Probabilistic computing using Cu0.1Te0.9/HfO2/Pt diffusive memristors

Kyung Seok Woo, Jaehyun Kim, Janguk Han, Woohyun Kim, Yoon Ho Jang and Cheol Seong Hwang ()
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Kyung Seok Woo: Seoul National University
Jaehyun Kim: Seoul National University
Janguk Han: Seoul National University
Woohyun Kim: Seoul National University
Yoon Ho Jang: Seoul National University
Cheol Seong Hwang: Seoul National University

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: Abstract A computing scheme that can solve complex tasks is necessary as the big data field proliferates. Probabilistic computing (p-computing) paves the way to efficiently handle problems based on stochastic units called probabilistic bits (p-bits). This study proposes p-computing based on the threshold switching (TS) behavior of a Cu0.1Te0.9/HfO2/Pt (CTHP) diffusive memristor. The theoretical background of the p-computing resembling the Hopfield network structure is introduced to explain the p-computing system. P-bits are realized by the stochastic TS behavior of CTHP diffusive memristors, and they are connected to form the p-computing network. The memristor-based p-bit is likely to be ‘0’ and ‘1’, of which probability is controlled by an input voltage. The memristor-based p-computing enables all 16 Boolean logic operations in both forward and inverted operations, showing the possibility of expanding its uses for complex operations, such as full adder and factorization.

Date: 2022
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DOI: 10.1038/s41467-022-33455-x

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