Interfacial and bulk switching MoS2 memristors for an all-2D reservoir computing framework
This work addresses the problem of efficient neuromorphic computing for edge AI applications, though it is incremental as it builds on existing memristor and reservoir computing concepts.
The study tackled the challenge of implementing reservoir computing by designing a network using MoS2 memristors with engineered short- and long-term memory dynamics, achieving 89.56% precision in a spoken-digit recognition task and analyzing a nonlinear time series equation.
In this study, we design a reservoir computing (RC) network by exploiting short- and long-term memory dynamics in Au/Ti/MoS$_2$/Au memristive devices. The temporal dynamics is engineered by controlling the thickness of the Chemical Vapor Deposited (CVD) MoS$_2$ films. Devices with a monolayer (1L)-MoS$_2$ film exhibit volatile (short-term memory) switching dynamics. We also report non-volatile resistance switching with excellent uniformity and analog behavior in conductance tuning for the multilayer (ML) MoS$_2$ memristive devices. We correlate this performance with trap-assisted space-charge limited conduction (SCLC) mechanism, leading to a bulk-limited resistance switching behavior. Four-bit reservoir states are generated using volatile memristors. The readout layer is implemented with an array of nonvolatile synapses. This small RC network achieves 89.56\% precision in a spoken-digit recognition task and is also used to analyze a nonlinear time series equation.