Machine learning (ML) has long been considered as a promising technology that can fundamentally transform the existing outdated optical networks into next-generation smart and autonomous entities.

Recently, a research group led by Associate Professor Hongyan FU from Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua Shenzhen International Graduate School (Tsinghua SIGS) has developed a fully automated inverse design method for large-scale multi-task silicon-based photonic devices.

Vibration sensors are widely used in the fields of infrastructure health, vehicle safety, biomedical equipment, intelligent electronic products, and mechanical equipment vibration monitoring. However, new challenges have shown up with the booming development of the Internet of things and sensor networks, especially in scenarios where extensive sensing/monitoring is required yet in-situ power supply is not easily accessible. One appealing alternative to this problem is to develop self-powered sen

From June 30th to July 1st, Tsinghua-Berkeley Shenzhen Institute (hereinafter referred to as TBSI) 5th Retreat Conference was successfully held in Shenzhen University Town

The RISC-V Mentorship Program enables one or more internship-style projects per 12-week session which match mentors and project leaders together with mentees and interns. Mentees are guided through a series of milestones by one or more project mentors through their project work and weekly meetings.Tsinghua-Berkeley RISC-V International Open Source Laboratory, as a Premier Members of the RISC-V International, also officially joined the RISC-V Mentorship Program, and will lead students to complete

Large-scale community volunteering efforts have contributed significantly to the societal governance of Shenzhen

Recently, Associate Professor Hongyan FU from Tsinghua-Berkeley Shenzhen Institute (TBSI) was invited by the international journal Nature to publish

The development in the field of human-computer interface has put higher demands on humanized and

Stacked two-dimensional (2D) materials with a finite angle generate moiré superlattices

Researchers from Tsinghua-Berkeley Shenzhen Institute of TSIGS have realized a non-Markov chain algorithm in a two-dimensional (2D) mineral-based resistive random access memory (RRAM) device