Application of Silicon-Germanium Source Tunnel-FET to Enable Ultralow Power Cellular Neural Network-Based Associative Memory

Abstract — This paper studies the application of tunnel FET (TFET) in designing a low power and robust cellular neural network (CNN)-based associative memory (AM). The lower leakage, steeper switching slope, and higher output resistance of TFET are exploited in designing an ultralow-power TFET-based operational transconductance amplifier (OTA). A TFET-OTA is utilized as a programmable synaptic weight multiplier for CNN. The ultralow-power of TFET-OTA enables a higher connectivity network even at a lower power, and thereby improves the memory capacity and input pattern noise tolerance of CNN-AM for low power applications. The TFET-based higher connectivity CNN also exploits the unique characteristics of TFET to improve the throughput efficiency of CNN-AM. Index Terms — Associative memory (AM), cellular neural network (CNN), tunnel FET (TFET), ultralow-power computing. I