用于光电神经形态计算的 CMOS 兼容型 Memristor

IF 4.703 3区 材料科学
Facai Wu, Chien-Hung Chou, Tseung-Yuen Tseng
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引用次数: 0

摘要

光电忆阻器是未来光控高密度存储和神经形态计算的理想候选器件。在这项工作中,与CMOS工艺兼容的ITO/HfO2/TiO2/ITO光电忆阻器展示了光可调电阻开关(RS)特性。该器件在可见光下的平均透射率为 79.24%。电铸后,实现了稳定的双极模拟开关、104 秒以上的数据保持和 106 个周期的耐久性。在 405 nm 波长的光照射下,无论是高阻状态还是低阻状态,都能观察到明显的电流增长。电刺激和光刺激都能实现突触特性的长期增效。此外,基于光增效和电传导抑制,模拟霍普菲尔德神经网络(HNN)被训练用于学习 10 × 10 像素大小的图像。经过 13 次迭代,HNN 成功地训练识别输入图像,训练准确率达到 100%。这些结果表明,这种光电忆阻器在神经形态应用方面具有很大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CMOS-Compatible Memristor for Optoelectronic Neuromorphic Computing

Optoelectronic memristor is a promising candidate for future light-controllable high-density storage and neuromorphic computing. In this work, light-tunable resistive switching (RS) characteristics are demonstrated in the CMOS process-compatible ITO/HfO2/TiO2/ITO optoelectronic memristor. The device shows an average of 79.24% transmittance under visible light. After electroforming, stable bipolar analog switching, data retention beyond 104 s, and endurance of 106 cycles are realized. An obvious current increase is observed under 405 nm wavelength light irradiation both in high and in low resistance states. The long-term potentiation of synaptic property can be achieved by both electrical and optical stimulation. Moreover, based on the optical potentiation and electrical depression of conductances, the simulated Hopfield neural network (HNN) is trained for learning the 10 × 10 pixels size image. The HNN can be successfully trained to recognize the input image with a training accuracy of 100% in 13 iterations. These results suggest that this optoelectronic memristor has a high potential for neuromorphic application.

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来源期刊
Nanoscale Research Letters
Nanoscale Research Letters NANOSCIENCE & NANOTECHNOLOGY-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
15.00
自引率
0.00%
发文量
110
审稿时长
2.5 months
期刊介绍: Nanoscale Research Letters (NRL) provides an interdisciplinary forum for communication of scientific and technological advances in the creation and use of objects at the nanometer scale. NRL is the first nanotechnology journal from a major publisher to be published with Open Access.
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