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Award Data

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The Award database is continually updated throughout the year. As a result, data for FY24 is not expected to be complete until March, 2025.

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.

The SBIR.gov award data files now contain the required fields to calculate award timeliness for individual awards or for an agency or branch. Additional information on calculating award timeliness is available on the Data Resource Page.

Displaying 1 - 10 of 5036 results
  1. Sub-One PUE through Silicon Cooling Efficiency

    SBC: JETCOOL TECHNOLOGIES INC            Topic: 1

    To achieve a quantum step in data center efficiency, new thermal management technologies must be coupled to the devices they cool. This proposal seeks to build on recent observations that microconvective cooling not only functions as a high-performance chip cooling technique with the potential for a low PUE, but is also capable of increasing the intrinsic efficiency of the silicon processors them ...

    SBIR Phase II 2023 Department of EnergyARPA-E
  2. Sub-One PUE through Silicon Cooling Efficiency

    SBC: JETCOOL TECHNOLOGIES INC            Topic: 1

    To achieve a quantum step in data center efficiency, new thermal management technologies must be coupled to the devices they cool. This proposal seeks to build on recent observations that microconvective cooling not only functions as a high-performance chip cooling technique with the potential for a low PUE, but is also capable of increasing the intrinsic efficiency of the silicon processors them ...

    SBIR Phase I 2023 Department of EnergyARPA-E
  3. NANOPROTONIC DEVICES FOR > 240× PERFORMANCE ANALOG AI HARDWARE

    SBC: Eva Technology Corp.            Topic: C

    This proposal seeks the development of analog AI training processors with more than 240× performance to push the boundaries of AI without spending billions of dollars to train each new advanced model, or worse, burning down the entire planet. The building blocks of these architectures will be the novel class of nanoprotonic devices with ideal characteristics we will develop here, such that the re ...

    SBIR Phase I 2023 Department of EnergyARPA-E
  4. NANOPROTONIC DEVICES FOR > 240× PERFORMANCE ANALOG AI HARDWARE

    SBC: Eva Technology Corp.            Topic: C

    This proposal seeks the development of analog AI training processors with more than 240× performance to push the boundaries of AI without spending billions of dollars to train each new advanced model, or worse, burning down the entire planet. The building blocks of these architectures will be the novel class of nanoprotonic devices with ideal characteristics we will develop here, such that the re ...

    SBIR Phase II 2023 Department of EnergyARPA-E
  5. Enabling Resilient and Secure Domestic Supply Chains for Critical Reactor Components with Novel Materials and Additive Manufacturing

    SBC: Foundation Alloy Technology Explorations, Inc            Topic: C

    Foundation Alloy integrates material design and part production by developing a new class of nanostructured alloys specifically engineered for powder metallurgy-based processing through 3D printing and sintering. Foundation Alloy is developing a design platform for new alloys that combine unprecedented performance with a unique suitability to today’s advanced manufacturing techniques. Foundation ...

    SBIR Phase I 2023 Department of EnergyARPA-E
  6. Enabling Resilient and Secure Domestic Supply Chains for Critical Reactor Components with Novel Materials and Additive Manufacturing

    SBC: Foundation Alloy Technology Explorations, Inc            Topic: C

    Foundation Alloy integrates material design and part production by developing a new class of nanostructured alloys specifically engineered for powder metallurgy-based processing through 3D printing and sintering. Foundation Alloy is developing a design platform for new alloys that combine unprecedented performance with a unique suitability to today’s advanced manufacturing techniques. Foundation ...

    SBIR Phase II 2023 Department of EnergyARPA-E
  7. Microwave Radiator for Curing Polymer Composites (MRCPC)

    SBC: PHYSICAL SCIENCES INC.            Topic: N23AT006

    The Navy requires a low-cost, industrial microwave system for curing aerospace composite materials. In this Phase I STTR proposal, Physical Sciences Inc outlines the development of a microwave applicator that uses low-cost RF sources and can be installed in heritage autoclaves for curing large aerospace composite parts. This technology has the potential to improve cured mechanical properties, and ...

    STTR Phase I 2023 Department of DefenseNavy
  8. Large-scale Meta-optic Optimization

    SBC: PHYSICAL SCIENCES INC.            Topic: N23AT008

    Physical Sciences Inc. (PSI), in collaboration with Stanford University, will develop an electromagnetic simulation package used for the development and optimization of large-scale meta-optics, and demonstrate the functionality of the package in the long-wave infrared (LWIR). Our team will combine recent progress in physics-augmented deep learning neural networks with rigorous far-field diffractio ...

    STTR Phase I 2023 Department of DefenseNavy
  9. Gradient index for reduced integration costs (GRIN-RICH)

    SBC: PHYSICAL SCIENCES INC.            Topic: N23AT011

    Physical Sciences Inc. partnered with Alfred University will develop an F/1, 90 degree full field of view MWIR/SWIR gradient index (GRIN) compound lens for reduced size and lens integration cost. The element-by-element achromatization and athermalization of GRIN provide useful performance improvements to GRIN systems. Element count is reduced (= 2), diversity of optical material needed is fixed, a ...

    STTR Phase I 2023 Department of DefenseNavy
  10. Sensor Modality Translation through Contrastive Deep Learning

    SBC: PHYSICAL SCIENCES INC.            Topic: N23AT013

    Physical Sciences Inc. (PSI), in collaboration with the University of Rhode Island, proposes to develop an advanced algorithm suite for data translation across sensing modalities to support the development of automated target recognition and classification algorithms for Unmanned Underwater Vehicles. The proposed Deep Diffusion Sensor Translation (DDST) leverages recent advancements in generative ...

    STTR Phase I 2023 Department of DefenseNavy
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