A Predictive Prognostic Model for Field Effect Transistors (FET) Navy SBIR FY2006.1 Sol No.: Navy SBIR FY2006.1 Topic No.: N06-007 Topic Title: A Predictive Prognostic Model for Field Effect Transistors (FET) Proposal No.: N061-007-0207 Firm: American Systems Technology Incorporated 888 West Big Beaver Road Suite #800 Troy, Michigan 48084 Contact: Michael Scherrer Phone: (248) 362-4100 Web Site: www.amsystech.com Abstract: With the increased demand for higher power and temperature electronics in more-electric aircraft, the health monitoring of FETs has become critical to overall aircraft safety. Current prognostic techniques that utilize analytical models and historical trends have not been adequate to accurately predict the remaining useful life of FETs. The Joint Strike Fighter's (JSF) prognostic health management (PHM) program makes use of available measurement and failure statistics to predict Time-to-Failure (TTF). More accurate prognostics have potential to reduce JSF life cycle costs by decreasing the rate of false alarms, time to repair, and scheduled maintenance. American Systems Technology, Inc. (ASTI) proposes to develop a prognostic algorithm that can provide more accurate Time-to Failure predictions for FETs in aircraft power supplies. In addition to conventional threshold, leakage, and conductance parameters, ASTI proposes to utilize Time Domain Reflectometry (TDR), and other techniques that have potential to predict catastrophic failure well in advance. TDR techniques hold promise in the determination of aging information when components have failure modes that are not recognizable using conventional techniques. Our research approach will be useful for future PHM and electronics subsystem design tools which should enable greater design resiliency, and minimal redundancy for aircraft FET applications. Benefits: Improvements in prognostic model precision to detect and predict FET aging and failure will benefit more-electric Joint Strike Fighter, UAV, and Army vehicle platforms by improving cost-effectiveness, mission readiness, and safety. Likewise, the safety and reliability of hybrid-electric vehicles in the automotive market can be enhanced through improved predictive FET prognostics. Return