Litcius/Paper detail

Ultra-Compact Ternary Logic Gates Based on Negative Capacitance Carbon Nanotube FETs

Mohammad Khaleqi Qaleh Jooq, Mohammad Hossein Moaiyeri, Khalil Tamersit

2020IEEE Transactions on Circuits & Systems II Express Briefs70 citationsDOI

Abstract

Ternary logic has been studied for several decades as it can offer significant advantages to reduce the number of interconnects and the complexity of operations. However, the excessive transistor count of the existing ternary logic gates can diminish these advantages in practice. In this brief, based on the negative capacitance (NC) feature of the ferroelectric materials and the well-proven electronic properties of the carbon nanotube field-effect transistor (CNTFET), we have proposed ultra-compact ternary logic gates. After developing a NC-CNTFET model, we have designed a 2-transistor ternary inverter, a 4-transistor ternary NAND, and a 4-transistor ternary NOR with the structures and transistor counts similar to the binary complementary metal-oxide-semiconductor (CMOS) logic. The simulation results ascertain the correct and robust functionality of the proposed ternary gates, even in the presence of process variations. Our proposed ternary inverter, NAND, and NOR gates lead to on average 65%, 60%, and 60% improvements in transistor count, 79%, 83%, and 77% improvements in the area, and 34%, 61%, and 54% improvements in energy-delay product (EDP) as compared to the previous state-of-the-art ternary gates. Our approach accentuates that the proposed ternary gates are the potential candidates for demonstrating more complex multi-valued arithmetic-logic units.

Topics & Concepts

Carbon nanotube field-effect transistorNAND gateTernary operationTransistorLogic gateInverterAND-OR-InvertPass transistor logicComputer scienceLogic familyElectronic engineeringMaterials scienceField-effect transistorElectrical engineeringLogic synthesisAlgorithmEngineeringVoltageProgramming languageFerroelectric and Negative Capacitance DevicesSemiconductor materials and devicesAdvanced Memory and Neural Computing