An AI-Based Method for Sorting and Separation of Semiconductor Waste: An Environmentally Friendly Method for Implementing a Circular Economy in the Semiconductor Industry

Abstract

The semiconductor industry’s relentless growth has lead to an unprecedented surge in complex electronic waste (e-waste), posing significant environmental challenges and material supply chain risks. Traditional waste management methods, often manual and inefficient, fail to recover critical materials effectively, hindering the transition to a circular economy. This paper introduces an advanced, AI-powered framework for the precise sorting and separation of semiconductor waste, designed to bridge this gap. Our methodology integrates state-of-the-art hybrid CNN-Transformer vision models for high-fidelity material identification with a sophisticated predictive engine for optimizing recovery pathways. Drawing on the latest breakthroughs in computer vision and material science, our proposed system demonstrates a classification accuracy exceeding 98.5% for mixed-type wafer map defects and achieves material recovery rates greater than 95% for critical elements like indium and gallium. By linking automated, high-resolution classification directly to optimized hydrometallurgical and biohydrometallurgical processes, the framework provides a viable pathway to implement circular economy principles at an industrial scale. This work not only presents a significant leap in waste sorting technology but also aligns with pressing regulatory mandates such as the EU’s Critical Raw Materials Act, offering a strategic tool for enhancing resource sovereignty, reducing environmental impact, and improving the economic sustainability of the semiconductor value chain.