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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.

  1. Multisensor-Integrated Organ-on-a-Chip

    SBC: SPECTRAL ENERGIES LLC            Topic: AF19AT002

    The Air Force seeks three-dimensional bioprinted tissue that can accurately replicate complex multi-cell function and that can be integrated with biosensors. To address this need, Spectral Energies in collaboration with Prof. Khademhosseini of the University of California, Los Angeles (UCLA) proposes to develop an organ-on-a-chip system. The organ-on-a-chip system will be capable of accurately mod ...

    STTR Phase I 2019 Department of DefenseAir Force
  2. Ultrahigh-Bandwidth Robust Performance Diagnostics for Rotating Detonation Engines

    SBC: SPECTRAL ENERGIES LLC            Topic: AF19AT011

    Spectral Energies proposes to design a multisensory diagnostic suite for measurements within elevated-pressure RDEs. This sensor will utilize tunable-laser absorption spectroscopy to measure temperature, pressure and H2O concentrations in the annulus of a rocket-RDE and background-oriented schlieren imaging system for flow density gradient imaging to provide time resolved information about the sho ...

    STTR Phase I 2019 Department of DefenseAir Force
  3. Wave-Optic Propagation Computation Enabled by Machine Learning Algorithms (WOPA)

    SBC: Luminit LLC            Topic: AF18BT004

    To address the U.S. Air Force need for Developing innovative wave-optics Propagation methods to model laser systems that are faster, efficient and more accurate, Luminit, LLC, and University of Southern California (USC) propose to develop Wave-Optic Propagation Computation Enabled by Machine Learning Algorithms (WOPA). The proposed algorithms will be based on cutting off redundant frequencies upon ...

    STTR Phase I 2019 Department of DefenseAir Force
  4. Solar Blind MgZnO Photodetectors

    SBC: AGNITRON TECHNOLOGY, INC.            Topic: A13AT006

    This Phase I program is focused on enhancement of the performance of MgZnO based solar blind detectors. MgZnO alloys have superior optoelectronic properties with bandgaps suitable for solar blind detection. Issues related to doping and miscibility will be addressed. This will involve the use of advanced MOCVD and MBE growth techniques and consideration of both Schottky and p-n junction devices. No ...

    STTR Phase I 2013 Department of DefenseArmy
  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. Detection of Radio Frequency and Magnetic Field Bioeffects in Living Cells

    SBC: QUINC.TECH INC.            Topic: AF18AT001

    The Biomagnetics Micro Dosimetry System (BMDS) program will design, model, and simulate a microdosimetry system that can measure and create a three dimensional map of weak radiofrequency signals in biological organisms. The heart of the BMDS project is the front end called a Quinc, that delivers very sensitive, broad band measurements with high spatial resolution.The Quinc based front end is a val ...

    STTR Phase I 2018 Department of DefenseAir Force
  7. System for Nighttime and Low-Light Face Recognition

    SBC: POLARIS SENSOR TECHNOLOGIES INC            Topic: SOCOM18A001

    The objective of this proposal is to develop instrumentation and algorithms for acquiring facial features for facial recognition in low- and no-light conditions.We will use cross-spectrum matching by exploiting infrared polarimetric imagery which tends to show features that match more closely visible imagery than conventional infrared.In addition to thermal infrared, we will also test subjects in ...

    STTR Phase I 2018 Department of DefenseSpecial Operations Command
  8. Exploitable Physics for Recognition and Classification

    SBC: MATRIX RESEARCH INC            Topic: AF12BT06

    ABSTRACT: The objective of this effort is to develop innovative methods for deriving a sparse set of physical target features that can be used for exploitation of air to ground signature data collected from sensor systems including electro-optical, infrared, and laser radar. Current classification methods require near exact replication of the original imaging parameters, or extensive modeling in ...

    STTR Phase I 2013 Department of DefenseAir Force
  9. Inhibiting Prolyl Hydroxylase to Mimic Natural Acclimatization to High Altitude to Improve Warfighter Performance at High Altitude

    SBC: Research Logistics Company            Topic: SOCOM17C001

    Acclimatization is the long-term adjustment that humans experience when exposed for weeks or months to high altitude. Acclimatization is important in this context because a warfighter who is acclimatized to high altitude is immune to high altitude illness, has superior work capacity, and has cognitive function approaching that found at sea level. In other words, the acclimatized warfighter is opti ...

    STTR Phase I 2018 Department of DefenseSpecial Operations Command
  10. Virtual Reality for Multi-INT Deep Learning (VR-MDL)

    SBC: INFORMATION SYSTEMS LABORATORIES INC            Topic: AF19AT010

    Recent advances and successes of deep learning neural networks (DLNN) techniques and architectures have been well publicized over the last several years. Voluminous, high-quality and annotated training data, or trial and error in a realistic environment, is required to achieve the promised performance potential of DLNNs. Unfortunately for DoD and/or Intelligence Community (IC) applications of mult ...

    STTR Phase I 2019 Department of DefenseAir Force
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