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There is great interest in “digital twins” to improve many aspects of semiconductor manufacturing, from increased device yield and performance, reduced consumption of energy and materials, increased flexibility, and to enable rapid uptake and scaling of new material, equipment, and process innovations. The digital twin has both physical and virtual components, with bilateral communication and control; the hope is to enable a wide range of models (of equipment, processes, wafers) at different fidelities (physical to simplified empirical, and machine-learning enabled), to support a wide range of “smart” functionalities. The road to digital twins goes through and builds upon many well-trodden paths. Here, several lines of research at MTL since the late 1980’s are highlighted, beginning with elements of the MIT Computer Aided Fabrication Environment including process flow languages, to DOE/Opt methods for automated surrogate model construction, and run by run control to track and compensate for equipment state and wear in CMP and other unit processes. The development of “statistical metrology” methods encompassed characterization and modeling of semiconductor variation, with layout pattern dependent models to identify “hot spots” in planarization, dishing, and erosion for a given design, as well as to guide dummy fill generation. An evolution from statistical to ML/AI approaches, particularly Bayesian methods, enabled design for manufacturability (DFM) for rapid MOSFET characterization, and then rapid fabrication process tuning, as well as AI-enabled anomaly detection. These and other paths bring us to an exciting next stage of the journey: by harnessing advances in sensing and data collection, AI methods, and computational power not possible at the beginning, the community is poised to create and deploy digital twins for semiconductor manufacturing.

The world of quantum mechanics holds enormous potential to address unsolved problems in communications, computation, precision measurements, and machine learning/AI. Dr. Englund's QP-Group at MIT pursues experimental and theoretical research towards machine learning hardware and critical quantum technologies (computing, networking, sensing) by precision control of photons and atomic systems, combining techniques from atomic physics, optoelectronics, and modern semiconductor devices. In this talk, Dr. Englund will share some of the latest research conducted by his group at MIT and their potential applications.

Quantum computers are fundamentally different than conventional computers. They promise to address certain problems that are practically prohibitive and even impossible to solve using today’s supercomputers. The challenge is building one that is large enough to be useful. In this talk, we will provide an overview of contemporary quantum computing at an intuitive level, including the technology, the promise, the hype, and the challenges ahead associated with realizing useful quantum computers at scale.

Building Innovation Bridges – While science, technology, and product development are essential, process and business model innovation are equally critical—and should often take precedence. Keith Dear, Managing Director of Fujitsu’s Centre for Cognitive and Advanced Technologies, will present a case study of their partnership with Callen Lenz, focusing on finding new markets, connecting the UK and Japan, and how this is driving science and technology (S&T) and product innovation, specifically in computer vision. Keith will discuss why Japan is a desirable location for building innovation bridges, how Fujitsu UK has been pursuing this goal and the benefits it brings to both countries and their companies.