Weihui Sang
Papers
1
Total Citations
10
H-Index
1
About
Weihui Sang is a leading researcher at the frontier of neuromorphic computing and intelligent robotics, with a focus on developing novel hardware for brain-inspired learning. Their most-cited work introduces a groundbreaking van der Waals ferroelectric memtransistor that implements reward-modulated spike-timing-dependent plasticity (STDP)—a key mechanism for biological reinforcement learning. This device, already garnering 10 citations since its 2025 publication, enables robotic systems to perform real-time object recognition and adaptive tracking, mimicking how the brain learns from feedback. Sang’s major contribution lies in bridging the gap between synaptic plasticity models and practical hardware, offering a scalable, energy-efficient platform for edge AI. By integrating ferroelectric materials with two-dimensional semiconductors, they have demonstrated a memtransistor that not only emulates neural learning rules but also supports direct reward signals, a critical step toward autonomous robots that can learn from their environment. This work has significant implications for next-generation intelligent systems, from prosthetics to autonomous vehicles, and positions Sang as a rising innovator in the field of neuromorphic engineering.
Research Focus
Key Achievements
Top Papers
- 1