Si-based self-programming neuromorphic integrated circuits for intelligent morphing wings
Abstract
Unlike artificial intelligent systems based on computers, which must be programmed for specific tasks, the human brain can learn in real-time to create new tactics and adapt to complex, unpredictable environments. Computers embedded in artificial intelligent systems can execute arbitrary inference algorithms capable of outperforming humans at specific tasks. However, without real-time self-programming functionality, they must be preprogrammed by humans and will likely to fail in unpredictable environments beyond their preprogrammed domains. In this work, a Si-based synaptic resistor (synstor) was developed by integrating Al2Ox/TaOy materials to emulate biological synapses. The synstors were characterized, and their operation mechanism based on the charge stored in the oxygen vacancies in the Al2Ox material was simulated and analyzed, to understand the inference, learning, and memory functions of the synstors. A self-programming neuromorphic integrated circuit (SNIC) based on synstors was fabricated to execute inference and learning algorithms concurrently in real-time with an energy efficiency more than six-orders of magnitudes higher than those of standard digital computers. The SNIC dynamically modified its algorithm in a real-time learning process to control a morphing wing, thus successfully improving its lift-to-drag force ratio and recovering the wing from stall in complex aerodynamic environments. The synaptic resistor circuits can potentially circumvent the fundamental limitations of computers, thus providing a platform analogous to neurobiological network with real-time self-programming functionality for artificial intelligent systems.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 22, 2022
- Source ID
- 10.1177/00219983221134929
Entities
People
- Atharva Deo
- Chen Wu
- Christopher M. Shaffer
- Daniel J Inman
- Dawei Gao
- Dhruva Nathan
- Fu-Kuo Chang
- Ich C Tran
- Jian-guo Zheng
- Jungmin Lee
- Kevin Haughn
- Lei He
- Mingjie Xu
- Mingyu Wang
- R. Stanley Williams
- Rahul Shenoy
- Suin Yi
- Tanay Topac
- Xiyuan Chen
- Yong Chen
- Zixuan Rong
Organizations
- Air Force Office of Scientific Research
- National Science Foundation
- Stanford University
- Texas A&M University
- University of California
- University of California, Los Angeles
- University of Michigan