An agile accelerated aging, characterization and scenario simulation system for gate controlled power transistors

Abstract

To advance the field of electronics prognostics, the study of transistor fault modes and their precursors is essential. This paper reports on a platform for the aging, characterization, and scenario simulation of gate controlled power transistors. The platform supports thermal cycling, dielectric over-voltage, acute/chronic thermal stress, current overstress and application specific scenario simulation. In addition, the platform supports insitu transistor state monitoring, including measurements of the steady-state voltages and currents, measurements of electrical transient response, measurement of thermal transients, and extrapolated semiconductor impedances, all conducted at varying gate and drain voltage levels. The aging and characterization platform consists of an acquisition and aging hardware system, an agile software architecture for experiment control and a collection of industry developed test equipment.

Probabilistic Monte-Carlo Method for Modelling and Prediction of Electronics Component Life

“…Prevalently, four kinds of accelerate aging methods are widely used in accelerated aging experiments, which are thermal cycling, hot carrier injection, electrical over stress and time dependent dielectric breakdown stimulus [12]. The IGBT functional failure such as die solder degradation and wire lift were brought by the thermal cycling accelerate aging approach.…”

“…IGBT condition mutation and lighting could be caused by the electrical overstress due to the excessive voltage, current or power. The breakdown of IGBT gate oxide will happen when the charge injection exceeds the threshold which is caused by accumulating of the temperature in the gate oxide when it is being operated [12]. Accelerated aging approaches such as thermal cycling and electrical overstress are used in IGBT accelerate aging experiments to speed up the degradation and failure of the IGBT in experiments environments which simulate the scenarios of industrial practical application.…”

“…In this paper, the prediction (or prognostic) error is defined by (12) where E r is the error value between the predicted and real values, RUL R is the real RUL value of an IGBT and RUL P is the predicted value obtained from the prognostic algorithm. Using equation (12), the prognostic accuracy can be quantitatively calculated using (13) Fig. 9.…”

Towards prognostics for electronics components

“…These 1 1 U.S. Government work not protected by U.S. copyright. 2 IEEEAC paper # 1337, Version 2, Updated January 9, 2009. components form the backbone of avionics systems that play an ever-increasing critical role in on-board, autonomous functions for vehicle controls, communications, navigation, and radar systems.…”

Accelerated aging system for prognostics of power semiconductor devices

“…Research interest in this area has increased in recent years and the notion that electronic devices fail without any previous indication of damage progression has changed as the research community continues to identify precursors of failure in a variety of electronic components. Current research efforts in prognostics for these power devices focuses on: a) identification of the failure modes and mechanisms [1,2]; b) identification of precursors of failure which serve as leading indicators of failure and play an essential role in the prediction of RUL [1][2][3]; c) development of accelerated aging methodologies and systems to accelerate the aging process of test devices by accelerating the aging process while continuously measuring key electrical and thermal parameters [1,3,4]; d) development of physics based degradation models to enable model-based prognostics [3]; and e) development of remaining life prediction algorithms with proper understanding and modeling of the different sources of uncertainty affecting the RUL estimate [5]. This paper describes an accelerated aging system that allows for the understanding of the effects of failure mechanisms and the identification of leading indicators of failure which are essential in the development of physics-based degradation models and prediction of remaining useful life.…”