Chalcogenide optomemristors for multi-factor neuromorphic computation

2021/07/02 by Sarwat, Syed Ghazi, Moraitis, Timoleon, Wright, C David +1 · 2 citations
#Applied Physics (physics.app-ph) #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Physical sciences

paper · doi:10.48550/arxiv.2107.00915

Abstract

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.

Cited by

• Conductance-dependent Photoresponse in a Dynamic SrTiO3 Memristor for Biorealistic Computing
• Synthesis of functional chalcogenide materials for memory/sensing devices and their integration into artificial sensory systems

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