Organismic materials for beyond von Neumann machines

Hai Tian Zhang, Priyadarshini Panda, Jerome Lin, Yoav Kalcheim, Kai Wang, John W. Freeland, Dillon D. Fong, Shashank Priya, Ivan K. Schuller, Subramanian K.R.S. Sankaranarayanan, Kaushik Roy, Shriram Ramanathan

Research output: Contribution to journalReview articlepeer-review

2 Scopus citations

Abstract

The elementary basis of intelligence in organisms with a central nervous system includes neurons and synapses and their complex interconnections forming neural circuits. In non-neural organisms such as slime mold with gel-like media, viscosity modulation enables adaptation to changing environments. At a larger scale, collective intelligence emerges via social interactions and feedback in animal colonies. Learning and memory are therefore multi-scale features that evolve as a result of constant interactions with the environment. There is growing interest in emulating such features of intelligence in computing machines and autonomous systems. Materials that can respond to their environment in a manner similar to organisms (referred to as "organismic materials") therefore may be of interest as hardware components in artificial intelligence machines. In this brief review, we present a class of semiconductors called correlated oxides as candidates for learning machines. The term "correlated" refers to the fact that electrons in such lattices strongly interact and the ground state is not what is predicted by classical band theory. Such materials can undergo insulator-metal transitions at near ambient conditions under external stimuli such as thermal or electrical fields, strain, and chemical doping. Depending on the mechanism driving the transition, intermediate states can be metastable with different volatilities, and the time scales of phase change can be controlled over many orders of magnitude. The change in electronic properties can be sharp or gradual, leading to digital or analog behavior. These properties enable the realization of artificial neurons and synapses and emulate the associative and non-associative learning characteristics found in various organisms. We examine microscopic properties concerning electronic and structural transitions leading to collective behavior and theoretical treatments of the ground state and dynamical response, showcasing VO2 as a model system. Next, we briefly review algorithms designed from the plasticity demonstrated by phase changing systems. We conclude the brief review with suggestions for future research toward realizing non-von Neumann machines.

Original languageEnglish (US)
Article number011309
JournalApplied Physics Reviews
Volume7
Issue number1
DOIs
StatePublished - Mar 1 2020

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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