Interfacial and Bulk Switching MoS <sub>2</sub> Memristors for an All-2D Reservoir Computing Framework

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.

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Publication Details

Journal
ACS Applied Electronic Materials
Published
2026-06-27
DOI
https://doi.org/10.1021/acsaelm.6c00504
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Kartick Biswas, Sourodeep Roy, Saptarshi Das, Prahalad Kanti Barman et al.
ACS Applied Electronic Materials
Advanced Memory and Neural Computing
article

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

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

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.

ACS Applied Electronic Materials
Pennsylvania State University (US), Indian Institute of Technology Madras (IN), Center for NanoScience (DE), Indian Institute of Science Bangalore (IN)
Indian Institute of Technology Madras, Science and Engineering Research Board
Openalex Percentile: Top 10%
Advanced Memory and Neural Computing
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