FET: Small: 2D Material Compound Synapse Arrays for Robust In-Memory and Neuromorphic Computing (2DNeuro)

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Novel computing systems that emulate the brain have received significant attention from the scientific and engineering community, as they can help analyze large amounts of information while consuming only small amounts of energy. Significant advances have been made on these neuro-inspired systems, but there are still limitations that prevent their practical application on portable devices. This work will investigate novel materials and designs that can alleviate these limitations, enabling a better execution of critical mathematical functions. The scientific breakthroughs resulting from this work will support robust, efficient, and massively parallel computing for data-intensive machine learning applications. A profound societal impact will be achieved by laying the foundations for novel computing hardware that will support cognitive systems and artificial intelligence of tomorrow. Another objective of this project is to integrate research and education by training students (including graduate and undergraduate researchers), by developing educational tools in form of web-based interactive platforms, and by supporting a graduate-level course in the topic of novel integrated memory and neuro-inspired computing architectures.This project will establish the foundations of new storage technology based on two-dimensional (2-D) atomically-thin materials, extending the practical engineering limits of neuro-inspired systems. These storage devices, known as memristors, can be programmed to different states, based on an externally applied stimulus. When organized in specific configurations, memristor arrays can be used to implement complex functions that are used by the system in a decision-making process. However, because of randomness in the operation of memristors, variation is inevitable, affecting the accuracy of the computation and the trustworthiness of the system-based decisions. This work aims to combine novel 2-D materials with new array configurations to mitigate the impact of variability in order to improve accuracy and performance of computation, while maintaining the benefits of low-energy consumption. The intellectual significance of this project extends from fundamental science relating to atomic-scale storage in 2-D materials, to applied engineering contributions relating to the demonstration of robust memristor arrays for neuro-inspired computing applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.