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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.

  1. Portable Ruggedized Energy Efficient Medical Sterilizer

    SBC: LYNNTECH INC.            Topic: N182096

    Current field sterilizers are large, power-intensive systems that present a logistical burden to forward-deployed resuscitative surgical facilities. There is a need for a low-cost, person-transportable medical sterilizer capable of sterilizing small surgical tools and trays in a Role 1-2 tactical environment. This Phase I SBIR will design and test a breadboard sterilization system that demonstrate ...

    SBIR Phase I 2019 Department of DefenseNavy
  2. Weather Avoidance Sensor Systems for Unmanned Aerial Systems

    SBC: RDRTEC INCORPORATED            Topic: N182099

    RDRTec with Raytheon as a subcontractor and transition partner proposes to design, develop, and demonstrate feasibility of enhanced approaches to unmanned aircraft weather avoidance using a combination of organic weather radar and electro-optics. The goal of this effort is to provide increased safety against weather hazards while minimizing unnecessary deviations by using visual cues and measurabl ...

    SBIR Phase I 2019 Department of DefenseNavy
  3. Anti-Reflective Surface Structures for Missile Domes

    SBC: NANOHMICS INC            Topic: N182105

    Next-generation infrared (IR) seekers will have an aerodynamically-engineered nose cone geometry with a significantly higher degree of curvature compared to conventional hemispherical domes. To prevent unwanted specular + diffuse reflections that obscure the IR focal plane array, the interior of the dome should ideally be treated with an anti-reflective (AR) layer that suppresses stray light refle ...

    SBIR Phase I 2019 Department of DefenseNavy
  4. Monolithic Beam Steerer for High Power Mid-infrared Quantum Cascade Lasers

    SBC: OMEGA OPTICS, INC.            Topic: N182109

    In this SBIR program, Omega Optics, University of Texas, Austin and Adtech Photonics propose a complete monolithic epitaxial device, with no wafer bonding, for surface emitting 2D non-mechanical optical beam steering at 4.6 micron wavelength. Two methods will be investigated: first, with slant etch normal emitters for azimuthal and elevation steering at a single wavelength using thermal phase shif ...

    SBIR Phase I 2019 Department of DefenseNavy
  5. Monolithic high-power QCL-based photonic integrated circuits for two-dimensional beam steering

    SBC: TRANSWAVE PHOTONICS, LLC            Topic: N182109

    TransWave Photonics, LLC in collaboration with the University of Texas at Austin, proposes to demonstrate a mid-infrared 2-dimensional beam-steering photonic integrated circuit (PIC) with a passive InGaAs/InP platform where high performance mid infrared quantum cascade laser sources, low-loss InGaAs waveguides, and beam steering photonic elements are monolithically integrated. Low mid-infrared opt ...

    SBIR Phase I 2019 Department of DefenseNavy
  6. Monolithic Quantum Cascade Laser-based Platform with Electronically Controlled Output Beam Steering

    SBC: IRGLARE LLC            Topic: N182109

    In response to the NAVY SBIR solicitation N182-109, the IRGLARE team proposes the development of a 4.6µm Quantum Cascade Laser (QCL) source delivering >1W of continuous wave (CW) optical power with monolithically integrated 2D beam steering mechanism. The steering angle of the output beam will be electronically controllable in the range of ±10 and ±25 degrees in the two planes normal to the dev ...

    SBIR Phase I 2019 Department of DefenseNavy
  7. Reliable 5W Continuous Wave Quantum Cascade Lasers with a Nearly Gaussian Circular Output Beam

    SBC: IRGLARE LLC            Topic: N182113

    In response to the NAVY SBIR solicitation N182-113, the IRGLARE team is pleased to propose the development of highly reliable 4.5µm Quantum Cascade Lasers (QCLs) delivering over 5W of continuous wave optical power in a nearly gaussian beam (M2 < 1.5) with a low ellipticity. The combination of the high optical power and long term reliability will be achieved by over a two-fold reduction in laser t ...

    SBIR Phase I 2019 Department of DefenseNavy
  8. Mid-infrared quantum cascade lasers with highly efficient heat extraction architecture

    SBC: TRANSWAVE PHOTONICS, LLC            Topic: N182113

    TransWave Photonics, LLC in collaboration with the University of Texas at Austin proposes to demonstrate a new active region and thermal packaging configuration for the mid-wave infrared quantum cascade lasers that provide both high modal overlap with the laser active region and a dramatically reduced thermal impedance of the laser compared to the current state-of-the-art. The proposed laser desig ...

    SBIR Phase I 2019 Department of DefenseNavy
  9. Adaptive Interference Rejection Algorithms

    SBC: Applied Research In Acoustics LLC            Topic: N182116

    To address the need to mitigate cooperative and noncooperative in-band interference in airborne anti-submarine warfare (ASW) systems, ARiA will develop and demonstrate the feasibility of signal and information processing algorithms for mitigating various types of in-band interference. ARiA will develop and evaluate (1) model-based adaptive signal processing in space, time, and Doppler to mitigate ...

    SBIR Phase I 2019 Department of DefenseNavy
  10. Fooling Computer Vision Classifiers with Adversarial Examples

    SBC: LYNNTECH INC.            Topic: N182127

    The Lynntech team proposes to develop a Computer Vision FoolKit system that integrates cutting-edge approaches to systematically evaluate physically realizable adversarial attacks against several leading computer vision classifiers. It has been noted that most deep neural networks are demonstrably vulnerable to adversarial examples, even in the form of small-magnitude changes in intensities of the ...

    SBIR Phase I 2019 Department of DefenseNavy
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