材料科学
神经形态工程学
晶体管
光电子学
薄膜晶体管
可扩展性
人工神经网络
绝缘体(电)
微晶
集成电路
多晶硅
CMOS芯片
半导体
磁滞
纳米技术
绝缘体上的硅
逻辑门
电子工程
过程(计算)
电压
突触
非易失性存储器
突触重量
计算机科学
量子隧道
作者
Taebin Lim,Solbee Lee,Heerak Wi,Kwak Joon-Young,Jin Jang
标识
DOI:10.1021/acsami.5c20927
摘要
Metal-oxide semiconductor (MOS)-based synaptic transistors are promising candidates for highly integrated neuromorphic chips. Ferroelectrics and electrolytes have been extensively studied, but they have not satisfied the scalability and integration of the chips. We report a synaptic thin-film transistor (TFT) based on polycrystalline InGaO (C-IGO) semiconductor, exhibiting a large counterclockwise hysteresis with a memory window (MW) ratio of 57%, attributed to positively charged oxygen vacancy (Vo+/++) migration within the C-IGO layer. The C-IGO TFT could be integrated with the back-end-of-line (BEOL) process. The scalable process could be achieved with self-aligned (SA) coplanar C-IGO TFT with conventional gate insulator (GI) materials. The C-IGO synaptic TFT exhibits high synaptic performance, including long-term potentiation/depression (LTP/LTD) characteristics. Modified National Institute of Standards and Technology (MNIST) handwritten digit image recognition simulation based on potentiation/depression curves yields high recognition accuracy (91.64%). The results validate the potential for high-density, neuromorphic, and artificial intelligence (AI) applications.
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