A stochastic encoder using point defects in two-dimensional materials

Harikrishnan Ravichandran, Theresia Knobloch, Shiva Subbulakshmi Radhakrishnan, Christoph Wilhelmer, Sergei P. Stepanoff, Bernhard Stampfer, Subir Ghosh, Aaryan Oberoi, Dominic Waldhoer, Chen Chen, Joan M. Redwing, Douglas E. Wolfe, Tibor Grasser and Saptarshi Das ()
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Harikrishnan Ravichandran: Penn State University
Theresia Knobloch: Institute for Microelectronics (TU Wien)
Shiva Subbulakshmi Radhakrishnan: Penn State University
Christoph Wilhelmer: Institute for Microelectronics (TU Wien)
Sergei P. Stepanoff: Penn State University
Bernhard Stampfer: Institute for Microelectronics (TU Wien)
Subir Ghosh: Penn State University
Aaryan Oberoi: Penn State University
Dominic Waldhoer: Institute for Microelectronics (TU Wien)
Chen Chen: Penn State University
Joan M. Redwing: Penn State University
Douglas E. Wolfe: Penn State University
Tibor Grasser: Institute for Microelectronics (TU Wien)
Saptarshi Das: Penn State University

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract While defects are undesirable for the reliability of electronic devices, particularly in scaled microelectronics, they have proven beneficial in numerous quantum and energy-harvesting applications. However, their potential for new computational paradigms, such as neuromorphic and brain-inspired computing, remains largely untapped. In this study, we harness defects in aggressively scaled field-effect transistors based on two-dimensional semiconductors to accelerate a stochastic inference engine that offers remarkable noise resilience. We use atomistic imaging, density functional theory calculations, device modeling, and low-temperature transport experiments to offer comprehensive insight into point defects in WSe2 FETs and their impact on random telegraph noise. We then use random telegraph noise to construct a stochastic encoder and demonstrate enhanced inference accuracy for noise-inflicted medical-MNIST images compared to a deterministic encoder, utilizing a pre-trained spiking neural network. Our investigation underscores the importance of leveraging intrinsic point defects in 2D materials as opportunities for neuromorphic computing.

Date: 2024
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DOI: 10.1038/s41467-024-54283-1

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