journal article·2026-06-27

Interfacial and Bulk Switching MoS 2 Memristors for an All-2D Reservoir Computing Framework

Asmita S. Thool, Sourodeep Roy, Prahalad Kanti Barman, Kartick Biswas, Pavan Nukala, Abhishek Misra, Saptarshi Das, Bhaswar Chakrabarti

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Abstract

In this study, we report non-filamentary resistance switching with 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. Monolayer (1L)-MoS 2 devices exhibit forming-free and unipolar resistance switching with short-term memory (STM). Bipolar and non-volatile resistance switching with outstanding cycle-to-cycle uniformity is observed for the multilayer (ML) MoS 2 memristive devices. Detailed transport analysis and high-resolution scanning tunneling electron microscopy (HRSTEM) studies correlate the non-volatile switching in ML-MoS 2 with a trap-assisted space charge limited conduction (SCLC) mechanism, leading to a bulk-limited resistance switching behavior. We fabricate a 16 × 16 array of ML-MoS 2 memristors and demonstrate long-term potentiation/depression behavior. The 1L-MoS 2 memristive devices with STM are used to generate four-bit reservoir states. Finally, we simulate a small reservoir computing (RC) framework by using experimental data from 5 reservoir neurons and the 16 × 16 synaptic array. This small RC network achieves 89.56% accuracy in a spoken-digit recognition task, owing to the excellent cycle-to-cycle uniformity in both the reservoir neurons and the synaptic array. Our work demonstrates the possibility of creating neural networks with reliable performance using non-filamentary memristive devices on a single 2D-material platform.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Memory and Neural Computing · Neural Networks and Reservoir Computing · Ferroelectric and Negative Capacitance Devices

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Electrical Engineering

Indian Institute of Technology Madras

Department of Physics

Center for Nano Science and Engineering, Indian Institute of Science

The Pennsylvania State University

Funding

Indian Institute of Technology Madras · Science and Engineering Research Board

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