Authors - Neeraj Mathur, Jiby Mariya Jose Abstract - Material Control Systems (MCS) serve as a critical software layer that coordinates material flow by issuing transport commands, tracking material lo-cations, and interfacing with factory equipment and automated handling systems. Although the term may appear to focus primarily on inventory management, it is most commonly used in high-tech environments such as semiconductor manufacturing to describe the software layer that manages, directs, and optimizes the movement, storage, and routing of materials (e.g., wafers and carriers) within a production or logistics environment. This paper presents the development and implementation of a novel Physical AI–based Material Control System. Unlike traditional MCS architectures that rely on rigid rule-based dispatching, the proposed approach leverages a Physical AI plat-form to enable unified and adaptive control across heterogeneous hardware, including stockers, Autonomous Mobile Robots (AMRs), and Overhead Hoist Transport (OHT) systems. By integrating real-time sensor fusion and adaptive motion planning, the proposed system enhances process logistics in semiconductor backend facilities, where high-mix production requires highly dynamic coordination between storage and transport resources.
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