NSF and Partners Invest $45M in Future of Semiconductors

New research and education awards will engage talent in semiconductor design and manufacturing.


The U.S. National Science Foundation announced 24 research and education projects with a total investment of $45.6 million — including funding from the "CHIPS and Science Act of 2022" — to enable rapid progress in new semiconductor technologies and manufacturing as well as workforce development.

The projects are supported by the NSF Future of Semiconductors (FuSe) program through a public-private partnership spanning NSF and four companies: Ericsson, IBM, Intel and Samsung.

"Our investment will help train the next generation of talent necessary to fill key openings in the semiconductor industry and grow our economy from the middle out and bottom up," said NSF Director Sethuraman Panchanathan. "By supporting novel, transdisciplinary research, we will enable breakthroughs in semiconductors and microelectronics and address the national need for a reliable, secure supply of innovative semiconductor technologies, systems and professionals."

Future semiconductors and microelectronics will require a broad coalition of science and engineering talent in academic and industrial sectors to pursue holistic, "co-design" approaches that advance materials, devices and systems integration. Co-design approaches simultaneously consider the performance, manufacturability, recyclability and environmental sustainability of such materials, devices and systems.

The FuSe program will accelerate the development of the U.S.-based workforce and knowledge that enable innovative semiconductor and microelectronics.

The FuSe investment for Fiscal Year 2023 supports 24 research and education projects through 61 awards to 47 institutions, including eight to minority-serving institutions and seven to NSF Established Program to Stimulate Competitive Research (EPSCoR) jurisdictions, and addresses three research topics:

Topic 1: Collaborative Research in Domain-Specific Computing

  • Bio-Inspired Sensorimotor Control for Robotic Locomotion with Neuromorphic Architectures Using Beyond-CMOS Materials and Devices - University of Pittsburgh
  • Co-designing Continual-Learning Edge Architectures with Hetero-Integrated Silicon-CMOS and Electrochemical Random-Access Memory - University of Illinois at Urbana-Champaign
  • Efficient Situation-Aware AI Processing in Advanced 2-Terminal SOT-MRAM - Arizona State University, Duke University, Stanford University
  • Enabling Photonic Computing Engines through Hetero-Integration - Queen's College
  • Metaoptics-Enhanced Vertical Integration for Versatile In-Sensor Machine Vision - Washington University in St. Louis, University of Illinois at Urbana-Champaign, University of Rochester.
  • A Reconfigurable Ferroelectronics Platform for Collective Computing - University of Virginia, Georgia Institute of Technology
  • Retunable, Reconfigurable, Racetrack-Memory Acceleration Platform - University of Pittsburgh, Northwestern University, University of California Los Angeles, The University of Texas at San Antonio

Topic 2: Advanced Function and High Performance by Heterogeneous Integration

  • Co-designed Systems for In-sensor Processing with Sustainable Nanomaterials - Duke University
  • Collaborative Optically Disaggregated Arrays of Extreme-MIMO Radio Units - University of California, Berkeley, Boston University, University of California Los Angeles
  • Deep Learning and Signal Processing Using Silicon Photonics and Digital CMOS Circuits for Ultra-Wideband Spectrum Perception - Florida International University, Northeastern University, University of Arkansas, University of Delaware
  • Electronic-Photonic Heterogeneous Integration for Sensing Above 1 THz - University of California Los Angeles
  • Heterogeneous Integration in Power Electronics for High-Performance Computing - Northeastern University, Cornell University
  • Indium Selenides Based Back End of Line Neuromorphic Accelerators - The Pennsylvania State University, University of Pennsylvania, Yale University
  • Monolithic 3D Integration (M3D) of 2D Materials-Based CFET Logic Elements towards Advanced Microelectronic - Washington University in St. Louis, Massachusetts Institute of Technology, University of California Los Angeles, The University of Texas at Austin
  • Substrate-Inverted Multi-Material Integration Technology - Massachusetts Institute of Technology, Dartmouth, University of Delaware
  • Thermal Co-Design for Heterogeneous Integration of Low Loss Electromagnetic and RF Systems - Oregon State University, Florida International University, University of South Florida

Topic 3: New Materials for Energy Efficient, Enhanced-Performance and Sustainable Semiconductor-Based Systems

  • GeSnO2 Alloys for Next-Generation Semiconductor Devices - University of Michigan-Ann Arbor, University of Minnesota Twin Cities
  • Heterogeneous Integration of III-Nitride and Boron Arsenide for Enhanced Thermal and Electronic Performance - The University of Texas at Austin, The Ohio State University, University of Michigan-Ann Arbor, The University of Texas at Dallas
  • High-throughput Discovery of Phase Change Materials for Co-designed Electronic and Optical Computational Devices (PHACEO) - University of Maryland, College Park, Howard University, Massachusetts Institute of Technology, University of Washington in Seattle
  • Interconnects with Co-Designed Materials, Topology and Wire Architecture - Rensselaer Polytechnic Institute, Cornell University, University of Notre Dame
  • Polymer SWIR Photodiodes for Focal Plane Arrays - North Carolina State University, University of North Carolina at Chapel Hill
  • Precise Sequence Specific Block Copolymers for Directed Self-Assembly - Co-design of Lithographic Materials for Pattern Quality, Scaling and Manufacturing - The University of Chicago
  • Spin Gapless Semiconductors and Effective Spin Injection Design for Spin-Orbit Logic - University of Cincinnati, Illinois Institute of Technology, Iowa State University, Northern Illinois University, The University of Alabama
  • Ultra-Low-Energy Logic-in-Memory Computing Using Multiferroic Spintronics - Rice University

These awards will be supported in part by Ericsson, IBM, Intel and Samsung, which have committed to providing annual contributions through NSF. This public-private partnership will help to inform research needs, spur breakthroughs, accelerate technology translation to the market and prepare the future workforce through practical experiences, while addressing the growing demand for semiconductors in the U.S.


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