Probabilistic computing using Cu<sub>0.1</sub>Te<sub>0.9</sub>/HfO<sub>2</sub>/Pt diffusive memristors.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 36180426.
- Also identified by DOI 10.1038/s41467-022-33455-x and PMC identifier 9525628.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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 Cu<sub>0.1</sub>Te<sub>0.9</sub>/HfO<sub>2</sub>/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.