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

© 2022 The Authors. Advanced Materials published by Wiley-VCH GmbH.

Bibliographische Detailangaben
Veröffentlicht in:Advanced materials (Deerfield Beach, Fla.). - 1998. - 35(2023), 37 vom: 27. Sept., Seite e2205381
1. Verfasser: Farronato, Matteo (VerfasserIn)
Weitere Verfasser: Mannocci, Piergiulio, Melegari, Margherita, Ricci, Saverio, Compagnoni, Christian Monzio, Ielmini, Daniele
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2023
Zugriff auf das übergeordnete Werk:Advanced materials (Deerfield Beach, Fla.)
Schlagworte:Journal Article 2D semiconductors charge-trap memory neural networks neuromorphic engineering reservoir computing
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520 |a 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 complementary metal-oxide-semiconductor (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 a MoS2 channel where memory operation arises, thanks to electron trapping/detrapping at interface states. Transistor operation, memory characteristics, and synaptic potentiation/depression for neuromorphic applications are demonstrated. The CTM device shows outstanding linearity of the potentiation by applied drain pulses of equal amplitude. Finally, pattern recognition is demonstrated 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 
650 4 |a Journal Article 
650 4 |a 2D semiconductors 
650 4 |a charge-trap memory 
650 4 |a neural networks 
650 4 |a neuromorphic engineering 
650 4 |a reservoir computing 
700 1 |a Mannocci, Piergiulio  |e verfasserin  |4 aut 
700 1 |a Melegari, Margherita  |e verfasserin  |4 aut 
700 1 |a Ricci, Saverio  |e verfasserin  |4 aut 
700 1 |a Compagnoni, Christian Monzio  |e verfasserin  |4 aut 
700 1 |a Ielmini, Daniele  |e verfasserin  |4 aut 
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