Human-Machine Collaboration in Semiconductor Processes

Abstract

Human-machine collaboration represents an essential frontier for optimizing semiconductor fabrication processes, addressing unique challenges that require both human expertise and computational precision. This article explores the transformative integration of AI-powered systems, augmented reality interfaces, digital twins, and collaborative technologies in semiconductor manufacturing operations. AI-vision systems and cobots enhance precision handling while minimizing contamination risks, while augmented reality interfaces project process parameters and maintenance procedures directly into operators' field of vision. Digital twin technology creates virtual representations allowing engineers to test configurations without disrupting production, complemented by human-in-the-loop machine learning systems that incorporate operator feedback for improved anomaly detection and predictive maintenance. Explainable AI models provide transparent reasoning for process adjustments while knowledge management systems systematically capture best practices and preserve institutional memory. Through these collaborative human-machine partnerships, semiconductor manufacturers achieve higher productivity, improved yields, and accelerated innovation cycles while addressing challenges in interface design, cybersecurity, and establishing optimal automation-supervision equilibrium.