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Interfacial and bulk switching MoS2 memristors for an all-2D reservoir computing framework

arXiv 2511.16557 ↗First posted 20 Nov 2025cs.ET cs.AIcited by 0

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Abstract (arXiv)

In this study, we design a reservoir computing (RC) network by exploiting short- and long-term memory dynamics in Au/Ti/MoS2/Au memristive devices. The temporal dynamics is engineered by controlling the thickness of the Chemical Vapor Deposited (CVD) MoS2 films. Devices with a monolayer (1L)-MoS2 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) MoS2 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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 36 references that are in the map. First posted on arXiv 20 Nov 2025.

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3 of 36 references.

References · 3

  1. 2021Carbon Emissions and Large Neural Network Trainingcited by 561
  2. 2021Next Generation Reservoir Computingcited by 160
  3. 2025You Are What You Eat -- AI Alignment Requires Understanding How Data Shapes Structure and Generalisationcited by 15

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