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Award Data
The Award database is continually updated throughout the year. As a result, data for FY22 is not expected to be complete until September, 2023.
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.
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Proactive Risk Monitoring Using Predictive Analytics
SBC: ARCTOS Technology Solutions, LLC Topic: MDA16T002In a pilot study effort in 2013, the Secretary of Defense for Manufacturing and Industrial Base Policy (ODASD (MIBP)) developed a methodology that goes beyond legacy reactive and program-centric frameworks for assessing industrial base risk. This proposed effort leverages the pilot studys work with the fragility and criticality (FaC) assessment to develop a predictive and proactive tool to assist ...
STTR Phase I 2017 Department of DefenseMissile Defense Agency -
Carbon nanotube coatings on electrochemical textured surfaces for advanced adsorptive baffles
SBC: Faraday Technology, Inc. Topic: MDA18T003This proposed STTR program addresses the challenge of developing advanced absorptive baffles to minimize stray and reflected light across the visible and infrared wavebands for exo-atmospheric optical sensors and seeker telescopes. To achieve this goal Faraday Technology and Pacific Northwest National Laboratory will develop electrochemically textured pyramidal surfaces with CNT black coatings as ...
STTR Phase I 2019 Department of DefenseMissile Defense Agency -
Additive Manufacturing of Metallic Materials for High Strain Rate Applications
SBC: MRL MATERIALS RESOURCES LLC Topic: MDA17T001Metallic additive manufacturing (AM) is an attractive technology for the production of lethality test articles due to the potential for significantly reduced lead time and manufacturing cost.However, in order to be effective in providing accurate lethality data, the properties of the AM material have to match closely the properties of conventionally manufactured alloys found in real threat targets ...
STTR Phase I 2018 Department of DefenseMissile Defense Agency -
SmallSat Stirling Cryocooler for Missile Defense (SSC-X)
SBC: WECOSO, INC. Topic: MDA17T003West Coast Solutions (WCS), in collaboration with the Georgia Institute of Technology and Creare LLC, proposes an adaptation of our SmallSat Stirling Cryocooler (SSC) technology in response to STTR Topic MDA17-T003: High-Efficiency, Low-Volume, Space-Qualified Cryogenic-Coolers. In Phase 1 we will scale up a design currently in development for NASA to meet the Missile Defense Agency (MDA) topic re ...
STTR Phase I 2018 Department of DefenseMissile Defense Agency -
Producibility of Gallium Nitride Semiconductor Materials
SBC: Inlustra Technologies LLC Topic: MDA09T001Inlustra Technologies and the University of Notre Dame propose a Phase I STTR program that, combined with a subsequent Phase II effort, will result in methods for the scalable production of semi-insulating non-polar GaN substrates. These substrates will be utilized in the fabrication of high-power/high-frequency AlGaN-GaN electronic devices capable of reliable operation under high thermal load. ...
STTR Phase I 2010 Department of DefenseMissile Defense Agency -
An Ultra-High Temperature Ceramic with Improved Fracture Toughness and Oxidation Resistance
SBC: Plasma Processes, LLC Topic: MDA09T002Hypersonic missile defense systems are being designed to reach global threats. During flight, external surfaces are predicted to reach temperatures in excess of 2200C. As a result, innovative, high performance thermal protection systems (TPS) are of great demand. Among ultra-high temperature ceramics (UHTC), it is well known that ZrB2- and HfB2-based materials have high melting temperatures and ...
STTR Phase I 2010 Department of DefenseMissile Defense Agency -
Contamination-free, Ultra-rapid Reactive Chemical Mechanical Polishing (RCMP) of GaN substrates
SBC: Sinmat Inc Topic: MDA09T001Gallium Nitride (GaN) substrates are ideal materials for fabrication of high-power and high-frequency devices based on III-V materials. The current state-of-the-art Chemical Mechanical Polishing (CMP) methods are plagued by several challenges, including, surface charge affects due to surface contamination, and sub-surface damages, which can limit the quality of III-V devices. Furthermore, there is ...
STTR Phase I 2010 Department of DefenseMissile Defense Agency -
Software Defined Multi-Channel Radar Receivers for X-band Radars
SBC: DGNSS Solutions, LLC Topic: MDA09T003The primary objective of the proposed research is to develop proof of concept of a software programmable X-Band radar system using low cost antenna array technology with digital beamforming architecture based on multiple receiver channels. The performance objectives will aim at a minimum of 400 MHz instantaneous bandwidth and a minimum instantaneous dynamic range of 52 dB. The objective of the t ...
STTR Phase I 2010 Department of DefenseMissile Defense Agency -
Efficient Clutter Suppression and Nonlinear Filtering Techniques for Tracking Dim Closely Spaced Objects in the Presence of Debris
SBC: TOYON RESEARCH CORPORATION Topic: MDA12T004EO/IR elements of the Ballistic Missile Defense System (BMDS) responsible for detecting and tracking ballistic missile threats encounter extraordinarily challenging threat and scene phenomenology. Specifically, non-stationary clutter characteristic of airborne and satellite-based sensor systems, along with dim target signatures, closely-spaced objects, and dense debris clouds typical of ballistic ...
STTR Phase I 2013 Department of DefenseMissile Defense Agency -
Hybrid DNN-based Transfer Learning and CNN-based Supervised Learning for Object Recognition in Multi-modal Infrared Imagery
SBC: TOYON RESEARCH CORPORATION Topic: 1On this effort Toyon Research Corp. and The Pennsylvania State University are developing deep learning-based algorithms for object recognition and new class discovery in look-down infrared (IR) imagery. Our approach involves the development of a hybrid classifier that exploits both transfer learning and semi-supervised paradigms in order to maintain good generalization accuracy, especially when li ...
STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency