An Area and Energy Efficient All Resistive Neuromorphic-Computing Platform Implemented by a 4-bit-per-cell RG-FinFET Memory

In this paper, an ALL resistive neuromorphic computing (ARNC) platform was demonstrated with Restive-gate FinFET memory, which includes three major building blocks: weight, ReLU, and ADC. The weight consists of 4-bit-per-cell RG-FinFET memory arrays with gradual and symmetrical tuning capability of the conductance, reliable endurance up to 10
⁵
cycles for whole 16 states, and excellent data retention. ReLU shows linear output responses when the input is positive and sharply cut-off for negative input. The ADC was implemented by a 16 parallel RG-FinFETs, featuring 267 MHz of the operation frequency,
$0.28\ \mu\mathrm{W}$
of the power at V
cc
= 0.8V, and very small area size of 10
⁻⁵
mm
²
. It is well-suited for the energy-efficient AI-Inference in CIM.