On Digital Twins for Semiconductor Manufacturing

This presentation will expound the challenges involved in the generation of digital twins (DT) as the central tools for supporting innovation and providing informed decision support for the optimization of in-service performance of complex physical machines, devices, and components, alongside prognostics for their sustainment and life extension. A DT is comprised of a set of virtual information constructs, designed to provide an accurate in-silico representation of a physical object or system, with continual bidirectional information flow tracking the internal state, and in-service functional response of the physical twin (PT). This presentation will describe the foundational AI/ML (artificial intelligence/machine learning) concepts and frameworks needed to formulate and continuously update the DT of the selected PT. The central challenge comes from the need to establish reliable models for predicting the functional response of the PT, which is expected to exhibit highly complex, stochastic, nonlinear behavior, with functional transience conditioned on varied use conditions or service operations of the PT. This task demands a rigorous statistical treatment (i.e., uncertainty reduction, quantification and propagation through a network of human-interpretable models) and fusion of insights extracted from inherently incomplete (i.e., limited information gathered with the available sensors), uncertain, and disparate (due to diverse sources of data gathered at different times and fidelities, such as physical experiments, numerical simulations, and domain expertise) data used in calibrating both the initial model of the PT as well as its continuous update. This presentation will illustrate with examples how a suitably designed Bayesian framework combined with emergent AI/ML toolsets can uniquely address this challenge. Specifically, we will demonstrate the important roles of (i) emergent AI/ML toolsets for Bayesian inference (e.g., multi-output Gaussian process regression, generative models), (ii) high-throughput strategies for designing and employing non-standard experiments, and (iii) a SaaS platform for enabling highly efficient collaboration and knowledge sharing between distributed teams/participants in realizing the goals described above.