隨著時代與技術的發展，自1950年代第一個積體電路誕生以來，以及摩爾定律的提出，晶片技術隨著製程能力的提升，每年都以倍數成長，推動了科技業的快速發展。目前，大多數電子元件使用矽作為主要材料。矽作為間接能隙半導體，其發光效率較低，因此提升其光致發光能力成為一項重要挑戰。本研究旨在探討矽材料的光學特性，特別是透過摻雜III-V族元素（硼與砷）對其光致發光特性的影響。
本研究使用第一原理模擬軟體VASP(Vienna Ab initio Simulation Package)進行模擬，並配搭使用CHGNet機器學習預測矽在不同摻雜條件下的光學特性，並探討隨著摻雜元素之間距離變化對光致發光強度的影響。此外，我們使用Python套件PyPhotonic進行光學特性的分析與模擬，進一步探討摻雜對元件性能的影響。研究的目的是通過理解摻雜對矽發光特性的作用，為未來矽基光電元件的發展提供理論支持。

With the advancement of technology over time, since the birth of the first integrated circuit in the 1950s and the introduction of Moore's Law, chip technology has exponentially grown with improvements in manufacturing processes, driving the rapid development of the tech industry. Currently, most electronic devices use silicon as the primary material. However, as silicon is an indirect bandgap semiconductor, its luminescence efficiency is relatively low, making it a significant challenge to enhance its photoluminescence. This study aims to investigate the optical properties of silicon, specifically through the co-doping of III-V group elements (boron and arsenic) and their effects on its photoluminescence.
In this research, we used the first-principles simulation software VASP (Vienna Ab initio Simulation Package) and CHGNet which is a graph neural network-based machine-learning interatomic potential (MLIP) to predict the optical properties of silicon under different doping conditions and to explore the effects of varying distances between dopant atoms on photoluminescence intensity. Additionally, we utilized the Python package PyPhotonic to further analyze and simulate the optical characteristics, examining how doping impacts device performance. The goal of this study is to understand how doping affects the luminescence properties of silicon, providing theoretical support for the development of future silicon-based optoelectronic devices.

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