Accelerated Learning in Wide-Band-Gap AlN Artificial Photonic Synaptic Devices: Impact on Suppressed Shallow Trap Level,Nano Letters

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Accelerated Learning in Wide-Band-Gap AlN Artificial Photonic Synaptic Devices: Impact on Suppressed Shallow Trap Level
Nano Letters ( IF 9.6 ) Pub Date : 2021-07-30 , DOI: 10.1021/acs.nanolett.1c01885
Moonsang Lee ₁ , Seunghyun Nam ₁ , Byungjin Cho ₂ , Ojun Kwon ₂ , Hyun Uk Lee ₃ , Myung Gwan Hahm ₁ , Un Jeong Kim ₄ , Hyungbin Son ₅

Affiliation

Artificial synaptic platforms are promising for next-generation semiconductor computing devices; however, state-of-the-art optoelectronic approaches remain challenging, owing to their unstable charge trap states and limited integration. We demonstrate wide-band-gap (WBG) III–V materials for photoelectronic neural networks. Our experimental analysis shows that the enhanced crystallinity of WBG synapses promotes better synaptic characteristics, such as effective multilevel states, a wider dynamic range, and linearity, allowing the better power consumption, training, and recognition accuracy of artificial neural networks. Furthermore, light-frequency-dependent memory characteristics suggest that artificial optoelectronic synapses with improved crystallinity support the transition from short-term potentiation to long-term potentiation, implying a clear emulation of the psychological multistorage model. This is attributed to the charge trapping in deep-level states and suppresses fast decay and nonradiative recombination in shallow traps. We believe that the fingerprints of these WBG synaptic characteristics provide an effective strategy for establishing an artificial optoelectronic synaptic architecture for innovative neuromorphic computing.

中文翻译：

宽带隙 AlN 人工光子突触器件中的加速学习：对抑制浅陷阱水平的影响

人工突触平台有望用于下一代半导体计算设备；然而，最先进的光电方法仍然具有挑战性，因为它们的电荷陷阱状态不稳定且集成度有限。我们展示了用于光电神经网络的宽带隙 (WBG) III-V 材料。我们的实验分析表明，WBG 突触增强的结晶度促进了更好的突触特性，例如有效的多级状态、更宽的动态范围和线性，从而使人工神经网络具有更好的功耗、训练和识别精度。此外，光频相关的记忆特性表明，具有改善结晶度的人工光电突触支持从短期增强到长期增强的转变，这意味着对心理多存储模型的清晰模仿。这归因于深能级中的电荷俘获，并抑制了浅陷阱中的快速衰减和非辐射复合。我们相信这些 WBG 突触特征的指纹为建立用于创新神经形态计算的人工光电突触架构提供了有效的策略。

更新日期：2021-09-22

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