Brain‐Inspired Photonic Neuromorphic Devices using Photodynamic Amorphous Oxide Semiconductors and their Persistent Photoconductivity

The combination of a neuromorphic architecture and photonic computing may open up a new era for computational systems owing to the possibility of attaining high bandwidths and the low‐computation‐power requirements. Here, the demonstration of photonic neuromorphic devices based on amorphous oxide se...

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Veröffentlicht in:Advanced materials (Weinheim) 2017-07, Vol.29 (28), p.n/a
Hauptverfasser: Lee, Minkyung, Lee, Woobin, Choi, Seungbeom, Jo, Jeong‐Wan, Kim, Jaekyun, Park, Sung Kyu, Kim, Yong‐Hoon
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Sprache:eng
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Zusammenfassung:The combination of a neuromorphic architecture and photonic computing may open up a new era for computational systems owing to the possibility of attaining high bandwidths and the low‐computation‐power requirements. Here, the demonstration of photonic neuromorphic devices based on amorphous oxide semiconductors (AOSs) that mimic major synaptic functions, such as short‐term memory/long‐term memory, spike‐timing‐dependent plasticity, and neural facilitation, is reported. The synaptic functions are successfully emulated using the inherent persistent photoconductivity (PPC) characteristic of AOSs. Systematic analysis of the dynamics of photogenerated carriers for various AOSs is carried out to understand the fundamental mechanisms underlying the photoinduced carrier‐generation and relaxation behaviors, and to search for a proper channel material for photonic neuromorphic devices. It is found that the activation energy for the neutralization of ionized oxygen vacancies has a significant influence on the photocarrier‐generation and time‐variant recovery behaviors of AOSs, affecting the PPC behavior. A brain‐inspired photonic neuromorphic device is demonstrated using an amorphous indium‐gallium‐zinc‐oxide film. By utilizing the persistent photoconductivity behavior, short‐term memory/long‐term memory, spike‐timing‐dependent plasticity, and neural facilitation are emulated, which are the important synaptic functions for learning and memory. This work may open up new possibilities to realize ultrafast and massive parallel synaptic computing systems based on photonic neuromorphic devices.
ISSN:0935-9648
1521-4095
DOI:10.1002/adma.201700951