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Physical neural networks made of analog resistive switching processors are promising platforms for analog computing and for emulating biological synapses. State-of-the-art resistive switches rely on either conductive filament formation or phase change. These processes suffer from poor reproducibility or high energy consumption, respectively. Our work, on one hand, focuses on understanding and controlling the variability of the conductive filament formation in insulating oxide materials. On the other hand, we are innovating alternative synapse designs that rely on a deterministic charge-controlled mechanism, modulated electrochemically in a solid state, and that consists of shuffling the smallest cation, the proton. As typical throughout our research, here, too, we combine experimental synthesis, fabrication, and characterization with first principles-based computational modeling to gain a deep understanding and control of these promising devices.

AI’s influence is undeniable in the digital realm, affecting consumers’ lives and corporate operations. Transferring these advancements to sectors producing physical goods, such as drug discovery and biotech, commodity chemicals, materials for energy and sustainability, and manufacturing, presents a thrilling prospect and a translational challenge. This talk will explore the present use cases and the potential of applying generative AI within the chemistry and materials domain. Unlike a large part of the tech sector, these industries are capital-intensive and cautious, meaning that AI must bridge an “execution gap” between the digital and physical realms for value generation. We will outline strategies to overcome current technical and cultural hurdles.

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.