2021/07/02 by Syed Ghazi Sarwat, Timoleon Moraitis, Sarwat, Syed Ghazi +5 · 2 citations
Computer Science · Physics and Astronomy · #Applied Physics (physics.app-ph) #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Physical sciences #cs.ET #physics.app-ph
paper · pdf · doi:10.48550/arxiv.2107.00915
arxiv created 2021/07/02 · arxiv updated 2021/07/05
Neural processing on devices and circuits is fast becoming a popular approach to emulate biological neural networks. Elaborate CMOS and memristive technologies have been employed to achieve this, including chalcogenide-based in-memory computing concepts. Here we show that nano-scaled films of chalcogenide semiconductors can serve as building-blocks for novel types of neural computations where their tunable electronic and optical properties are jointly exploited. We demonstrate that ultrathin photoactive cavities of Ge-doped Selenide can emulate the computationally powerful non-linear operations of three-factor neo-Hebbian plasticity and the shunting inhibition. We apply this property to solve a maze game through reinforcement learning, as well as a single-neuron solution to the XOR, which is a linearly inseparable problem with point-neurons. Our results point to a new breed of memristors with broad implications for neuromorphic computing.