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Addressing energy and performance challenges of data communication from microscale to macroscale

The Center for Advanced Semiconductor Chips with Accelerated Performance (ASAP) is working to strengthen U.S. leadership in critical technologies — including high-performance computing, advanced manufacturing, 5G and beyond — by creating new materials and process paradigms for efficient electrical interconnects, photonic integration, and in-memory computing solutions targeting digital, analog, and RF platforms.

ASAP’s research program includes:

  • multiphysics modeling of inverse material design and reliability-aware materials process development for improved interconnect conductors and inter-wire dielectrics; 
  • scalable integration of III-V and III-N devices on silicon for optical and terahertz interconnects; 
  • development of non- volatile CMOS-compatible memory for in-memory and non-von Neumann computing.

ASAP will also strive to advance the new frontier of low temperature, ultra-high aspect ratio, in situ and 3D monitoring, and self- assembled monolithic approaches. The tightly coupled experimental-theoretical approach to materials design, the cross- disciplinary expertise of team members, and experimental capabilities are required to address challenging scientific and industry-relevant problems.

 

 

REGISTER FOR SPRING IAB MEETING

Registration is free. More information found here.

April 18th & 19th, 2024

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Industry Mentorship

Industry support to ASAP Researchers and Projects is invaluable. If your company is interested in providing value to our center, please find more information in the links below.

Research Projects for Mentorship

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Technical Interchange Meeting (TIM) Schedule

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Past TIM Files and Presentations

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ASAP Center

Shaloo Rakheja, Naresh Shanbhag, and Qing Cao discuss how the ASAP Center and its collaborators are meeting the challenges facing the semiconductor industry.

 

The project was supported by the Center for Advanced Semiconductor Chips with Accelerated Performance (ASAP), an IUCRC supported by NSF under Grant #EEC-2231625.

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