神经形态工程学
材料科学
光电子学
场效应晶体管
晶体管
阈值电压
MOSFET
超低功耗
电子工程
计算机科学
电压
物理
电气工程
功率(物理)
功率消耗
工程类
人工智能
人工神经网络
量子力学
作者
Jae Seung Woo,Chae Lin Jung,Ki Ryung Nam,Woo Young Choi
标识
DOI:10.1002/aisy.202300242
摘要
Charge‐trapping tunnel field effect transistors (CT‐TFETs) are experimentally demonstrated, and their array operations are discussed for low‐power large‐scale neuromorphic applications. CT‐TFETs cointegrated with charge‐trapping metal–oxide–semiconductor FETs (CT‐MOSFETs) through complementary metal–oxide–semiconductor logic process exhibit ≈2,000× lower on‐current ( I on ) and ≈3,000× lower off‐current ( I off ) than CT‐MOSFETs, rendering them suitable for high‐accuracy large‐scale neuromorphic systems. According to the experimental and simulation results, CT‐TFETs outperform CT‐MOSFETs in terms of more accurate analog vector‐matrix multiplication than that of CT‐MOSFETs due to the following two reasons: first, CT‐TFETs feature a lower voltage (IR) drop resulting from lower I on than that of CT‐MOSFETs. Second, the former is more robust to the IR drops than the latter due to weak channel length modulation. For example, unlike CT‐MOSFETs, the proposed CT‐TFETs exhibit ignorable weight degradation in spite of the 1 Ω wire resistance. CT‐TFET arrays show ≈700× lower power consumption and ≈10% higher MNIST classification accuracy than CT‐MOSFET arrays, making CT‐TFET arrays promising for extensive and versatile neuromorphic computing applications.
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