Distributed federated learning of semiconductor parameters for enhanced manufacturing and design

Abstract

A test and measurement system includes a network and multiple nodes coupled to the network. Each node incorporates a device under test (DUT), one or more test and measurement instruments, one or more artificial intelligence (AI) models, and one or more processors. The processors are configured to generate measurement training data from the DUT using the test and measurement instruments, train the AI models using the measurement training data, and determine a predictive estimate of a performance metric of the DUT based on outputs of the AI models. The system enables distributed or federated learning across nodes without centralizing raw DUT measurements, allowing each node to refine model parameters locally while optionally sharing model updates. By leveraging locally generated measurement data and AI-based predictive analytics, the system enhances device characterization, reliability assessment, and design workflows while reducing data-handling burdens and preserving device-specific confidentiality.

Dependencies are inferred from the claim text.