High-volume, rapid fabrication of three-dimensional (3D) nano-architected materials and devices is key to deploying next-generation nanotechnologies to solve real-world challenges. Due to the field-of-view limitations of conventional optics, existing 3D nanofabrication techniques face fundamental challenges in throughput, proximity error, and stitching defects on the path to scaling. At LLNL, we have developed a scalable 3D nanofabrication platform that utilizes a metalens-generated focal spots array to massively parallelize two-photon lithography (TPL). Our current system uses over 100,000 high-contrast metalenses to enable the rapid parallel production of complex nanostructures with nanoscale resolution (up to 110 nm) over a 12-cm^2 write field. By programmatically patterning the focal spot array using a spatial light modulator, we further devise an adaptive parallel printing strategy for precise grayscale linewidth modulation and high-throughput production of semi-periodic and fully aperiodic 3D geometries. In this talk, I will share the details of this platform technology that received the R&D 100 Award in 2025. This work represents a paradigm shift for 3D nanolithography from lab-scale prototyping towards wafer-scale production, empowering TPL to be widely adopted at volume in microelectronics, quantum technology, biomedicine, and other fields. I look forward to engaging with the IEEE community to discover suitable use cases in the microelectronics industry, especially for advanced packaging. Speaker(s): Xiaoxing Xia Agenda: 11:30am: Networking & Pizza Noon-1PM: Seminar Bldg: EAG Laboratories, 810 Kifer Road, Sunnyvale, California, United States, 94086

Next Generation Two-photon 3D Printing Technologies [] Abstract: Utilizing a suite of additive manufacturing technologies for applications in responsive and architected materials, energy storage, carbon recycling, microfluidics, quantum computing, and inertial confinement fusion [] []Speaker: Dr. Xiaoxing Xia Staff Scientist Lawrence Livermore National Lab Xiaoxing Xia is a staff scientist at the Lawrence Livermore National Laboratory, California, USA. He leads projects in developing next generation two-photon 3D printing technologies to achieve higher throughput, finer resolution, and multi-materials printing capability. His team utilizes a suite of additive manufacturing technologies for applications in responsive and architected materials, energy storage, carbon recycling, microfluidics, quantum computing, and inertial confinement fusion. He received his PhD in Materials Science from California Institute of Technology and BA in Physics from University of Chicago. AGENDA: Thursday October 16, 2025 11:30 AM: Networking, Pizza & Drinks Noon -- 1 pm: Seminar Please register on Eventbrite before 9:30 AM on Thursday October 16, 2025 $4 IEEE members $6 non IEEE members (discounts for unemployed and students ) See examplesAdd Co-sponsored by: 636940-Santa Clara Valley Section Chapter,EMB18 Bldg: ==> Use corner entrance: Kifer Road / San Lucar Court ==> Do not enter at main entrance on Kifer Road, EAG Labs, 810 Kifer Road, Sunnyvale, California, California, United States, 95051

AI-Enhanced RF/Mixed-Signal Circuits for Reliable Operations IEEE SSCS Distinguished Lecturer Prof. Vanessa Chen Abstract: AI-driven design and optimization are revolutionizing RF and mixed-signal circuits for operation in extreme environments, including high radiation and wide temperature ranges. This talk explores the use of reinforcement learning (RL) and generative models to improve circuit robustness and adaptability. RL-based self-healing techniques leverage embedded electromagnetic sensors for real-time monitoring and dynamic fault recovery, while generative models accelerate design space exploration, enabling resilient and efficient circuit topologies. The presentation will highlight AI-enhanced designs such as adaptive power amplifiers, PMICs, and multispectral sensors that enhance performance and reliability in harsh environments. Speaker biography: Vanessa Chen earned her Ph.D. in electrical and computer engineering from Carnegie Mellon University in 2013. Before joining Carnegie Mellon University, she was affiliated with The Ohio State University. During her doctoral studies at Carnegie Mellon from 2010 to 2013, she conducted research on algorithm-assisted approaches for improving energy efficiency and ultra-high-speed ADCs with on-chip real-time calibration, and interned at IBM T. J. Watson Research Center in 2012. Prior to academia, she held positions as a circuit designer at Qualcomm in San Diego and Realtek, Hsinchu, Taiwan, focusing on self-healing RF/Mixed-signal circuits. Her research focuses on AI-enhanced circuits and systems, which include intelligent sensory interfaces, RF/mixed-signal hardware security, and ubiquitous sensing and computing systems. Dr. Chen has received the NSF CAREER Award in 2019. She has been involved in various technical program committees, including ISSCC, VLSI, CICC, A-SSCC, and DAC. She also has served as an Associate Editor for several IEEE journals, including TCAS-I, TBioCAS, and OJCAS. Additionally, she has contributed as a Guest Editor for TCAS-II and ACM JETC. She is currently an IEEE SSCS Distinguished Lecturer in 2025/2026. Please register to allow for proper planning. Agenda: 5:30pm: Networking 6:00pm: Talk 7:00pm: Event ends Room: 101/101A EE, Bldg: Packard , 350 JANE STANFORD WAY, STANFORD, California, United States