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."

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

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  

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.

Lecture & Discussion & Lab

Paper Survey: 30%;

Attendance: 20%;

Projects: 50%

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 

李永福：yongfu.li@sjtu.edu.cn