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Berkeley Lab Targets 2029 Completion for Light Source Upgrade

The upgraded Advanced Light Source synchrotron will deliver soft X-ray beams at least 100 times brighter than the current facility.

WHAT YOU NEED TO KNOW
  • The upgraded Advanced Light Source will deliver soft X-ray beams at least 100 times brighter than the current installation.
  • Berkeley Lab targets project completion in 2029 after nearly a decade of development.
  • Upgrades include a new crystallography sample production facility and support for Hyper-NA EUV Lithography research.

Berkeley Lab is upgrading its Advanced Light Source synchrotron to deliver soft X-ray beams at least 100 times brighter than the existing facility, targeting completion in 2029. The facility, operated for the U.S. Department of Energy Office of Science, reached its performance limits after 30 years of continuous use since opening in 1993.

The upgrade project has been underway for nearly a decade. By improving beam coherence and focusing more light into smaller areas, the facility will provide higher-resolution data on chemical, magnetic, and electronic properties as reactions unfold in real time.

Microelectronics and quantum hardware

Berkeley Lab said the facility will enable a next-generation platform called Hyper-NA EUV Lithography to advance microchip manufacturing. The laboratory's Center for X-Ray Optics previously used the light source to develop foundational extreme ultraviolet lithography techniques starting in the late 1990s. Researchers at the Quantum Systems Accelerator also use the beams to test candidate materials for quantum computing qubits.

Biological tools and autonomous testing

The facility is adding new hardware and software across beamlines for X-ray crystallography and small-angle X-ray scattering, alongside a new crystallography sample production facility. Energy researchers plan to track structural changes in battery particles and catalytic reactions used to generate fuels.

Data generated by the upgraded beamlines will train artificial intelligence models to predict chemical reactions and validate product designs. These datasets will also power automated agents paired with sample-handling robotics to manage trial-and-error laboratory experiments autonomously.

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