Keynote and Plenary sessions
Keynote #1
Mr. Joshua J. Caron
Topic: Integration and Complexity Challenges in Mobile RF on the Path to 6G
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Affiliation:
Senior Director of Engineering, Skyworks Solutions
Bio:​
Josh Caron is Senior Director of Engineering at Skyworks Solutions, where he leads advanced development of RF acoustic filters and front end modules for next generation mobile devices. With more than three decades of experience spanning RF systems, acoustic filtering, power amplifier architectures, and heterogeneous integration, Josh has played a key role in delivering high performance solutions used in billions of wireless devices worldwide.
Earlier in his career, Josh worked on acoustic wave sensors and materials research as part of Dr. John Vetelino’s group at the University of Maine, where he earned his BSEE and MSEE degrees and contributed to early advancements in SAW/BAW based sensing technologies. His industry work has since focused on advancing the state of the art in RF acoustic filters and addressing the increasingly complex challenges of modern cellular front ends, including wideband architectures, carrier aggregation, thermal and linearity scaling, coexistence management, and advanced packaging.
Josh is deeply engaged in bridging device level innovation with system level requirements as the industry moves toward 5G Advanced and 6G. He is the inventor on more than 60 U.S. patents, both issued and pending, and has collaborated closely with global OEMs and technology partners to shape the next generation of mobile RF performance.
Keynote #2
Dr. Kaushik Sengupta
Topic: AI-enabled Chip (RF) Design beyond Human Intuition
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Affiliation:
Professor, Electrical and Computer Engineering, Princeton University
Bio:​
Dr. Sengupta is an IEEE Fellow and currently Professor in the department of Electrical and Computer Engineering at Princeton University. He received a B.Tech/M.Tech (dual degree) in Electronics and Electrical Communication Eng. from the Indian Institute of Technology, Kharagpur, in 2007, an M.S. in Electrical Engineering from Caltech in 2008, and a Ph.D. in Electrical Engineering from Caltech in 2012.His research interests include novel chip-scale architectures for intelligent sensing and communication for a wide range of emerging applications. He received the DARPA Young Faculty Award in 2018, the Bell Labs Prize in 2017, the Young Investigator Program Award from the Office of Naval Research in 2017, the Prime Minister Gold Medal Award from IIT Kharagpur in 2007, the Charles Wilts Prize at Caltech for the best Electrical Engineering Ph.D. thesis in 2013, and the inaugural Young Alumni Achievement Award from IIT Kharagpur in 2018. He served as a Distinguished Lecturer for the IEEE Solid-State Circuits Society from 2019 to 2020 and for the IEEE Microwave Theory and Technology Society from 2021 to 2023. He is a recipient of the 2021 IEEE Microwave Theory and Technology Outstanding Young Engineer Award and the 2022 IEEE Solid-state Circuits New Frontier Award. He received the IEEE Microwave Prize in 2015, several best paper awards including IEEE IMS (2020,2021,2022,2025) , RFIC (2012), and the Best paper of the year award from IEEE Journal of Solid-State Circuits in 2023 for the first deep-learning enabled RFIC design. He has over 20+ patents, and his work led to the start-up on long-distance wireless power transmission called Guru Inc, based in California, in which he has served as an advisor.
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Abstract: Traditionally, chip-scale RF system design has been in the domain of the expert, dominated by thumb rules and trial and error techniques. Designing these ICs, that form the bedrock of the wireless networks, is complex, time-consuming, requires years of expertise, and therefore, can be very expensive. Historically, the design process of RF IC design has relied on intuition based approaches with standard templates that are subsequently optimized, time-consuming parameter sweeps, or ad-hoc population-based metaheuristic optimization methods. There is no reason to believe that this approach is optimal in any sense. This talk will discuss how inverse design with AI-based approaches can open a new design space and allow rapid designs on demand. It will discuss deep-learning based modeling and generative AI approaches, that are transferrable across process design technologies, for inverse design and automated synthesis of mmWave/sub-THz circuits and antennas.
