在用于神经形态计算的溶液处理氧化物晶体管中进行光学/电气控制的Ag+金属化
Rajarshi Chakraborty1, Himanshu Singodia1, Subarna Pramanik1
1School of Materials Science and Technology, Indian Institute of Technology (Banaras Hindu University) Varanasi, Varanasi-221005, Uttar Pradesh, India. bnpal.mst@iitbhu.ac.in.
Nanoscale
|November 18, 2025
概括
本研究介绍了一种基于氧化物的新型记忆晶体管用于神经形态计算,为突触功能提供双光学和电气控制. 该设备表现出高稳定性和低能耗,为高效的大脑启发的计算系统铺平了道路.
科学领域:
- 材料科学 材料科学 材料科学
- 电子 电子 电子 电子 电子 电子 电子
- 计算机科学 计算机科学
背景情况:
- 神经形态计算旨在模仿人类大脑的结构和功能.
- 记忆器件是构建人工突触的关键组件.
- 开发高效和稳定的memristors对于推进神经形态系统至关重要.
研究的目的:
- 为神经形态计算设计和演示一种可处理溶液的基于氧化物的记忆晶体管.
- 通过门电压和光调节实现通道导电量的双调性.
- 评估设备在复制突触功能和启用认知任务方面的性能.
主要方法:
- 使用LiInSnO4,SnO2和Ag+交换的LiV3O8.8制造一个记忆晶体管.
- 在电气和光学刺激下设备性能的表征.
- 测试突触功能的测试,如配对脉冲促进和可塑性.
- 展示光驱动的逻辑和认知功能.
- 神经网络模拟用于识别准确性评估.
主要成果:
- 墨晶体管具有双调性,在低电压下运行,LRS/HRS比率高达10^3.3.
- 在10^3个切换周期,10^6个脉冲周期和10^5秒的保留时间中表现出稳定的性能.
- 在超低能耗 (193 pJ 光学,540 pJ 电气) 的情况下,突触功能的复制.
- 成功展示了光驱动逻辑,认知功能和帕夫洛夫条件的成功展示.
- 在神经网络模拟中实现了98% (光学) 和95% (电气) 的识别精度.
结论:
- 开发的基于氧化物的memtransistor是高效神经形态计算的有希望的候选者.
- 双电和光学调制提供了对突触重量调制的精确控制.
- 该设备执行复杂认知功能的能力突显了它对大脑启发人工智能的潜力.
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