All‐Electric Nonassociative Learning in Nickel Oxide
Abstract
Habituation and sensitization represent nonassociative learning mechanisms in both non‐neural and neural organisms. They are essential for a range of functions from survival to adaptation in dynamic environments. Design of hardware for neuroinspired computing strives to emulate such features driven by electric bias and can also be incorporated into neural network algorithms. Herein, cellular‐like learning in oxygen‐deficient NiOx devices is demonstrated. Both habituation learning and sensitization response can be achieved in a single device by simply controlling the magnitude of the electric field. Spontaneous memory relaxations and dynamic redistribution of oxygen vacancies under electric bias enable such learning behavior of NiOx under sequential training. These characteristics in simple device arrays are implemented to learn alphabets as well as demonstrate simulated algorithmic use cases in digit recognition. Transition metal oxides with carefully prepared defect concentrations can be highly sensitive to electronic structure perturbations under moderate electrical stimulus and serve as building blocks for next‐generation neuroinspired computing hardware.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 19, 2022
- Source ID
- 10.1002/aisy.202200069
Entities
People
- A. N. M. Nafiul Islam
- Abhronil Sengupta
- Chi Chen
- Fanny Rodolakis
- Hua Zhou
- Jasleen Kaur
- Kaushik Roy
- Neda Alsadat Aghamiri
- Qi Wang
- Richard Tran
- Robert Andrawis
- Sampath Gamage
- Sandip Mondal
- Shriram Ramanathan
- Shyue Ping Ong
- Yohannes Abate
- Zhen Zhang
Organizations
- Air Force Office of Scientific Research
- Argonne National Laboratory
- BioXFEL
- National Science Foundation
- Pennsylvania State University
- Purdue University
- University of California, San Diego
- University of Georgia