Course Overview

Course Title: Emerging Semiconductor Devices and Their Sustainable Innovations

Relevant SDGs: No Poverty, Quality Education, Gender Equality, Reduced Inequalities, Sustainable Cities and Communities

Credit(s): 32/2

Course Description:

This course is designed for both local and international graduate students, as well as senior undergraduate students. It is conducted entirely in English, and participants are expected to have a certain foundation in microelectronics and basic English communication skills.

With the continuous miniaturization of silicon-based CMOS device process dimensions, integrated circuits have experienced rapid development in the era of "Moore's Law," driving progress in the entire information society. However, as the physical limits of devices approach and Moore's Law becomes challenging to sustain, the performance of devices not only results in excessive global energy consumption in integrated circuits but also creates technological barriers that concentrate the IC dividend in a few countries. With the development of technologies such as AI and emerging semiconductor devices, barriers in various links of the IC industry chain are expected to significantly decrease, benefiting a larger global area.

This course focuses on emerging integrated circuit technologies in the "post-Moore era," introducing the working principles, modeling methods, and circuit design methods of several emerging semiconductor devices. It also incorporates artificial intelligence to expedite the modeling and design cycle and reduce inequality between regions. The course will be delivered through a combination of lectures and practical exercises. Lectures will cover the theoretical foundations, methods, and algorithms related to data-driven compact modeling artificial intelligence technology. Practical exercises will involve applying these technologies to real transistor modeling and circuit design problems, guiding students to use datasets and software tools provided by the instructor.

The objective of this course is to provide students with necessary knowledge and skills, broaden their technical perspectives, and cultivate critical thinking and problem-solving abilities in the "post-Moore era."

Academic Team

PI:

• 李永福/Yongfu Li, Associate Professor, SJTU School, yongfu.li@sjtu.edu.cn
• 孙亚男/Yanan Sun, Associate Professor, SJTU School, sunyanan@sjtu.edu.cn
• 赵健/Jian Zhao, Associate Professor, SJTU School, zhaojianycc@sjtu.edu.cn
• 邵雷来/Leilai Shao, assistant professor, SJTU School, leilaishao@sjtu.edu.cn

Collaborators:

1. Noor Ain Kamsani, Associate Professor, Universiti Putra Malaysia (Malaysia), nkamsani@upm.edu.my

2. Fakhrul Zaman Rokhani, Associate Professor, Universiti Putra Malaysia (Malaysia), fzr@upm.edu.my

3. Yehea Ismail, Professor, American University in Cairo (Egypt), y.ismail@aucegypt.edu  

What skills will students get?

1. Develop and apply data-driven models for accurate and efficient characterization and simulation of these transistors.

2. Critical thinking and problem-solving abilities in the context of advanced transistor modeling.

Mode of Teaching

Lecture & Discussion & Lab

Grading

Paper Survey: 30%;

Attendance: 20%;

Projects: 50%

Course-specific Restrictions

Major Requirement: Electronics Engineering, Computer Science, Applied Physics or Other Related Majors

Prerequisites: at least one of the below courses is studied: “Principles of Circuits”, “Digital Integrated Circuits Design”, “Semiconductor Physics”

Year of Study: 3rd year or above for undergraduate students, and postgraduate student 

Class Schedule

Instructors

Yongfu Li

Yongfu Li received the B.Eng. and Ph.D. degrees from the Department of Electrical and Computing Engineering, National University of Singapore (NUS), Singapore.

He is currently an Associate Professor with the Department of Micro and Nano Electronics Engineering and MoE Key Lab of Artificial Intelligence, Shanghai Jiao Tong University, China. He was a research engineer with NUS, from 2013 to 2014. He was a senior engineer (2014-2016), principal engineer (2016-2018) and member of technical staff (2018-2019) with GLOBALFOUNDRIES, as a Design-to-Manufacturing (DFM) Computer-Aided Design (CAD) research and development engineer. His research interests include analog/mixed signal circuits, data converters, power converters, biomedical signal processing with deep learning technique and DFM circuit automation.

Yanan Sun

Yanan Sun is an associate professor and doctoral supervisor of the Department of Micro and Nano Electronics, Shanghai Jiao Tong University. She received her bachelor's degree in Microelectronics from Shanghai Jiao Tong University in 2009 and her doctorate degree in electronic and computer engineering from Hong Kong University of Science and Technology in 2015. In the same year, she joined Shanghai Jiao Tong University. Dr. Sun is responsible for digital integrated circuit design and cutting-edge technology and other related courses, committed to industry-university-research collaborative education, presided over the Ministry of Education industry-university-collaborative education project, school-level "double first-class" quality course construction project. She has received the first-class award for undergraduate course in the national level, Shanghai level, and university level.

Jian Zhao

Jian Zhao received his Bachelor and Doctor degree in Mechanical Engineering from Nanjing University of Science and Technology in 2011 and 2017, respectively. During his doctoral studies, he conducted visiting research at the VLSI Laboratory of the Department of Electrical and Computer Engineering, National University of Singapore from 2012 to 2015. He has done his postdoctoral research at the Institute of Circuits and Systems, Department of Electronic Engineering, Tsinghua University from 2017 to 2018. From 2019 onwards, he joined Department of Micro and Nano Electronics of Shanghai Jiao Tong University as an tenure-track assistant professor, and was promoted to tenure-track associate Professor in 2022.

 

He is engaged in the research of analog and mixed-signal integrated circuits for wireless body area networks, biomedical sensing, MEMS and other applications. He presided over a project of the National Natural Science Foundation of China and a postdoctoral Fund of China, and was selected into the "Morning Light Program" of Shanghai Education Commission in 2019. Till date, he has published more than 30 SCI papers and conference papers, including JSSC, ISSCC and VLSI. He is/was an associate editor in IEEE TCAS-I and TBioCAS journals and served as a reviewer in JSSC, TCAS-I, TCAS-II, and TBioCAS.

Leilai Shao

Leilai Shao is an assistant professor and doctoral supervisor of Shanghai Jiao Tong University. From 2011 to 2015, he obtained a bachelor's degree in Electronic Information Engineering from Zhejiang University (Zhu Kezhen College). From 2015 to 2018, he obtained a master’s degree in ECE from University of California, Santa Barbara. From 2018 to 2020, he obtained the Ph.D. degree in ECE from the University of California, Santa Barbara, his research supervisor is Tim Cheng. From 2020 to 2021, he joined Facebook as a data scientist. Since September 2021, he joined Shanghai Jiao Tong University. During his doctoral period from 2015 to 2020, he worked closely with HP Labs, Facebook, Tencent and other industrial laboratories, and engaged in the algorithm research of large-scale integrated circuit design (VLSI), flexible electronics, circuit design automation (EDA) and machine learning of new semiconductor electronics for a long time. He has published more than 20 articles in top journals and conferences in related fields, including multiple Invited Papers, one work (including a joint work) 8, Including (Nature Communications, IEEE/ACM Design&Test, IEEE/ACM DAC, IEEE DATE, IEEE ASP-DAC). He has also received Best Paper Award Nominations from IEEE DATE 2018, the top conference in the EDA field.

Course Contact

李永福：yongfu.li@sjtu.edu.cn