Assistant Professor at Zhejiang University, China
Dr. Yishu Zhang is a distinguished researcher and member of the “Hundred Talents of Researcher” program at the School of Integrated Circuits, Zhejiang University. With five years of academic and industrial experience, his work focuses on the development of advanced memristive devices and neuromorphic computing. As Director of the Reliability Department at Zhejiang CMOS Innovation Platform, Dr. Yishu Zhang leads research on high-reliability RRAM devices and CMOS-compatible systems. His contributions bridge materials innovation with circuit design, enhancing computing performance and energy efficiency for next-generation electronic technologies.
Professional Profiles
Education
Dr. Yishu Zhang earned his Ph.D. in Electronic Engineering from the Singapore University of Technology and Design under the supervision of Professor Zhao Rong, maintaining an excellent. Prior to his doctoral studies, he completed his Bachelor of Science in Microelectronics from Jilin University, where he achieved. His academic training provided a strong foundation in semiconductor physics, materials engineering, and microelectronics design, equipping him with interdisciplinary skills essential for cutting-edge research in integrated circuits and emerging memory devices.
Professional Experience
Dr. Yishu Zhang currently serves as an Assistant Professor at the School of Integrated Circuits, Zhejiang University, where he mentors multiple doctoral and master’s students in neuromorphic and in-memory computing. Concurrently, as the Director of the Reliability Department at the Zhejiang CMOS Innovation Platform, he leads R&D in 55nm CMOS-based RRAM and eFlash technologies. His previous research fellowships at the National University of Singapore and A*STAR’s Institute for Infocomm Research advanced his expertise in 2D materials and neuromorphic systems, while earlier academic collaborations at Sungkyunkwan University deepened his understanding of graphene-based devices.
Research Interest
Dr. Yishu Zhang’s research focuses on emerging memristive devices such as RRAM, CBRAM, and 2D ferroelectric systems for neuromorphic and in-memory computing. His work aims to bridge device physics and artificial intelligence hardware design, enabling low-power, high-speed computing architectures. He is particularly interested in developing CMOS-compatible materials and architectures that mimic biological neural systems. His studies explore the intersection of materials science, semiconductor engineering, and cognitive computing, contributing to next-generation memory technologies and energy-efficient AI hardware solutions.
Research Skills
Dr. Yishu Zhang possesses advanced expertise in micro-nanofabrication, semiconductor process development, and electrical characterization of nanoelectronic devices. He is proficient in the design and analysis of memristive circuits, Python-based data processing, and device simulation. His technical capabilities span cleanroom fabrication, RRAM reliability testing, and CMOS process integration. As a multidisciplinary researcher, he effectively combines materials synthesis, electrical engineering, and computational analysis to develop innovative hardware solutions for neuromorphic and intelligent computing applications.
Awards and Honors
Dr. Zhang has received numerous recognitions for his scientific excellence and innovation, including the Best Author Award from the International Journal of Extreme Manufacturing and selection for the Zhejiang Province Thousand Talents Project in 2024. He has also earned accolades such as the Silver Award at the National Postdoctoral Innovation and Entrepreneurship Competition and prizes from the HICOOL Global Entrepreneurship and Hangzhou Maker World Competitions. His awards reflect his outstanding contributions to semiconductor research, technological innovation, and leadership in advancing neuromorphic computing technologies.
Conclusion
Through his academic and professional journey, Dr. Yishu Zhang has emerged as a leading researcher in advanced memory and neuromorphic device technologies. His interdisciplinary approach, integrating material innovation with circuit design and artificial intelligence, positions him at the forefront of electronic and computing research. Dedicated to developing high-performance, energy-efficient systems, he continues to shape the future of semiconductor technology and contribute significantly to the global advancement of intelligent hardware and integrated circuit innovation.
Publications Top Notes
1. Self-Rectifying Memristors with Ultra-High Rectification Ratio for Attack-Resilient Autonomous Driving Systems
2. Two-Dimensional Materials Based 2T2R Synaptic Kernel for Efficient Neuromorphic Computing
3. Implementing Multimodal Hardware Security with Two-Dimensional α-In2Se3 Ferroelectric Transistor
4. Hardware-Implemented DropConnect Function for Energy-Efficient Neuromorphic Computing