Huang, Ru
Professor
Research Interests: Novel nano-scaled CMOS devices
Office Phone: 86-10-6275 7761
Email: ruhuang@pku.edu.cn
Huang, Ru received the B.S. (highest honors) and M.S. degrees in electronic engineering from Southeast University, Nanjing, China in 1991 and 1994, respectively, and the Ph.D. degree in microelectronics from Peking University, Beijing, China in 1997. She joined the faculty of Peking University in 1997 and currently a professor and the Dean of School of EECS, Peking University. She is an elected academician of Chinese Academy of Science, 2015 and IEEE Fellow, 2016. Her research interests include nano-scaled CMOS devices, ultra-low-power new devices, new device for neuromorphic computing, emerging memory technology and device variability/reliability.
She has authored or coauthored five books, 3 book chapters and more than 250 papers, including more than 70 papers in IEDM (25 IEDM papers from 2007 to 2016), VLSI Technology Symposium, IEEE EDL and IEEE T-ED, and gave more than 35 invited talks at international conferences. She is the holder of more than 200 granted patents (45 U.S. patents) most of which have been co-licensed by SMIC, a top semiconductor company in China. Prof. Huang is an editor of IEEE T-ED and Associate Editor-in-Chief of journal of “Science China: Information China”. She is the Chair of IEEE EDS SRC Region 10 and elected BoG member. She served as the General Chair/Co-Chair of ICSICT 2016/2014/2012 and ISLPED 2013, CSTIC 2018/2017, TPC Co-Chair of ICSICT 2004/2008, TPC members of many other international conferences and symposiums. She is the winner of National Technology Invention Award, National Award of Science and Technology Progress and many other awards.
Prof. Ru Huang has been the leader of many major national projects with substantial funding, as well as a couple of international collaborative projects with Samsung, Intel and Fujitsu. Here list three significant technical contributions of Prof. Ru Huang in terms of multi-gate nanowire transistors, steep-slope devices and random device variability.
Prof. Huang has made comprehensively important contributions to multi-gate Si nanowire FET. She was among the first to report an epi-free demonstration of gate-all-around SNWTs on bulk substratesand, and reported the first experiment of an analog nanowire circuit module for low-power applications. Moreover, she demonstrated experimentally and solved some mysteries of nanowire device physics, including quasi-ballistic transport, unique low-frequency noise behavior, and was the first to show the non-negligible self-heating and model the parasitic capacitance and variations in SNWTs. All these have contributed much better understanding of the nanowire device physics and thus provided helpful design guidelines for SNWTs.
Regarding the steep-slope devices for ultra-low-power applications, to solve the low on current issue of silicon based tunneling FET without degradation of other device properties, she proposed a kind of hybrid-control of Schottky injection and tunneling mechanism and a new multi-finger-gate tunneling FET of dopant-segregated Schottky Barrier source (mFSB-TFET), which experimentally shows ION by about two magnitude orders higher than traditional TFET, steeper SS over 5.5 decades of current and a minimum SS of 29mV/dec, as well as higher ION/IOFF ratio of 108 with the same footprint, which is very promising for future ultra-low power circuits and has been collaborated with SMIC.
Prof. Huang has contributed to the research of random device variability, including both the static and dynamic variations. For example, her group proposed the first full-set compact model for static random variations in FinFET, including FER, GER and WFV, which can be embedded into industry-standard BSIM-CMG model. In addition, her group was among the first to point out the stochastic NBTI effect in transistor aging, and further reported how to accurately predict the end-of-life variability and parametric yield, which is critical to the resilient circuit design for advanced technology nodes. Some of these results have been transferred to SMIC, Hisilion, Cadence and Synopsys.
