Prognostic ASIC and IC set for Process-Related Integrated Circuits (IC))

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-06-C-0208
Agency Tracking Number: N061-006-0732
Amount: $149,755.00
Phase: Phase I
Program: SBIR
Awards Year: 2006
Solicitation Year: 2006
Solicitation Topic Code: N06-006
Solicitation Number: 2006.1
Small Business Information
MANAGEMENT SCIENCES, INC.
6022 Constitution Avenue NE, Albuquerque, NM, 87110
DUNS: 094142122
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 Kenneth Blemel
 Vice President
 (505) 255-8611
 kenneth_blemel@mgtsciences.com
Business Contact
 Marlene Blemel
Title: President
Phone: (505) 255-8611
Email: kay_blemel@mgtsciences.com
Research Institution
N/A
Abstract
MSI has developed and tested a Prognostic Health Management (PHM) microchip module that is used for monitoring electrical systems. We propose new research to determine the feasibility of developing a prognostic system based on a self-test IC, “a system on a substrate”. Our research will explore and define a set of web-based cognitive fusion algorithms that will provide a model based formalism that determines the expended IC life in the host in which the IC is embedded. Among the algorithms we propose to develop is a self learning cognitive algorithm that continuously improves based on field histories to provide the system’s capability to maximize PHM functionality. The IC will provide a revolutionary advanced state-of-the-art in Prognostic Health Management (PHM) of ICs by moving the PHM architecture from the circuit card level to the integrated circuit level, thus reducing or eliminating ancillary components dedicated to PHM functions on circuit cards. In Phase I our research will define the requirements and create a virtual prototype that will be fully developed in Phase II. We will demonstrate the model’s prognostic ability.BENEFITS: These advanced models would be applicable to any electronic system across the defense and commercial industries that aspire to apply diagnostic, prognostic, and health management capabilities. Any results and understanding gained from applying these failure progression rate models will provide a significant crossover benefit to a multitude of similar applications.

* information listed above is at the time of submission.

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