIC-based sensing devices</a>, there is a growing number of PIC applications that are already transforming diagnostic tools, enhancing wearable devices, and improving surgical outcomes. Most importantly, the precision, size, cost-effectiveness and non-invasive operations advantage of the technology make it an enabler for a whole range of novel healthcare applications that are hardly possible nowadays. In the years to come, it is highly likely that PICs enable innovative applications beyond what is available today, which will further revolutionize healthcare delivery.<hr><h2>The Global Photonics Engineering Contest is now live!</h2>The Global Photonics Engineering Contest invites engineers, innovators, and startups to push the boundaries of Photonic Integrated Circuits (PIC) and create applications that address complex engineering challenges and push the boundaries of the industry. Whether you’re in semiconductors, data &amp; telecom, medtech, or agritech, we want to see your breakthrough idea that can harness the power of PICs to change the world.<a href="https://www.wevolver.com/article/global-photonics-engineering-contest?utm_source=photon+delta&utm_medium=articles&utm_campaign=photonics"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1733741315174-Untitled+%281920+x+500+px%29+%281%29.png" style="width: 100%px;" class="fr-fic fr-dib"></a><ul><li dir="ltr">Prize: €50,000 worth of services from the PhotonDelta ecosystem to to make your idea market-ready + a chance to receive up to €2 million in pre-seed funding.</li><li dir="ltr">Audience: Open to all engineers, researchers, OEMs, hardware manufacturers, startups/scale-ups, research teams, and students working on innovative PIC applications.</li><li dir="ltr">Early Bird Opportunity:&nbsp;All submissions received by 31st December 2024, will receive personalized feedback from PhotonDelta engineers.</li><li dir="ltr">End date: March 3rd 2025</li></ul><a href="https://www.wevolver.com/article/global-photonics-engineering-contest?utm_source=photon+delta&utm_medium=articles&utm_campaign=photonics" class="button-main-action">ENTER THE CHALLENGE NOW</a><hr>

There has been a growing interest in research and commercial applications for PICs for various markets, including medical technology (MedTech), as they offer compact optical solutions at a low cost when mass-produced. 


Unlocking the Power of Photonic Integrated Circuits in MedTech

The deep neural network models that power today’s most demanding machine-learning applications have grown so large and complex that they are pushing the limits of traditional electronic computing hardware.Photonic hardware, which can perform machine-learning computations with light, offers a faster and more energy-efficient alternative. However, there are some types of neural network computations that a photonic device can’t perform, requiring the use of off-chip electronics or other techniques that hamper speed and efficiency.Building on a decade of research, scientists from MIT and elsewhere have developed a new photonic chip that overcomes these roadblocks. They demonstrated a fully integrated photonic processor that can perform all the key computations of a deep neural network optically on the chip.The optical device was able to complete the key computations for a machine-learning classification task in less than half a nanosecond while achieving more than 92 percent accuracy — performance that is on par with traditional hardware.The chip, composed of interconnected modules that form an optical neural network, is fabricated using commercial foundry processes, which could enable the scaling of the technology and its integration into electronics.In the long run, the photonic processor could lead to faster and more energy-efficient deep learning for computationally demanding applications like lidar, scientific research in astronomy and particle physics, or high-speed telecommunications.“There are a lot of cases where how well the model performs isn’t the only thing that matters, but also how fast you can get an answer. Now that we have an end-to-end system that can run a neural network in optics, at a nanosecond time scale, we can start thinking at a higher level about applications and algorithms,” says Saumil Bandyopadhyay ’17, MEng ’18, PhD ’23, a visiting scientist in the Quantum Photonics and AI Group within the Research Laboratory of Electronics (RLE) and a postdoc at NTT Research, Inc., who is the lead author of a paper on the new chip.Bandyopadhyay is joined on the paper by Alexander Sludds ’18, MEng ’19, PhD ’23; Nicholas Harris PhD ’17; Darius Bunandar PhD ’19; Stefan Krastanov, a former RLE research scientist who is now an assistant professor at the University of Massachusetts at Amherst; Ryan Hamerly, a visiting scientist at RLE and senior scientist at NTT Research; Matthew Streshinsky, a former silicon photonics lead at Nokia who is now co-founder and CEO of Enosemi; Michael Hochberg, president of Periplous, LLC; and Dirk Englund, a professor in the Department of Electrical Engineering and Computer Science, principal investigator of the Quantum Photonics and Artificial Intelligence Group and of RLE, and senior author of the paper. The research&nbsp;<a href="https://www.nature.com/articles/s41566-024-01567-z" target="_blank">appears today in&nbsp;Nature Photonics</a>.<h2>Machine learning with light</h2>Deep neural networks are composed of many interconnected layers of nodes, or neurons, that operate on input data to produce an output. One key operation in a deep neural network involves the use of linear algebra to perform matrix multiplication, which transforms data as it is passed from layer to layer.But in addition to these linear operations, deep neural networks perform nonlinear operations that help the model learn more intricate patterns. Nonlinear operations, like activation functions, give deep neural networks the power to solve complex problems.In 2017, Englund’s group, along with researchers in the lab of Marin Soljačić, the Cecil and Ida Green Professor of Physics,&nbsp;<a href="https://news.mit.edu/2017/new-system-allows-optical-deep-learning-0612" target="_blank">demonstrated an optical neural network on a single photonic chip</a> that could perform matrix multiplication with light.But at the time, the device couldn’t perform nonlinear operations on the chip. Optical data had to be converted into electrical signals and sent to a digital processor to perform nonlinear operations.“Nonlinearity in optics is quite challenging because photons don’t interact with each other very easily. That makes it very power consuming to trigger optical nonlinearities, so it becomes challenging to build a system that can do it in a scalable way,” Bandyopadhyay explains.They overcame that challenge by designing devices called nonlinear optical function units (NOFUs), which combine electronics and optics to implement nonlinear operations on the chip.The researchers built an optical deep neural network on a photonic chip using three layers of devices that perform linear and nonlinear operations.<h2>A fully-integrated network</h2>At the outset, their system encodes the parameters of a deep neural network into light. Then, an array of programmable beamsplitters, which was&nbsp;demonstrated&nbsp;in the 2017 paper, performs matrix multiplication on those inputs.The data then pass to programmable NOFUs, which implement nonlinear functions by siphoning off a small amount of light to photodiodes that convert optical signals to electric current. This process, which eliminates the need for an external amplifier, consumes very little energy.“We stay in the optical domain the whole time, until the end when we want to read out the answer. This enables us to achieve ultra-low latency,” Bandyopadhyay says.Achieving such low latency enabled them to efficiently train a deep neural network on the chip, a process known as in situ&nbsp;training that typically consumes a huge amount of energy in digital hardware.“This is especially useful for systems where you are doing in-domain processing of optical signals, like navigation or telecommunications, but also in systems that you want&nbsp;to learn in real time,” he says.The photonic system achieved more than 96 percent accuracy during training tests and more than 92 percent accuracy during inference, which is comparable to traditional hardware. In addition, the chip performs key computations in less than half a nanosecond. &nbsp; &nbsp;&nbsp;“This work demonstrates that computing — at its essence, the mapping of inputs to outputs — can be compiled onto new architectures of linear and nonlinear physics that enable a fundamentally different scaling law of computation versus effort needed,” says Englund.The entire circuit was fabricated using the same infrastructure and foundry processes that produce CMOS computer chips. This could enable the chip to be manufactured at scale, using tried-and-true techniques that introduce very little error into the fabrication process.Scaling up their device and integrating it with real-world electronics like cameras or telecommunications systems will be a major focus of future work, Bandyopadhyay says. In addition, the researchers want to explore algorithms that can leverage the advantages of optics to train systems faster and with better energy efficiency.

Researchers demonstrated a fully integrated photonic processor that can perform all key computations of a deep neural network optically on the chip, which could enable faster and more energy-efficient deep learning for computationally demanding applications like lidar or high-speed telecommunications. Image: Sampson Wilcox, Research Laboratory of Electronics.

This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time.

Photonic processor could enable ultrafast AI computations with extreme energy efficiency

<a href="https://seas.harvard.edu/person/jia-liu">Jia Liu</a>, Assistant Professor of Bioengineering;&nbsp;<a href="https://seas.harvard.edu/person/na-li">Na Li</a>, Winokur Family Professor of Electrical Engineering and Applied Mathematics; and&nbsp;<a href="https://seas.harvard.edu/person/petros-koumoutsakos">Petros Koumoutsakos</a>, Herbert S. Winokur, Jr. Professor of Computing in Science and Engineering at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), have been awarded an Emerging Frontiers in Research and Innovation (EFRI) grant from the National Science Foundation. The $2 million grant will support research into developing a bio-symbiotic system that mimics the scalability, adaptability, and efficiency of biological neural networks capable of performing long-term, complex computational tasks.The research is a collaboration between SEAS, the Wyss Institute for Biologically Inspired Engineering at Harvard University, and the University of Texas at Austin.The grant is part of the Emerging Frontiers in Research and Innovation (EFRI): Biocomputing through EnGINeering Organoid Intelligence program, which supports research and development of organoid intelligence systems and biocomputing research.At SEAS, Liu’s group brings together researchers from a range of disciplines to develop advanced bioelectronics that integrate nanoelectronics, soft electronics, genetic and genomic engineering, and machine learning to interface with biological systems. Previously, Liu’s group demonstrated the integration of brain organoids with soft and flexible electronics as cyborg organoids for recording and controlling brain organoid development. With the EFRI grant, Liu and his team will test the cyborg brain organoid’s ability to perform computational tasks, with the goal of inspiring new AI algorithms, enhancing data processing efficiency, and advancing machine learning beyond current capabilities.“Current artificial intelligence and machine learning technologies do not match the adaptability and efficiency of biological systems,” said Liu. “Our goal is to develop a seamlessly integrated biological and electronics system for long-term 3D neural network computing, truly merging nature and machine intelligence together.""NSF's investment will lead to biological computing with superior power and efficiency by harnessing the mechanisms behind complex biological behavior for smart systems," said Susan Margulies, NSF Assistant Director for Engineering. "Advances in biocomputing will open new opportunities for artificial intelligence, biotechnology, and more sustainable computing."

Liu and team receive NSF EFRI grant for biocomputing research

Harnessing scalability, adaptability, and efficiency of biological neural networks through flexible brain-computer interfaces

<section id="isPasted">The combination of light-based and electronic components on a single computer chip would have many benefits for our computing needs. Chips would be faster and consume less energy when using light. However, the problem with current silicon-based technologies is that they cannot emit light. TU/e full professor Erik Bakkers has big plans to change that, though. Along with colleagues at TU Munich and the University of Twente, the research team has just been awarded a €14 million ERC Synergy grant. €5 million will be used to back groundbreaking research at TU/e, with Bakkers coordinating the international project.</section><section tabindex="-1">“Light-emitting chips is the holy grail of future computing technologies – the realization of these chips could revolutionize computing technologies.”That’s how&nbsp;<a href="https://www.tue.nl/en/research/researchers/erik-bakkers" target="_blank">Erik Bakkers</a> – Full Professor at the Department of Applied Physics and Science Education – views the promise of light-emitting microchips.<h3>SiGe holds the key</h3>To get to the promised land, though, a fundamental property of the materials used to make our microchips needs to change.“We must change how atoms are arranged relative to each other. In technical terms, we need to move away from the natural cubic structure of many materials to one that is based on the hexagonal arrangement of atoms. If we achieve this, the materials will be able to emit light.”This is the central goal of the project ‘Bright Chips’, a collaboration between Bakkers, Professor Floris Zwanenburg from the University of Twente, and Professor Jonathan Finley from the Technical University of Munich. The project has just received significant funding to the tune of €14 million as part of the ERC Synergy grant initiative.The material of choice for the ‘Bright Chips’ project is silicon germanium (SiGe) – an alloy that has been shown in recent years to have the suitable properties to emit light.<h3>Three’s not a crowd</h3>“We want to understand how and why SiGe emits light, both from a classical and quantum physics perspectives,” says Bakkers. “Our project is a truly multidisciplinary project which combines the worlds of materials science, photonics, and electronics. And each member of the ‘Bright Chips’ project team will focus on one of these three topics.”At TU/e, the Bakkers’ team will fabricate a new material system based on hexagonal SiGe. Thereafter, the team at TU Munich led by Jonathan Finley will characterize the new SiGe material, and using the material, they plan to demonstrate single photon emitters and lasers.Finally, at the University of Twente, Floris Zwanenburg’s group will fabricate and measure the performance of electronic devices that incorporate the SiGe light-emitting components.“A major milestone or goal for Bright Chips is to study optically-connected SiGe-spin qubits. These can be quickly changed, upscaled, and they are expected to be long-lived due to the unique properties of the materials we will work with,” says Bakkers. “We’re excited to get started with the project!”<h3 id="isPasted">Making blood cells</h3>The second project with TU/e involvement to get an ERC Synergy grant is ‘MakingBlood’.&nbsp;<a href="https://www.tue.nl/en/research/researchers/cecilia-sahlgren" target="_blank">Cecilia Sahlgren</a>, a part-time Full Professor at the Department of Biomedical Engineering and mainly based at Åbo Akademi University in Finland, is one of the four principal investigators on the project.“MakingBlood is seeking to make human blood stem cells (also known as hematopoietic stem cells) using human pluripotent stem cells,” says Sahlgren. “At present, blood stem cells are collected from donors.”“However, too often, a suitable stem cell donor cannot be found. We will explore the cellular, molecular, and physical features that are important for the generation of blood stem cells. Lessons learned here will be used to make blood cells in the lab using a dedicated&nbsp;in vitro setup.”TU/e will play two roles in the project. First, researchers will manufacture a high throughput approach to control flow with magnetic cilia, which will be used to build the embryonic niches that are important for the formation of blood stem cells in the human embryo.This will be done in collaboration with&nbsp;<a href="https://research.tue.nl/en/persons/ye-wang" target="_blank" rel="noreferrer">Ye Wang</a>, who is based in the group of Jaap den Toonder at the Department of Mechanical Engineering.Second, researchers will create&nbsp;in vitro models of the embryonic niches where blood stem cells are formed in the embryo. The ‘MakingBlood’ project is coordinated by the Hospital del Mar Research Institute in Barcelona, Spain, by Professor Anna Bigas.The other principal investigators are Gerald de Haan, Research Director of Sanquin, the Dutch Blood Bank, and Dr Cristina Pina, Senior Lecturer in Biomedical Sciences at Brunel University.<hr><h3 id="isPasted">About the ERC Synergy Grant</h3>A group of two to a maximum of four Principal Investigators (PIs) working together and bringing different skills and resources to tackle ambitious research problems. &nbsp;One will be designated as the corresponding PI (cPI).No specific eligibility criteria regarding the academic training&nbsp;are foreseen for ERC Synergy Grants.PI’s must present a competitive track record that is appropriate to their career stage. Proposals are evaluated on the sole criterion of scientific excellence , which takes on the additional meaning of outstanding intrinsic synergetic effect.</section>

Nemea computer chip with light coming out of it. Image: Created by Jason Jung using Midjourney v4

The first project is developing light-based chips to bridge the gap between photonics and electronics. The second project plans to make blood cells using blood stem cells. The two projects are the first ERC Synergy grants for TU/e.

ERC Synergy grants for cutting-edge light-based chips and for blood cells

Fine-tuning large language models adapts pre-trained models to specific tasks or domains using tailored datasets, while Retrieval-Augmented Generation (RAG) combines retrieval systems with generative models to dynamically incorporate external, up-to-date knowledge into outputs.

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In this episode, we explore a groundbreaking neurotechnology that boosts memory without the need for invasive surgery potentially helping people all over the world struggling with spatial memory.

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Thin film deposition applies material layers at the atomic/molecular level, vital for advancing electronics, energy, and aerospace. Key methods like CVD, PVD, and ALD enhance material properties, driving innovations in semiconductors, solar cells, and precision tech.

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