Defect-engineered monolayer MoS2 with enhanced memristive and synaptic functionality for neuromorphic computing

Rajput, Manisha; Mallik, Sameer Kumar; Chatterjee, Sagnik; Shukla, Ashutosh; Hwang, Sooyeon; Sahoo, Satyaprakash; Kumar, G. V. Pavan; Rahman, Atikur

doi:10.1038/s43246-024-00632-y

Title: Defect-engineered monolayer MoS₂ with enhanced memristive and synaptic functionality for neuromorphic computing

Journal Article · 16 September 2024 · Communications Materials

DOI: https://doi.org/10.1038/s43246-024-00632-y · OSTI ID:2448247

Rajput, Manisha ^[1];

^[2]; Chatterjee, Sagnik ^[1]; Shukla, Ashutosh ^[1];

^[3]; Sahoo, Satyaprakash ^[2]; Kumar, G. V. Pavan ^[1];

^[1]

Indian Institute of Science Education and Research, Pune (India)
Institute of Physics, Bhubaneswar (India). Laboratory for Low Dimensional Materials; Homi Bhabha National Institute, Mumbai (India)
Brookhaven National Laboratory (BNL), Upton, NY (United States). Center for Functional Nanomaterials (CFN)

Two-dimensional transition metal dichalcogenides (TMDs)-based memristors are promising candidates for realizing artificial synapses in next-generation computing. However, practical implementation faces several challenges, such as high non-linearity and asymmetry in synaptic weight updates, limited dynamic range, and cycle-to-cycle variability. Here, utilizing optimal-power argon plasma treatment, we significantly enhance the performance matrix of memristors fabricated from monolayer MoS₂. Our approach not only improves linearity and symmetry in synaptic weight updates but also increases the number of available synaptic weight updates and enhances Spike-Time Dependent Plasticity. Notably, it broadens the switching ratio by two orders, minimizes cycle-to-cycle variability, reduces non-linear factors, and achieves an energy consumption of ~30 fJ per synaptic event. Implementation of these enhancements is demonstrated through Artificial Neural Network simulations, yielding a learning accuracy of ~97% on the MNIST hand-written digits dataset. Our findings underscore the significance of defect engineering as a powerful tool in advancing the synaptic functionality of memristors.

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: SC0012704

OSTI ID:: 2448247

Report Number(s):: BNL--226195-2024-JAAM

Journal Information:: Communications Materials, Journal Name: Communications Materials Journal Issue: 1 Vol. 5; ISSN 2662-4443

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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