This paper presents an algorithm based approach for TFET (Tunnel Field Effect Transistor) design. A numerous number of meta-heuristic algorithms have been used to procure the best device dimension for a Si–Ge (Silicon–Germanium) pocket n-TFET. The foremost important task is to find an alternative to hit and trails based device optimization and thereby improve the device performance by using those techniques. The algorithm based approach requires an objective function. The surface potential based models efficiently represents the device physical properties, thus surface potential based model is used as an objective function. The impact of the channel length, of the Si–Ge layer and device thickness, as well as of oxide thickness are studied by considering them as design variables. The design process involves simulating and validating the obtained dimensions in Technology Computer Aided Design (TCAD). State of art techniques are being outperformed by this algorithmic approach and out of all applied algorithms the Human Behavior Particle Swarm Optimization algorithm (HBPSO) is more accurate. An ON-current of 4.8 × 10^–4 A and OFF-current of 4.8 × 10^–12 A is achieved by optimizing the structure.

本文提出了一种基于算法的TFET（隧道场效应晶体管）设计方法。大量的元启发式算法已用于为Si-Ge（硅-锗）口袋n-TFET获得最佳的器件尺寸。最重要的任务是找到基于命中和痕迹的设备优化的替代方法，从而通过使用这些技术来提高设备性能。基于算法的方法需要目标函数。基于表面电势的模型有效地表示了设备的物理特性，因此基于表面电势的模型被用作目标函数。通过将沟道长度，Si-Ge层和器件厚度以及氧化物厚度的影响作为设计变量来进行研究。设计过程涉及在技术计算机辅助设计（TCAD）中模拟和验证获得的尺寸。这种算法方法的性能正在超越现有技术，在所有应用的算法中，人类行为粒子群优化算法（HBPSO）更准确。导通电流4.8×10通过优化结构，可实现^–4 A的关断电流4.8×10 ^–12A。