Enhancing high-speed digital systems: MVL circuit design with CNTFET and RRAM

Abstract

Multiple Value Logic (MVL) circuits in high-speed digital systems can represent significantly more information than traditional binary logic circuits. The increasing demand for enhanced data storage capacity has driven the adoption of MVL circuits. However, designing MVL circuits presents challenges, particularly in achieving improved performance aligned with the desired threshold voltage. To address this challenge, harnessing carbon nanotube field-effect transistors (CNTFETs) has been instrumental in designing MVL circuits. CNTFETs offer distinct advantages due to their large resistors. Furthermore, substituting these resistors with Resistive Random Access Memory (RRAM) to implement Ternary logic circuits enhances storage capacity by enabling multiple resistance states within a single cell. This paper presents a novel MVL circuit design that leverages the unique characteristics of CNTFETs and RRAM. Additionally, gate implementation is explored using a Recurrent Neural Network − Long-Short Term Memory (RNN-LSTM) based Tasmanian Devil Optimization algorithm. The practical implementation uses Synopsis HSPICE software with CNTFET technology at 32 nm. A comprehensive performance analysis compares this approach with state-of-the-art solutions regarding delay, area, and power consumption. Significant reductions in area, power consumption, and transistor count are revealed with the proposed approach.

Keywords

• Machine learning
• Recurrent neural network
• CNTFET 32 nm technology
• MVL
• RNN-LSTM
• SDG 7