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Argonne National Laboratory

Microelectronics Capabilities

Argonne leverages multi-disciplinary teams, world-class facilities, and powerful scientific tools to confront some of the most profound scientific and technological challenges in microelectronics.

Materials Development

Argonne has extensive expertise in design of materials relevant to microelectronics, including quantum materials, polymers, and inks for printed electronics, and in exploration of relevant phenomena that they display.  Argonne also has broad capabilities in materials synthesis and nanofabrication and in the fabrication of devices for high-frequency applications. Our ability to synthesize, characterize, and process novel ultrawide-bandgap materials, such as diamond, puts us in a unique position to continue existing efforts and also to expand efforts in integrating them with other semiconductor materials and explore new physics phenomena underpinning these novel devices. Our focus is on fundamental through use-inspired research on materials, heterostructures, and devices. Our co-design approach is built upon collaborations between basic and applied research. For example, our applied researchers have considerable expertise in printing devices on polymer substrates, and our atomic layer deposition capabilities include a system for in-situ deposition at Argonne‚Äôs Advanced Photon Source. In addition, the capabilities at our Materials Engineering Research Facility are appropriate for some of the microelectronics materials/devices being developed at Argonne.

Measurement and Characterization

Argonne has broad and deep expertise in materials and device measurement. Advanced imaging, scattering, and spectroscopy capabilities are available at the Advanced Photon Source and, through the use of other advanced microscopy capabilities, at the Center for Nanoscale Materials, including transmission electron microscopy and scanning probe microscopy. We can conduct in-situ characterization, including studies of in-situ growth, over a wide range of temperatures and physical environments. We can also explore a wide range of length scales from atomic to the macroscale and timescales ranging from quasi-static to ultrafast, in addition to capabilities for exploring microelectronics under extreme conditions. Critical to all techniques is the ability to carry out faster data acquisition and to use machine learning and AI approaches for smart acquisition and analysis. Argonne has many high-performance computing resources to draw from, including the Argonne Leadership Computing Facility.

Modeling and Simulations

Argonne has extensive expertise in modeling and simulation of materials, devices, and architectures. This expertise includes modeling computer device efficiency and the full software stacks. There is also considerable interest in edge computing, where general development of microelectronic sensors and circuit design are of value. We also have considerable expertise in the development of computational methods for efficient execution of science applications on large-scale systems. Lifecycle analysis tools include analysis of energy, environmental impacts, and cost. Supply-chain modeling allows for an understanding the implications of supply disruption, including evaluation of strategies, consequences and risks on the supply, and demand side of the specific market. The tools also enable modeling the effect of policies on supply chains and resilience analysis and planning for decision makers.

Data Science Approaches

Data science approaches are currently being applied to our microelectronics-related research, consistent with their broad application in other scientific areas of interest across Argonne. Examples include the use of autonomous discovery to create semiconducting polymers and data science approaches applied to Advanced Photon Source studies of ink properties, where data are fed directly to a machine learning tool. A number of these capabilities are unique to Argonne. Our researchers have considerable expertise in the foundations of machine learning and in its application to different disciplines of science. We also have expertise in data intensive computing, including data analysis, data management, and storage.