基于2D MoSe2纳米片的memristors作为神经形态计算的人工突触和恶感受体
Huan Duan1, Dehui Wang1, Jingxi Gou1
1College of Chemistry and Materials Science, Sichuan Normal University, Chengdu, 610066, China. wenjing.jie@sicnu.edu.cn.
Nanoscale
|May 30, 2023
概括
研究人员使用2D MoSe2纳米片开发了新型的memristor,用于大脑启发的计算. 这些人造突触和神经元表现出模拟切换,使突触可塑性和人工神经网络 (ANN) 的高精度图像识别.
科学领域:
- 材料科学 材料科学 材料科学
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
背景情况:
- 神经形态计算模仿人类大脑,对于人工智能 (AI) 的发展至关重要.
- 人工神经网络 (ANN) 依赖于人工突触和神经元作为基本单元.
- 记忆器是模拟这些人工突触和神经元功能的有希望的候选者.
研究的目的:
- 使用二维化 (MoSe2) 纳米片制造和表征双终端记忆器.
- 为了研究它们对突触可塑性的模拟电阻切换 (RS) 行为.
- 在神经形态计算应用中展示他们的潜力,包括图像识别和人工感应器.
主要方法:
- 使用2D MoSe2纳米片制造双终端记忆器.
- 对模拟电阻开关 (RS) 特性进行分析.
- 突触功能的实施和测试 (配对脉冲促进,LTP,LTD).
- 构建一个用于图像识别和模拟学习忘记行为的ANN.
- 作为识别有害刺激的人工恶感受体的评估.
主要成果:
- 制造的MoSe2记忆器表现出类似的RS行为,这对突触可塑性至关重要.
- 成功模拟关键的突触功能:配对脉冲促进,长期强化和长期抑郁.
- 使用ANN中的memristors实现了92%的高图像识别准确度.
- 证明了模仿人类学习和忘记过程的能力.
- 展示了作为人工恶感受体检测有害刺激的潜力.
结论:
- 基于2D MoSe2纳米板的memristors为神经形态计算提供了一个可行的平台.
- 模拟RS行为非常适合实现突触可塑性和学习功能.
- 这些设备对先进的人工智能应用,包括图像识别和感官仿真,显示出重大前景.
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