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

arXiv: https://arxiv.org/abs/2511.16557 · HTML: https://mlsanity.com/paper/2511.16557/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 36 references that are in the map.
- First posted on arXiv 20 Nov 2025.

## Builds on (most cited first, 3 of 3)

- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [Next Generation Reservoir Computing](https://mlsanity.com/paper/2106.07688/) (2021)
- [You Are What You Eat -- AI Alignment Requires Understanding How Data Shapes Structure and Generalisation](https://mlsanity.com/paper/2502.05475/) (2025)

## 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.