A double-gate heteromaterial tunnel FET optimized using an evolutionary algorithm

摘要

A new technique is proposed herein for the design and optimization of double-gate heteromaterial tunnel field-effect transistors (FETs). The presented approach determines the optimum device dimensions, using a reference analytical model of the surface potential as an objective function. Many high-performance evolutionary optimization algorithms have been applied to determine optimum dimensions at reduced computational cost and complexity. A comparison of all these algorithms reveals particle swarm optimization to be the most suitable in terms of achieving an optimum surface potential at higher convergence speed. The optimized values are validated against technology computer-aided design (TCAD) simulation results, revealing acceptable values for the ON-current, and an OFF-current of 4.8 x 10(-15) A in accordance with International Technology Roadmap for Semiconductor (ITRS) 2014 requirements. The subthreshold slope is found to be 45 mV/dec. The algorithm dynamically fixes the lengths of the source, channel, and drain junctions, device thickness, etc. The automated determination of these parameters can effectively improve the computational complexity while providing accurate designs for tunnel field-effect transistors.