Complex Oxides for Brain‐Inspired Computing: A Review

Abstract

The fields of brain‐inspired computing, robotics, and, more broadly, artificial intelligence (AI) seek to implement knowledge gleaned from the natural world into human‐designed electronics and machines. In this review, the opportunities presented by complex oxides, a class of electronic ceramic materials whose properties can be elegantly tuned by doping, electron interactions, and a variety of external stimuli near room temperature, are discussed. The review begins with a discussion of natural intelligence at the elementary level in the nervous system, followed by collective intelligence and learning at the animal colony level mediated by social interactions. An important aspect highlighted is the vast spatial and temporal scales involved in learning and memory. The focus then turns to collective phenomena, such as metal‐to‐insulator transitions (MITs), ferroelectricity, and related examples, to highlight recent demonstrations of artificial neurons, synapses, and circuits and their learning. First‐principles theoretical treatments of the electronic structure, and in situ synchrotron spectroscopy of operating devices are then discussed. The implementation of the experimental characteristics into neural networks and algorithm design is then revewed. Finally, outstanding materials challenges that require a microscopic understanding of the physical mechanisms, which will be essential for advancing the frontiers of neuromorphic computing, are highlighted.

Document Details

Document Type
Pub Defense Publication
Publication Date
Nov 30, 2022
Source ID
10.1002/adma.202203352

Entities

People

  • A. N. M. Nafiul Islam
  • Abhronil Sengupta
  • Alexander A Chubykin
  • Dillon D. Fong
  • Haoming Yu
  • Shriram Ramanathan
  • Subramanian K R S Sankaranarayanan
  • Sukriti Manna
  • Sunbin Deng
  • Tae Joon Park
  • Yifan Yuan

Organizations

  • Argonne National Laboratory
  • Army Research Office
  • National Science Foundation
  • National Science Foundation Directorate for Mathematical & Physical Sciences
  • Office of Basic Energy Sciences
  • Office of Science
  • Pennsylvania State University
  • Purdue University
  • United States Department of Energy
  • University of Illinois Urbana–Champaign

Tags

Readers

  • Distributed Systems and Data Platform Development
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • Autonomy
  • Autonomy - Autonomous System Control
  • Microelectronics
  • Microelectronics - Graphene