I-Corps: Analog Layout Design Suite
- Institution
- University of Minnesota-Twin Cities, MN
- Principal investigator
- Ramesh Harjani
- Funding
- $50K
- Duration
- 2023–2024
Moonbase score
Still being scored
Abstract
The broader impact/commercial potential of this I-Corps project is the development of technology for state-of-the-art, mixed-signal semiconductor chips or integrated circuits. Applications range from wireless and wired communication to driverless cars, smart cities, and biomedical devices. The software is able to reduce the layout design time by 90 times compared to manual/human design time, while ensuring that the output quality is comparable. Changes in the design process are crucial for the analog integrated circuit market to keep up with the demand for chips and new applications. This technology can provide faster speeds over current processes while reducing user input and effort. This software also opens doors to faster chip design turnaround times and reduces chip design costs, both of which are critical in today’s chip design companies. This I-Corps project is based on the development of technology addressing the bottlenecks in computer chip design turnaround time. The demand for semiconductor chips or integrated circuits has increased significantly over the years due to their use in applications such as wireless communication, smart cities, and smarter cars. These integrated circuits are composed of digital and analog blocks. While the implementation of digital blocks is automated and takes comparatively less effort, analog block implementation is still a manual task that takes months in the industry. This technology is an electronic design automation software that aims to reduce the time and effort required in analog integrated circuits block implementation. The software targets the physical implementation or layout design phase of production where the designed circuit is placed and routed with physical wires as it would be on the silicon chip. This technology automatically generates analog layouts using conventional techniques as well as machine learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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