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Reservoir Computing with Charge-trap Memory Based on a MoS2 Channel for Neuromorphic Engineering.

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Novel memory devices are essential for developing low power, fast and accurate in-memory computing and neuromorphic engineering concepts that can compete with the conventional CMOS digital processors. 2D semiconductors provide… Click to show full abstract

Novel memory devices are essential for developing low power, fast and accurate in-memory computing and neuromorphic engineering concepts that can compete with the conventional CMOS digital processors. 2D semiconductors provide a novel platform for advanced semiconductors with atomic thickness, low current operation and capability of 3D integration. This work presents a charge trap memory (CTM) device with MoS2 channel where memory operation arises thanks to electron trapping/detrapping at interface states. We demonstrate transistor operation, memory characteristics and synaptic potentiation/depression for neuromorphic applications. The CTM device shows outstanding linearity of the potentiation by applied drain pulses of equal amplitude. We finally demonstrate pattern recognition by reservoir computing where the input pattern is applied as a stimulation of the MoS2 -based CTMs, while the output current after stimulation is processed by a feedforward readout network. The good accuracy, the low current operation and the robustness to input random bit flip makes the CTM device a promising technology for future high-density neuromorphic computing concepts. This article is protected by copyright. All rights reserved.

Keywords: mos2 channel; neuromorphic engineering; trap memory; charge trap; memory; reservoir computing

Journal Title: Advanced materials
Year Published: 2022

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