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Add to Calendar2026-02-11 00:00:002026-02-13 00:00:00SEMICON Korea 2026TRANSFORM TOMORROWThe future of the semiconductor industry starts here.Join us at SEMICON Korea 2026 to shape tomorrow together. HoursFebruary 11, 2026 | 10:00-17:00 (Last entry 16:30)February 12, 2026 | 10:00-17:00 (Last entry 16:30)February 13, 2026 | 10:00-16:00 (Last entry 15:30)VenueCOEX (Hall A, B, C, D, E, Grand Ballroom, Platz and ASEM Ballroom)Westin Seoul ParnasGrand InterContinental Seoul ParnasScale550 Exhibitors, 2409 booths (2025: 501 exhibitors, 2301 booths)Seoul South KoreaSEMI.org[email protected]America/Los_Angelespublic
High-temperature materials are critical for applications in harsh-environment circuitry, where devices must operate reliably under extreme thermal, chemical, and mechanical stress. In this Master Class, Dr. Shenqiang (Shen) Ren will present a high-throughput ink materials development strategy enabled by non-equilibrium processing and hybrid additive manufacturing. This approach enables rapid synthesis, combinatorial screening, and direct integration of functional materials onto diverse substrates. The resulting materials exhibit strong electrical performance, robust adhesion, and long-term stability under harsh operating conditions. This high-throughput framework accelerates the discovery and deployment of printable materials for interconnects, heaters, and EMI shielding, providing a versatile pathway toward next-generation printed electronics designed for extreme environments.
ABOUT THE SPEAKER
Shenqiang (Shen) Ren, PhD Dr. Shenqiang Ren is a Professor of Materials Science and Engineering at the University of Maryland, College Park, with research interests in emerging functional and structural materials. He received his Ph.D. in Materials Science and Engineering from the University of Maryland, College Park, and subsequently completed postdoctoral training at the Massachusetts Institute of Technology (MIT).
United States
Shenqiang (Shen) Ren , PhD
Department of Materials Science and Engineering, Professor
Join us for a focused Master Class with Dr. Shenqiang (Shen) Ren, exploring the development of high‑temperature materials for harsh‑environment printed and flexible hybrid electronics (FHE). This session will examine how devices can be engineered to operate reliably under extreme thermal, chemical, and mechanical stress—conditions where conventional materials and processes often fail.
The Master Class will also highlight how this high‑throughput framework accelerates the discovery and deployment of printable materials for interconnects, heaters, and EMI shielding, offering a versatile pathway toward next‑generation printed electronics designed for extreme conditions.
High Throughput Material Development for Extreme Environment Printed Electronics
Flexible Electronics Master Class #30
10:00 am - 12:00 pm
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Add to Calendar2026-08-26 10:00:002026-08-26 12:00:00FEMC#30 High Throughput Material Development for Extreme Environment Printed ElectronicsJoin us for a focused Master Class with Dr. Shenqiang (Shen) Ren, exploring the development of high‑temperature materials for harsh‑environment printed and flexible hybrid electronics (FHE). This session will examine how devices can be engineered to operate reliably under extreme thermal, chemical, and mechanical stress—conditions where conventional materials and processes often fail.The Master Class will also highlight how this high‑throughput framework accelerates the discovery and deployment of printable materials for interconnects, heaters, and EMI shielding, offering a versatile pathway toward next‑generation printed electronics designed for extreme conditions.United StatesSEMI.org[email protected]America/Los_Angelespublic
America/Los_Angeles
REGISTER NOW
This comprehensive course will cover the underlying fundamentals, the interrelationship between materials and processes, and the hardware used in printed electronics. The masterclass will also explore how printed electronics can be applied currently in example applications including practical aspects of the technology, its highlights and challenges and scalability. We will also explore emerging new technologies at the cutting edge of FHE, semiconductor packaging, photonics and quantum materials. Throughout the course there will be opportunities for Q/A and discussions and case studies based on participant's interest.
ABOUT THE SPEAKER
J. Devin MacKenzie, PhD Dr. Devin MacKenzie is the Washington Research Foundation Professor and an Assoc. Prof. of Materials Science and Engineering and Mechanical Engineering at UW. He is also the Technical Director of the Washington Clean Energy Testbeds, a lab that provides open-access to world-class advanced manufacturing and characterization tools for printed optoelectronics, sensors and energy device research and scale-up. Devin also has 25 years of entrepreneurial experience in sustainable materials and manufacturing of semiconductors, optoelectronics and energy devices. Prior to UW he was CEO and co-founder of printed battery company, Imprint Energy (acquired CCL/Avery), Previously, as the CTO of Add-Vision, Inc. (acquired Sumitomo Chemical), Dr. MacKenzie led R&D for roll-to-roll printed flexible OLEDs at Add-Vision with licensing in Europe and Asia. Prior to Add-Vision, he led printed silicon RF device and product engineering at Kovio, Inc. a Si Valley MIT spin-out (acquired Thin Film Electronics). Dr. MacKenzie also co-founded, Plastic Logic, from Cambridge University as a postdoc and subsequently a visiting scientist in Physics at the Cavendish Laboratory. Prior to that he worked at Bell Labs and NASA. Dr. MacKenzie has authored over 240 publications and patents that have been licensed globally and has been cited over 12,900 times in fields ranging from rare earth-doped nitrides to quantum materials. He holds Ph.D, MS, and undergraduate degrees in Materials Science and Engineering from the University of Florida and the Massachusetts Institute of Technology.
United States
J. Devin MacKenzie, PhD
Washington Research Foundation Professor of Clean Energy, Associate Professor
Join us for a comprehensive Master Class with Dr. Devin MacKenzie, as he dives into the fundamentals and real-world applications of printed and flexible hybrid electronics (FHE). This session will explore the critical interrelationship between materials, processes, and hardware used in printed electronics, providing a strong foundation for understanding how these systems are designed and manufactured.
Participants will gain insight into current applications of printed electronics, along with practical considerations such as performance, scalability, and manufacturing challenges. The course will also highlight emerging technologies at the forefront of innovation, including advances in FHE, semiconductor packaging, photonics, and quantum materials
Advancing Printed Electronics Technology: From Macroelectronics to Quantum Devices
Flexible Electronics Master Class #31
10:00 am - 12:00 pm
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Add to Calendar2026-09-30 10:00:002026-09-30 12:00:00FEMC#31 Advancing Printed Electronics Technology: From Macroelectronics to Quantum DevicesJoin us for a comprehensive Master Class with Dr. Devin MacKenzie, as he dives into the fundamentals and real-world applications of printed and flexible hybrid electronics (FHE). This session will explore the critical interrelationship between materials, processes, and hardware used in printed electronics, providing a strong foundation for understanding how these systems are designed and manufactured.Participants will gain insight into current applications of printed electronics, along with practical considerations such as performance, scalability, and manufacturing challenges. The course will also highlight emerging technologies at the forefront of innovation, including advances in FHE, semiconductor packaging, photonics, and quantum materialsUnited StatesSEMI.org[email protected]America/Los_Angelespublic
America/Los_Angeles
REGISTER NOW
Digital Twin is a virtual representation of the structure, context, and behavior of physical systems or a process, with a live link to a physical system serving as a key enabler for predictive and data-driven optimization. In Printed and Flexible Hybrid Electronics (FHE), manufacturing involves multiple interdependent variables—different printing technologies, inks, substrates, and process conditions—each introducing its own complexity. In practice, additional challenges such as equipment drift, batch-to-batch variations, and environmental fluctuations further impact process consistency and yield. Changing a process or transferring it between tools is often difficult, as each setup is highly customized and sensitive to local conditions. To address these challenges, Digital Twin frameworks connect data from design, fabrication, and metrology into continuously learning digital models. They enable early detection of process drifts, virtual experimentation for process development, and data-driven optimization that reduces time, cost, and waste.
This course introduces Digital Twin frameworks for FHE, focusing on Deep Neural Network (DNN)-based predictive models. Participants will learn how to integrate design, fabrication, and metrology data into continuously learning virtual twins that detect process drifts, enable virtual experimentation, and optimize manufacturing. The program covers the full workflow—from image processing and virtual metrology to AI model training, validation, and hyperparameter tuning—using real datasets. A hands-on “Build Your Own Digital Twin” module in Google Colab will provide practical experience in training and refining models for printed electronics applications, equipping attendees with both theoretical insight and applied skills for process optimization and performance prediction.
ABOUT THE SPEAKER
Benyamin Davaji, PhD Benyamin Davaji is an Assistant Professor in the Department of Electrical and Computer Engineering at Northeastern University, Boston, Massachusetts, where his research centers on integrated microsystems for sensing and computation using mechanical waves. His work spans acoustic and ultrasound transducers, biointerfaces, and microcalorimetry, with a strong emphasis on data-guided nanofabrication, advanced semiconductor device manufacturing, and interdisciplinary approaches to microsystem design and manufacturing. He earned his Ph.D. in Electrical and Computer Engineering from Marquette University in 2016 and completed a postdoctoral appointment at Cornell University.
Join us for a Master Class with Benyamin Davaji, PhD, as he introduces Digital Twin frameworks for Printed and Flexible Hybrid Electronics, demonstrating how AI- and DNN-based models integrate design, fabrication, and metrology data along with printing technologies to detect process drift, enable virtual experimentation, and optimize manufacturing performance. Participants gain hands-on experience building continuously learning digital twins to reduce variability, cost, and time to optimization.
Digital Twins for Printed Electronics: How Can AI Learn FHE Printing?
Flexible Electronics Master Class #29
10:00 am - 12:00 pm
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Add to Calendar2026-06-10 10:00:002026-06-10 12:00:00FEMC#29 Digital Twins for Printed Electronics: How Can AI Learn FHE Printing?Join us for a Master Class with Benyamin Davaji, PhD, as he introduces Digital Twin frameworks for Printed and Flexible Hybrid Electronics, demonstrating how AI- and DNN-based models integrate design, fabrication, and metrology data along with printing technologies to detect process drift, enable virtual experimentation, and optimize manufacturing performance. Participants gain hands-on experience building continuously learning digital twins to reduce variability, cost, and time to optimization. United StatesSEMI.org[email protected]America/Los_Angelespublic
America/Los_Angeles
REGISTER NOW
A digital twin is a computer representation of the structure, context, and behavior of physical systems, which are critical components in the optimization of computational systems, accurately representing physical systems and processes. A Digital Twin can be used to reduce or eliminate iterative physical experiments needed for optimization, thereby enhancing yield, saving time, and resources. Semiconductor manufacturing involves numerous complex steps, where accurate control of each step is crucial to achieving the overall manufacturing yield and minimizing variations in device characteristics. The complexity of the manufacturing processes' flows limits flexibility for testing novel approaches. Unit processes can be based on first-principal models (physics-based), data-based models, or hybrid models combining both approaches when possible. Fabrication processes in the cleanroom and on printed electronics tools are often a function of time-varying parameters, including those of the equipment, environment, and materials. The parameters often have co-dependencies across different process steps and tool sets.
This course will cover the necessary material to create DNN-based Digital twins for nanofabrication processes in cleanrooms. The course will include experimental details for data preparation, data processing, training models, and use case demonstrations. Nanofabrication process equipment can inherently have millions of internal variables and can learn from datasets, providing a robust and complementary approach to traditional feedback control and process stabilization methods. The included Digital Twin modes are developed using images (CD-SEMs, optical images), time history data (Optical Emission Spectroscopy), and textual process information (recipes and materials). The course will include: (1) Approaches to preprocess image data and create learning-based models, (2) using DNN-based domain translation for learning to predict the DUV nanolithography and ICP Plasma Etch, (3) virtual metrology methods for quantification of learning outcomes, and (4) developing a new class of process-aware DNN-based digital twins.
ABOUT THE SPEAKERS
Benyamin Davaji, PhD Benyamin Davaji is an Assistant Professor in the Electrical and Computer Engineering Department at Northeastern University. His research focuses on integrated microsystems with an emphasis on sensing and computation using mechanical waves, acoustic/ultrasound transducers, bio-interfaces, and microcalorimetry. He also applies data-guided methods to nanofabrication process development and semiconductor manufacturing. Dr. Davaji earned his PhD in Electrical and Computer Engineering from Marquette University in 2016 and later served as a post-doctoral associate at Cornell University’s School of Electrical and Computer Engineering.
Peter Doerschuk, PhD
Peter Doerschuk, Professor at Cornell University since 2006, previously served on the Purdue faculty in Electrical and Computer Engineering and Biomedical Engineering. He earned B.S., M.S., and Ph.D. degrees in Electrical Engineering from MIT, an M.D. from Harvard Medical School, and completed training at Brigham and Women’s Hospital and a postdoc at MIT. His research applies computational nonlinear stochastic systems to biology and medicine, spanning viral 3-D structure determination using electron microscopy and x-ray scattering, to nonlinear models of ethanol pharmacokinetics that enable sensor processing, pattern recognition, and individualized physiological analysis.
Join us for this engaging Master Class with Benyamin Davaji, PhD, Assistant Professor of Electrical and Computer Engineering at Northeastern University and Peter Doerschuk, Professor of Electrical and Computer Engineering and Biomedical Engineering at Cornell University, as they explore the role of digital twin models in advancing semiconductor manufacturing. The masterclass will highlight how data-guided methods and computational modeling are transforming unit process development, driving efficiency and innovation across the semiconductor industry. With expertise spanning microsystems, acoustic transducers, and nanofabrication, the speakers will provide insights into how digital twins can bridge research and production to accelerate breakthroughs in semiconductor technology.
Digital Twin Models for Semiconductor Manufacturing Unit Process
Flexible Electronics Master Class #27
10:00 am - 12:00 pm
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Add to Calendar2025-11-05 10:00:002025-11-05 12:00:00FEMC#27 Digital Twin Models for Semiconductor Manufacturing Unit ProcessJoin us for this engaging Master Class with Benyamin Davaji, PhD, Assistant Professor of Electrical and Computer Engineering at Northeastern University and Peter Doerschuk, Professor of Electrical and Computer Engineering and Biomedical Engineering at Cornell University, as they explore the role of digital twin models in advancing semiconductor manufacturing. The masterclass will highlight how data-guided methods and computational modeling are transforming unit process development, driving efficiency and innovation across the semiconductor industry. With expertise spanning microsystems, acoustic transducers, and nanofabrication, the speakers will provide insights into how digital twins can bridge research and production to accelerate breakthroughs in semiconductor technology.United StatesSEMI.org[email protected]America/Los_Angelespublic
America/Los_Angeles
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Improved materials are required for progress in fields including printed electronics, semiconductors, aerospace & defense, energy, and life sciences/medical care. Developing such materials and chemicals involves considerations of performance, cost, manufacturability, safety, and stability. Scaling production of new materials is a very different challenge than making 50 grams in a laboratory. Transitioning/commercializing new materials is a separate effort where intellectual property, regulation, quality control, system documentation, supply chain development, and business relationships become key. This talk will address these aspects of introducing a new material into our enterprise.
ABOUT THE SPEAKER:
Larry Takiff, PhD
Larry Takiff is co-Founder and President of Akita Innovations LLC, a small business in Massachusetts that develops and produces novel chemicals and materials for government and commercial customers. He founded Akita in 2012 with Lawrence Hancock after working at Polaroid Corporation, Aprilis Inc., and Nomadics/ICx/FLIR Systems. He is a chemist by training and a businessman by necessity. At Akita, he has initiated and led projects in materials R&D and pilot production for a variety of government agencies/DoD components and for commercial partners. Currently he is leading an effort to scale production of a decontamination fluid for the Defense Threat Reduction Agency (DTRA), and a FlexTech project to advance the TRL/MRL of a dielectric ink for printed electronics, as well as several Phase II SBIR development projects.
Join us for this insightful Master Class with Larry Takiff, PhD, Co-Founder and CEO of Akita Innovations LLC, as he explores the path from lab-scale breakthroughs to scalable material solutions across high-impact industries. Dr. Takiff will dive into the challenges and strategies of transitioning novel materials such as those used in printed electronics, semiconductors, aerospace, and medical devices into commercial success.
Development, Scaling, and Transition of Novel Chemicals & Materials
Flexible Electronics Master Class #28
10:00 am - 11:30 am
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Add to Calendar2025-11-12 10:00:002025-11-12 11:30:00FEMC#28 Development, Scaling, and Transition of Novel Chemicals & MaterialsJoin us for this insightful Master Class with Larry Takiff, PhD, Co-Founder and CEO of Akita Innovations LLC, as he explores the path from lab-scale breakthroughs to scalable material solutions across high-impact industries. Dr. Takiff will dive into the challenges and strategies of transitioning novel materials such as those used in printed electronics, semiconductors, aerospace, and medical devices into commercial success.United StatesSEMI.org[email protected]America/Los_AngelespublicWatch on-demand
