Prof. Xiaoteng Zhao | Wireline Communication | Excellence in Research Award

Prof. Xiaoteng Zhao | Wireline Communication | Excellence in Research Award

Professor | Xidian University | China

Professor Xiaoteng Zhao of Xidian University is a leading expert in high-speed data interfaces and chiplet interconnects, recognized for advancing integrated circuit design in next-generation wireline communication systems. He holds advanced degrees in microelectronics and integrated circuit engineering with specialization in high-speed mixed-signal architectures, and his professional experience includes serving as principal investigator for major national research initiatives, directing innovations in reconfigurable chiplet interconnects, multi-level high-speed receivers, and energy-efficient communication circuits. He has led teams in developing record-breaking transceiver designs and contributed extensively to the field through influential publications in premier IEEE venues such as ISSCC, JSSC, CICC, and RFIC. His research spans CDR systems, PLL architectures, frequency dividers, multi-mode EOMs, analog front-end techniques, and wireline link optimization, supported by more than twenty patents and impactful collaborative projects. His achievements have earned multiple honors, including best paper recognition and national-level commendation for semiconductor research advancements. In addition to his technical contributions, he has served on editorial boards, participated in IEEE standardization efforts, taken on TPC roles, and maintained active membership in professional engineering societies, reinforcing his leadership and service to the microelectronics community. Quote 257, h-index 9, i10-index 8.

Profile: Google Scholar

Featured Publications

X. Zhao, Y. Dong, Y. Zhang, H. Chang, Y. Qi, Z. Yang, C. Han, H. Liang, Y. Yu, A Full-Rate 8.2-to-15.1-Gb/s Reference-Less CDR using Low-Cost SAR-Based Frequency Acquisition Technique Achieving 265 ns Acquisition Time. Microelectronics Journal, 106944, 2025.

Z. Yang, X. Zhao, H. Sun, X. Su, Z. Dong, Y. Dong, Y. Yu, H. Liang, S. Liu, A 56 Gb/s PAM4 slope-sampling CDR with simultaneous four-output phase interpolator. Microelectronics Journal, 106870, 2025.

J. Liu, X. Su, Z. Yang, Z. Dong, C. Han, X. Zhao, S. Liu, A 7-bit 8 GHz Phase Interpolator With Eight-Phase Output Using a Linear Weighting Scheme Using Only 50% Interpolation Units. Microelectronics Journal, 106874, 2025.

M. Zhang, R. Li, X. Zhao, X. Su, Z. Dong, Z. Yang, H. Su, H. Liang, Y. Yu, S. Liu, Load-Driven Inductive Peaking Design for Broad Band Continuous-Time Linear Equalizer. Microelectronics Journal, 106873, 2025.

Z. Dong, X. Zhao, Z. Yang, X. Su, H. Han, F. Bu, D. Sun, S. Liu, Z. Zhu, A 0.0006-mm² 0.13-pJ/bit 9–21-Gb/s Sub-Sampling CDR with Inverter-Based Frequency Multiplier and Embedded 1:3 DEMUX in 65-nm CMOS. IEEE J. Solid-State Circuits, 2025.

Mr. Jufeng Han | Materials Informatics | Best Researcher Award

Mr. Jufeng Han | Materials Informatics | Best Researcher Award

Master | Institute of Semiconductors | China

Dr. Jufeng Han, currently pursuing a Master’s degree in Artificial Intelligence for Science at the Institute of Semiconductors, Chinese Academy of Sciences, is an emerging researcher specializing in materials informatics and semiconductor and optoelectronic materials. His academic foundation combines advanced studies in artificial intelligence with applications in materials science, focusing on the integration of data-driven modeling with physical principles. Professionally, he has contributed to innovative research projects, most notably the development of a symbolic–neural hybrid modeling framework for perovskite bandgap prediction—an approach that enhances accuracy and interpretability in photovoltaic material screening. His work has been recognized with a Best Paper Candidate nomination and publication acceptance in Materials Today Energy. Beyond research, he collaborates within interdisciplinary teams at the Institute of Semiconductors, demonstrating leadership in bridging AI methodologies with materials discovery. His research contributions have strengthened the role of AI in accelerating semiconductor innovation, particularly in energy-efficient and sustainable technologies. He is a member of the Association for the Advancement of Artificial Intelligence (AAAI) and maintains a strong academic presence through his Google Scholar profile. Jufeng Han’s combination of technical expertise, academic excellence, and forward-looking research vision positions him as a promising scholar in AI-driven materials science and a deserving nominee for the Best Researcher Award.

Profile: Scopus

Featured Publications

Han, Jufeng*, Bandgap prediction for perovskite materials based on symbolic–neural hybrid modeling. Materials Today Energy, Accepted.

Han, Jufeng*, Symbolic–neural hybrid framework for enhanced interpretability and accuracy in perovskite bandgap prediction. Institute of Semiconductors, Chinese Academy of Sciences, In production.

Han, Jufeng, AI-driven modeling approaches for semiconductor and optoelectronic material discovery. AI for Science Research Series, Under review.