Berkeley Emerging Technologies Research Center

The Berkeley Emerging Technologies Research (BETR) Center is a hub of physical electronics research at the University of California, Berkeley.  It serves as a nexus for interactions with companies for long-term research collaborations and knowledge transfer, as well as access to faculty and students who are building the technological foundation for future electronic devices and information systems. Corporate sponsors gain early access to innovative ideas and research results, while university researchers gain insight into challenges faced by industry.  Projects range from new materials and manufacturing processes to novel computing and memory devices to heterogeneous integrated systems.


Mission Statement

The BETR Center fosters innovation in materials, processes and devices toward the vision of ubiquitous electronics and information systems for enhancing health and quality of life in our global society.


Focus Areas

Looming Power Crisis for Computing

Fundamentally new concepts for more energy-efficient logic switches (transistor replacements) and more energy-efficient on-chip communication (interconnect replacements) are needed to extend and go beyond the era of Moore’s Law. In addition to breakthroughs in solid-state science and technology, innovations in circuit design and system architecture will be necessary to avert a power crisis for computing.

Advent of the Internet of Things

The era of ubiquitous computing, wherein electronic devices are pervasive and wirelessly networked with access to cloud computing requires heterogeneous integration to diversify functionality and mechanical flexibility in mobile devices. For these to be affordable, new manufacturing techniques must be developed through interdisciplinary research into novel tools, processes, and materials that are compatible with low-cost plastic substrates.

Proliferation of Big Data Applications

“Big Data” has become the main driver for advances in memory technology and high-performance computing, as real-time processing of large data sets for analytics and machine learning is resulting in burgeoning demand for data storage and rapid transformation of data into intelligence. Hardware innovations (including non-von-Neumann architectures) as well as new computational algorithms and software systems will be needed to meet the demand within reasonable energy and cost constraints.