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The Award database is continually updated throughout the year. As a result, data for FY21 is not expected to be complete until September, 2022.

Download all SBIR.gov award data either with award abstracts (290MB) or without award abstracts (65MB). A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.

  1. Micro-Supercapacitor for Integration with MEMS Energy Harvesting and CMOS ICs

    SBC: CFD Research Corporation            Topic: DMEA21A001

    percapacitors provide energy densities comparable to thin film batteries, high power densities, ultrafast recharge, and long cycle lives for applications requiring high duty cycle and pulse power output.  Micro-supercapacitors (MSC) are the most promising energy storage technology for integration with energy harvesting, via micro-electro-mechanical systems (MEMS), and microelectronics integrated ...

    STTR Phase I 2021 Department of DefenseDefense Microelectronics Activity
  2. Graphenated Carbon Nanotube Based MEMS Supercapacitors

    SBC: Faraday Technology, Inc.            Topic: DMEA21A001

    The inherent advantages of MEMS technology, including small size and cost-effective fabrication, make it ideal for numerous application in a wide range of industries ranging from defense, automotive, medical, to consumer industries. For applications that require self-powered MEMS electronics, an integrated energy storage device is required. Due to their small size, excellent cycle life and high po ...

    STTR Phase I 2021 Department of DefenseDefense Microelectronics Activity
  3. Integrated Micro-Supercapacitors via Laser Induced Graphene from Photoresist

    SBC: Cornerstone Research Group, Incorporated            Topic: DMEA21A001

    While supercapacitors have been demonstrated for decades, the biggest challenge is to develop a reliable fabrication strategy which can integrate these devices with current CMOS (complementary metal oxide semiconductor) technology. Current fabrication technologies do not have good compatibility with other electronic components or cannot manufacture the supercapacitor in a small form factor. Conseq ...

    STTR Phase I 2021 Department of DefenseDefense Microelectronics Activity
  4. Energy & Power Dense Supercapacitor: On-Chip Integration in MEMs Fabrication

    SBC: MAINSTREAM ENGINEERING CORPORATION            Topic: DMEA21A001

    Due to recent advances in the field of microelectronics, there is an increasing demand for micro-sized energy storage devices that are capable of being incorporated into and provide energy for MEMS devices. In order to continue to enable this technological growth, and the benefits that stem from it, the storage density of electrical energy must also continually be improved, especially with respect ...

    STTR Phase I 2021 Department of DefenseDefense Microelectronics Activity
  5. Computerized Robotic Delayering and Polishing System

    SBC: Spectral Energies, LLC            Topic: DMEA18B001

    The proposed research and technical objectives in this project deal with computerized automatic delayering and polishing system that would be applicable to both commercial and government semiconductor device research and development with applications including Failure Analysis (FA), Fault Isolation (FI), and Reverse Engineering (RE) of semiconductor microelectronic devices. This project could hel ...

    STTR Phase I 2019 Department of DefenseDefense Microelectronics Activity
  6. Algorithms for Look-down Infrared Target Exploitation

    SBC: Signature Research, Inc.            Topic: 1

    Signature Research, Inc. (SGR) and Michigan Technological University (MTU) propose a Phase I STTR effort to develop a learning algorithm which exploits the spatio-spectral characteristics inherent within IR imagery and motion imagery.Our archive of modelled and labeled data sets will allow our team to thoroughly capture the variable elements that will drive machine learning performance.The overall ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  7. Algorithm Performance Evaluation with Low Sample Size

    SBC: Signature Research, Inc.            Topic: NGA20C001

    The team of Signature Research, Inc. and Michigan Technological University will develop and demonstrate methods and metrics to evaluate the performance of machine learning-based computer vision algorithms with low numbers of samples of labeled EO imagery. We will use the existing xView panchromatic dataset to demonstrate a proof-of-concept set of tools. If successful, in Phase II, we will extend t ...

    STTR Phase I 2021 Department of DefenseNational Geospatial-Intelligence Agency
  8. Low-shot Automated Performance Prediction via Transfer Learning

    SBC: Etegent Technologies, Ltd.            Topic: NGA20C001

    Low-shot objection recognition has become an area of active research in recent years, with advances dramatically improving performance when only a few samples are available, nominally fewer than 20. These technologies are a focus of the intelligence community (IC) because this challenge pertains to many intelligence problems, e.g., objects of interest are rare due to their use, sensitive nature, ...

    STTR Phase I 2021 Department of DefenseNational Geospatial-Intelligence Agency
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