IP-aware Federated Manufacturing Intelligence

The application of deep learning in semiconductor manufacturing holds great promise but faces significant challenges, particularly in safeguarding intellectual property (IP) during data exchanges. Consider a scenario where the same equipment is deployed across multiple manufacturers. Equipment providers need to collect operational data to offer continuous service improvements, while manufacturers must balance the utility of shared data with the risk of IP leakage. This challenge is compounded by the opacity of deep learning models, raising the question: how can manufacturers ensure their sensitive data remains protected? We address this issue with the IP-aware Federated Manufacturing Intelligence (IPFMI) framework, a novel distributed learning architecture designed to ensure IP protection in federated learning (FL) settings. In IPFMI, data collected by Remote Equipment (RE) at each Semiconductor Manufacturer (SM) undergoes an IP-preserving transformation before being shared with a centralized Service Architect (SA). The SA aggregates this sanitized data to train a central model, enabling performance improvements such as predictive maintenance, process optimization, and anomaly detection without accessing raw data. This approach strikes a balance between utility and privacy, facilitating secure, privacy-preserving collaboration between manufacturers and equipment providers.