Deep learning algorithms, such as those employed by CHATGPT/artificial neural networks, have served as transformative catalysts across various sectors, significantly impacting our daily lives. Within the realm of semiconductor processes, the metric of yield stands as a crucial performance benchmark directly influencing the sustained success of enterprises. Among batches of chips, inherent disparities in quality persist, with each chip grade necessitating an appropriate operational environment. To preempt the pitfalls of customer returns or factory repairs, thereby mitigating manufacturing costs, in this study, we embark on a comprehensive exploration utilizing AI algorithms for quality classification. In pursuit of bolstering the competitive edge of semiconductor firms, the refinement of gallium arsenide chip quality classification to augment yield emerges as a paramount concern. Moreover, in this research endeavor, we seek to discern the physical parameters underlying GaAs defect products, offering pivotal insights to enhance the manufacturing process—an aspect poised to be a pivotal focal point.