神经形态工程学
CMOS芯片
晶体管
记忆电阻器
计算机科学
电子工程
阈下传导
电气工程
横杆开关
电压
光电子学
材料科学
工程类
人工神经网络
人工智能
作者
Loai Danial,Evgeny Pikhay,Eric Herbelin,Nicolás Wainstein,V.K. Gupta,Nimrod Wald,Yakov Roizin,Ramez Daniel,Shahar Kvatinsky
标识
DOI:10.1038/s41928-019-0331-1
摘要
Metal–oxide memristive integrated technologies for analogue neuromorphic computing have undergone notable developments in the past decade, but are still not mature enough for very large-scale integration with complementary metal–oxide–semiconductor (CMOS) processes. Although non-volatile floating-gate synapse transistors are a more advanced technology embedded within CMOS processes, their performance as analogue resistive memories remains limited. Here, we report a low-power, two-terminal floating-gate transistor fabricated using standard single-poly technology in a commercial 180 nm CMOS process. Our device, which is integrated with a readout transistor, can operate in an energy-efficient subthreshold memristive mode. At the same time, it is linearized for small-signal changes with a two-orders-of-magnitude resistance dynamic range. Our device can be precisely tuned using optimized switching voltages and times, and can achieve 65 distinct resistive levels and ten-year analogue data retention. We experimentally demonstrate the feasibility of a selector-free integrated memristive array in basic neuromorphic applications, including spike-time-dependent plasticity, vector-matrix multiplication, associative memory and classification training. A floating-gate memristive device fabricated in a commercial 180 nm CMOS process can be integrated into a selector-free memristive array and used to demonstrate basic neuromorphic applications.
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