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Award Data
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.
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Development of a Dynamic SCA 4.1 Waveform Compliance Test Platform
SBC: Reservoir Labs, Inc. Topic: AF06067The Software Communications Architecture (SCA) 4.1 is emerging as the worldwide standard for tactical software-defined radio waveform development. The lack of automated test tools targeted to the SCA is a limiting factor in the certification cycle, resulting in longer turnaround times at JTEL, which, in turn, jeopardizes the timely introduction of required waveforms and upgrades. Due to the scope ...
SBIR Phase II 2020 Department of DefenseNavy -
Advanced Manufacturing and Material Measurements Software Tool WEAVE for the Accelerating and Automation of SEM image analysis in the Semiconductor Industry
SBC: Sandbox Semiconductor Incorporated Topic: 90The goal of this proposal is to create the software Weave™ to automate the extraction of critical dimensions (CDs) from scanning electron microscope (SEM) images for the microelectronics industry. Current best practices for extraction of CDs are that personnel analyze the images one by one, which is tedious, prone to human bias, time-consuming and expensive. Successful implementation of Weave™ ...
SBIR Phase II 2020 Department of CommerceNational Institute of Standards and Technology -
An Optical Imaging System to Characterize Mechanical Deformation at Microscopic Length Scale
SBC: ADDITIVE MANUFACTURING INNOVATIONS LLC Topic: 90Additive Manufacturing Innovations LLC (AM-Innov) in collaboration with Clarkson University and Naval Research Laboratory (NRL) proposes a new optical imaging system to characterize microscopic deformation of materials. The system, named as a Mechanical Testing at Microscale system (MT@micro), uses a micro-tensile testing device to mechanically load the specimen uniaxially and an optical imaging p ...
SBIR Phase I 2020 Department of CommerceNational Institute of Standards and Technology -
Watertight CAD for Integrating Isogeometric Analysis into the Model-Based Enterprise
SBC: Nvariate, Inc. Topic: 90Today’s Computer-Aided Design (CAD) applications utilize restrictive mathematical assumptions to approximate the compound geometric intersections necessary to represent real-world products. As a result, critical information is not represented within the Model-Based Definition (MBD) for downstream users in the digital thread, forcing engineers to manually repair CAD models and convert them into d ...
SBIR Phase II 2020 Department of CommerceNational Institute of Standards and Technology -
Standardizing Grease Sampling and Characterization By Automated Online Device
SBC: POSEIDON SYSTEMS, LLC Topic: 90Scheduled grease sampling is one of the more reliable methods for detecting mechanical issues within machinery. Offline laboratory‐based analysis of the grease can indicate component failures, such as spalling via high concentrations of ferrous particulates. However, consistency, costs, and frequency of sampling are less than ideal. Manually collecting grease samples can put personnel in danger ...
SBIR Phase I 2020 Department of CommerceNational Institute of Standards and Technology -
High Throughput, High-Pressure Small-Angle Neutron Scattering Sample Environment
SBC: STF TECHNOLOGIES LLC Topic: 90We address a need of the neutron user community by creating a minimum viable prototype of the only high-throughput hydrostatic pressure small angle neutron scattering sample environment (HTHP-SANS-SE). Our HTHP-SANS-SE will greatly improve the ease of use and reliability of measurements under extreme environments, thereby increasing throughput on the beamline and expanding feasibility. Current HP- ...
SBIR Phase I 2020 Department of CommerceNational Institute of Standards and Technology -
(3) Predictive Analytics for LPD-17 SAN ANTONIO Class MPDE
SBC: SparkCognition, Inc. Topic: N193A01SparkCognition, an artificial intelligence and machine learning company, can provide a system capable of analyzing data related to operations, maintenance and sustainment for twelve (12) LPD-17 SAN ANTONIO class main propulsion diesel engines. SparkCognition’s SparkPredict is a turnkey solution that analyzes sensor data and uses machine learning to return actionable insights, flagging suboptimal ...
SBIR Phase I 2020 Department of DefenseNavy -
(6) Applying AI/ML Methods to Streamline Software Development
SBC: TCG, Inc. Topic: N193A01Navy seeks to incorporate AI/ML to offload tedious cognitive or physical tasks. Our innovation is self-coding software that reacts to business changes as they happen at low effort and cost. It enables code reuse across the enterprise by referencing code from a common database, so any change can be immediately applied across all applications where it is used. We propose combining tools and techniqu ...
SBIR Phase I 2020 Department of DefenseNavy -
3- Artificial General Intelligence for Optimal Predictive Maintenance
SBC: INTELLIGENT ARTIFACTS INC. Topic: N193A01For the current SBIR Phase I application, the Navy is looking for novel solutions to predict and mitigate the failure of critical parts. Intelligent Artifacts’Artificial General Intelligence (AGI) software solution is a bottom-up analytical approach that learns continuously from the un-modeled data in real-time, and allows for complete explainability in both the functions used and in pointers to ...
SBIR Phase I 2020 Department of DefenseNavy -
(4) Deep Defense of Distributed Deep Learning (D4L)
SBC: EH GROUP INC Topic: N193A01This project aims to achieve trustworthy deep learning (TDL)-based distributed computation among wireless connected Naval devices (sensors, UAVs, mobile phones, ships, etc.) through the seamless integration of cryptography, a complimentary classifier, and communications (C4). Distributed Deep Learning (DDL) runs in a distributed Naval network setting and is vulnerable to attacks that have knowledg ...
SBIR Phase I 2020 Department of DefenseNavy