학술논문

Diode-Connected Overpass Channel Synaptic Transistors for Extremely-Low-Power Neuromorphic Systems
Document Type
Periodical
Source
IEEE Electron Device Letters IEEE Electron Device Lett. Electron Device Letters, IEEE. 44(5):833-836 May, 2023
Subject
Engineered Materials, Dielectrics and Plasmas
Components, Circuits, Devices and Systems
Logic gates
Transistors
Virtual machine monitors
Capacitance
Neural networks
Voltage
Tunneling
Poly-Si-based synaptic transistor
NOR-type array
extremely-low-power operation
spiking neural network (SNN)
Language
ISSN
0741-3106
1558-0563
Abstract
A poly-Si overpass channel synaptic (OCS) transistor is proposed for the extremely-low-power operation and low RC delay of neuromorphic systems. The OCS transistor has two major structural advantages. First, the on- current ( ${I}_{\text {on}}{)}$ can be reduced to sub 100 nA with high on/off ratio ( $10^{{5}}{)}$ because the channel wraps around the fin-shaped bottom gate. Second, the weights of the OCS transistors are finely divided by increasing the volume of the charge storage layer. The inference and weight update operations of a NOR-type OCS array were experimentally demonstrated. It was confirmed that the fabricated diode-connected (D-C) OCS array is suitable for vector-matrix multiplication (VMM) operation with its weighted-sum error smaller than 0.79% in inference. Each synaptic weight in the D-C OCS array was adjusted into sub-nA resolution by Fowler-Nordheim (FN) tunneling with asymmetric gates. Finally, the classification accuracy of the fashion MNIST dataset is 91.29% even after one year with four-bit quantization of spiking neural network (SNN).