In this paper, we investigate an optimized design of textured graphene nanoribbon phototransistor via quantum simulations. The graphene layer with textured morphology performs dual role, notably as the carrier conduction channel and also as an absorber of light. The proposed design characteristics are numerically investigated via a non-equilibrium Green’s function (NEGF) mode-space formulism in the ballistic edge. The method suggested was tailored to the modern carrier transport model, Electron-photon and Electron-phonon interactions. Besides, a simple analytical model that considers graphene light trapping morphology is developed and introduced into the simulation. This model is regarded as a fitness function for Multi-Objective Genetic Algorithm (MOGA) optimization technique to improve the GNR phototransistor sensing capability. Simulations results show that, compared to a conventional phototransistor, the proposed design has higher responsivity (47.55 mA/W), good sensitivity (2.41 × 10³), higher detectivity (6.09 × 10¹⁰ Jones), better Photocurrent to Dark Current Ratio (PDCR) (24.15) and keeping better scaling capability.

在本文中，我们通过量子模拟研究了织构化的石墨烯纳米带光电晶体管的优化设计。具有织构形态的石墨烯层起双重作用，特别是作为载流子传导通道以及光吸收剂。通过弹道边缘的非平衡格林函数（NEGF）模式空间公式对所提出的设计特性进行了数值研究。建议的方法是针对现代载流子传输模型，电子-光子和电子-声子相互作用量身定制的。此外，开发了一个考虑石墨烯光俘获形态的简单分析模型并将其引入仿真中。该模型被视为多目标遗传算法（MOGA）优化技术的适应度函数，以提高GNR光电晶体管的感应能力。³），更高的探测灵敏度（6.09×10 ¹⁰ Jones），更好的光电流与暗电流之比（PDCR）（24.15），并保持更好的缩放能力。