Angel Yanguas-Gil
Director, Argonne Microelectronics Institute (AMI)
Biography
With a background in theoretical physics, my current research activities focus on materials growth, microelectronics, and novel computing approaches such as neuromorphic and quantum.
In the area of materials, the focus of my research is understanding the fundamentals of materials growth, with a specific emphasis on the areas of advanced electronic materials, semiconductor processing and advanced packaging, both in the context of classical and quantum computing. Active areas of research include the development of in-situ capabilities for automatic materials discovery, the development of state of the art, multiscale simulation tools to predict the scale up of thin film growth and the dynamics of infiltration for high aspect area materials, and the application of AI to accelerate the development of new processes in microelectronics. My research leverages the advanced computing and machine learning, X-ray characterization, and cutting-edge atomic layer deposition capabilities at Argonne.
In microelectronics, my research focuses on the exploration of novel architectures for energy-efficient sensing and processing inspired on the insect brain, for instance for smart sensors, on-chip AI, and detectors . My primary interest is on areas that go beyond the current state of the art in machine learning, such as the development of systems with the ability to adapt to changes in their environment in real time and that are capable of exhibiting task-dependent learning and processing. In addition to the exploration of novel architectures and algorithms, my research also focuses on understanding how to best translate these architectures into hardware, both through the exploration of existing neuromorphic chips, and through the development of design principles and co-design approaches that will help guide the design of novel materials and devices.
In the media
- Evaluating generative AI for research using an open-ended benchmark
- Edge AI devices eye lifelong learning
- Argonne uses AI to optimize ALD in real time
- National Lab Researchers Boost Chip Design Processes With Artificial Intelligence
- Argonne Turns to Insects for Neuromorphic Computing
- Argonne’s machine-learning work may help ease US microchip shortage in time
- Making Chips At 3nm And Beyond
- Argonne Team Looks to Insect Brains as Models for Computer Chip Innovation