A stochastic encoder using point defect in two-dimensional materials

Abstract Defects pose a significant challenge to the reliability of electronic devices, particularly when dealing with scaled dimensions in the silicon microelectronic industry. Consequently, extensive efforts have been made to eliminate these defects from devices through the optimization of growth and fabrication processes. However, in the realm of emerging nanomaterials, such as two-dimensional semiconductors, a different scenario unfolds, where defects are prevalent. It is crucial to comprehend how these defects can impact device reliability. In this study, we employ a multifaceted approach, encompassing atomistic imaging, density functional theory calculations, device modeling, and low-temperature transport experiments, to unveil the implications of defects on device reliability. Specifically, we investigate their influence on random telegraph signals. On a parallel track, it is worth noting that defects have proven to be beneficial in numerous quantum and energy-harvesting applications. Surprisingly, their potential for computational purposes remains largely untapped. In our study, we harness defects in aggressively scaled 2D transistors to accelerate an inference engine based on a stochastic spiking neural network, offering remarkable noise resilience. In conclusion, our investigation underscores the critical importance of comprehending and leveraging intrinsic point defects in 2D materials, both as challenges to reliability and as opportunities for neuromorphic computing.