Details

Autor(en) / Beteiligte

• Woo, Kyung Seok
• Kim, Jaehyun
• Han, Janguk
• Kim, Woohyun
• Jang, Yoon Ho
• Hwang, Cheol Seong

Titel

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

Ist Teil von

• Nature communications, 2022-09, Vol.13 (1), p.5762-8, Article 5762

Ort / Verlag

London: Nature Publishing Group UK

Erscheinungsjahr

2022

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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 Cu 0.1 Te 0.9 /HfO 2 /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. Designing a computing scheme to solve complex tasks as the big data field proliferates remains a challenge. Here, the authors present a probabilistic bit generation hardware built using the random nature of Cu x Te 1− x /HfO 2 /Pt memristors capable of performing logic gates with invertible mode, showing the expandability to complex logic circuits.