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, Saptarshi Das
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. This study demonstrates how point defects in 2D semiconductors can be harnessed for neuromorphic computing. By using random telegraph noise in WSe2 field-effect transistors, the researchers improve inference accuracy of noise-inflicted medical images.