Prototyping & Manufacturing Research

Historically, researchers seeking to understand the opportunities and limits of new materials, devices, and circuit technologies have faced practical challenges. This is due to the difficulty of testing at a systems level. Therefore, focusing on developing physical prototypes is crucial for exploring new computing options.

To realize the premise of nanotechnology as hypothesized by Richard Feynman in 1959, area-selective growth and controlled placement of nano- and micro-scale devices are necessary. However, researchers face challenges in prototyping microelectronics. 

By researching prototyping methods, tools, and new materials,  SLAC Microelectronics hopes to ease these challenges and pave the way for the rapid creation of physical prototypes using new energy-efficient technologies.

Facilitating Prototyping

Prototyping challenges can be reduced by advancing high-throughput methods in nanotechnology, fabrication, and machine learning methods. This involves exploring new methodologies according to physics principles, including:

• Design methodologies
• Validation strategies
• Toolset design, emulation, and validation
• Fabrication
• Integration
• Packaging

Prototype Tooling Challenges

Particularly in the United States, dramatic increases in tooling costs pose a significant challenge in characterizing and fabricating new microelectronics technologies. Tooling costs impede the ability of researchers to evaluate new ideas and concepts. One possible solution is Incorporating machine learning into the design, emulation, and validation of tools. 

In parallel, new multi-modal, high-throughput platforms for characterizing materials are also needed. This dual-pronged approach will allow researchers to develop and manufacture prototypes for further study. 

The design and validation of materials and tools require integrated approaches for connecting these to existing and new systems. Therefore, exploring novel synthesis methods for materials and interfaces must move from concept to practice. This way, researchers can test simulation predictions and build richer material information databases.