Energy-Efficient III-V Tunnel FET-Based Synaptic Device with Enhanced Charge Trapping Ability Utilizing Both Hot Hole and Hot Electron Injections for Analog Neuromorphic Computing - Daehwan, Ahn; HU, SU MAN; Ko, Kyul; Park, Dong Hee; Suh, Ho young; Kim, Gyu-Tae; Han, Jae Hoon; SONG, JIN DONG; Jeong, Yeon Joo (American Chemical Society, 2022)
A charge trap device based on field-effect transistors (FET) is a promising candidate for artificial synapses because of its high reliability and mature fabrication technology. However, conventional MOSFET-based charge trap synapses require a strong stimulus for synaptic update because of their inefficient hot-carrier injection into the charge trapping layer, consequently causing a slow speed operation and large power consumption. Here, we propose a highly efficient charge trap synapse using III-V materials-based tunnel field-effect transistor (TFET). Our synaptic TFETs present superior subthreshold swing and improved charge trapping ability utilizing both carriers as charge trapping sources: hot holes created by impact ionization in the narrow bandgap InGaAs after being provided from the p(+)-source, and band-to-band tunneling hot electrons (BBHEs) generated at the abrupt p(+)n junctions in the TFETs. Thanks to these advances, our devices achieved outstanding efficiency in synaptic characteristics with a 5750 times faster synaptic update speed and 51 times lower sub-tJ/mu m(2) energy consumption per single synaptic update in comparison to the MOSFET-based synapse. An artificial neural network ANN) simulation also confirmed a high recognition accuracy of handwritten digits up to similar to 90% in a multilayer perceptron neural network based on our synaptic devices.