Microelectronics Reliability

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1 Microelectronics Reliability 52 (2012) Contents lists available at SciVerse ScienceDirect Microelectronics Reliability journal homepage: A prognostic approach for non-punch through and field stop IGBTs Nishad Patil a, Diganta Das a, Michael Pecht a,b, a Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, United States b Centre for Prognostics and System Health Management, City University of Hong Kong, Kowloon, Hong Kong article info abstract Article history: Received 1 March 2011 Received in revised form 13 July 2011 Accepted 19 October 2011 Available online 21 November 2011 Development of prognostic approaches for insulated gate bipolar transistors (IGBTs) is of interest in order to improve availability, reduce downtime, and prevent failures of power electronics. In this study, a prognostic approach was developed to identify anomalous behavior in non-punch through (NPT) and field stop (FS) IGBTs and predict their remaining useful life. NPT and FS IGBTs were subjected to electrical thermal stresses until their failure. X-ray analysis performed before and after the stress tests revealed degradation in the die attach. The gate emitter voltage (V GE ), collector emitter voltage (V CE ), collector emitter current (I CE ), and case temperature were monitored in situ during the experiment. The on-state collector emitter voltage (V CE(ON) ) increased and the on-state collector emitter current (I CE(ON) ) decreased during the test. A Mahalanobis distance (MD) approach was implemented using the V CE(ON) and I CE(ON) parameters for anomaly detection. Upon anomaly detection, the particle filter algorithm was triggered to predict the remaining useful life of the IGBT. The system model for the particle filter was obtained by a least squares regression of the V CE(ON) at the mean test temperature. The failure threshold was defined as a 20% increase in V CE(ON). The particle filter approach, developed using the system model based on the V CE(ON), was demonstrated to provide mean time to failure estimates of IGBT remaining useful life with an error of approximately 20% at the time of anomaly detection. Ó 2011 Elsevier Ltd. All rights reserved. 1. Introduction Insulated gate bipolar transistors (IGBTs) are the devices of choice for medium and high power, low frequency applications such as traction motors, high power switch mode power supplies, and variable speed drives [1]. Development of diagnostic and prognostic approaches has been motivated by reports of IGBT failures [2,3]. There have been several studies reported on IGBT anomaly detection. Xiong et al. [4] proposed an online diagnostic system to predict the failure of an automotive IGBT power module using a look-up table for the collector emitter voltage. Ginart et al. [5] developed an online ringing characterization technique to diagnose IGBT faults in power drives. Oukaour et al. [6] developed an approach to determine defective IGBTs from healthy IGBTs using neural nets. Lu et al. [7] used a physics-based strain model to determine the remaining life of an IGBT module. Saha et al. [8] implemented the particle filter algorithm for the prediction of the remaining useful life of a punch through (PT) IGBT, using the trend of the collector emitter current at turn-off. Although several approaches have been investigated for specific applications, there is a need for a comprehensive approach that Corresponding author at: Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, United States. address: pecht@calce.umd.edu (M. Pecht). enables both the detection of anomalous behavior and the prediction of remaining useful life (RUL). In this study, a prognostic framework for IGBTs is proposed and implemented. This framework involves the use of the Mahalanobis distance to detect anomalies in the IGBT and the particle filter algorithm to predict RUL. Non-punch through (NPT) and field stop (FS) IGBTs were subjected to electrical thermal stress by power cycling and several device and load parameters were monitored. Features from the monitored data were used to compute the Mahalanobis distance (MD). The MD was transformed by normalization using Box Cox transformation and used with an appropriate threshold to detect anomalous behavior. Upon anomaly detection, the particle filter algorithm was implemented to predict the remaining useful life. This paper is organized in five sections. Following this introductory section; in Section 2, the experimental approach and testing procedure are described. In Section 3, the prognostic framework is discussed. In Section 4, the results obtained by implementation of the prognostics framework are presented. In Section 5, the results of this study are summarized and future work is discussed. 2. Experimental approach and degradation analysis Electrical thermal stress tests were performed on 600 V rated non-punch through (NPT) and field stop (FS) IGBTs manufactured by International Rectifier. The experimental setup used for power cycling of IGBTs under a resistive load is shown in Fig /$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi: /j.microrel

2 N. Patil et al. / Microelectronics Reliability 52 (2012) NPT FS 6 V CE(ON) (A) Fig. 1. Experimental setup for power cycling of IGBTs Time (Hours) Fig. 4. V CE(ON) measured at 150 C for NPT and FS IGBT. T max T min P on Temperature ( o C) Power The experimental setup consists of a gate driver board, a main board, and a power conditioner board. The main board houses the terminal block for the IGBT device and BNC output ports connected to the terminal block, one port each for the gate emitter voltage (V GE ), collector emitter voltage (V CE ), and collector emitter current (I CE ) signals, which are measured using the oscilloscope. The main board also houses a 200 KHz Hall-effect current sensor with a 100 A maximum current rating to measure the I CE. A detailed schematic of the setup is provided in [13]. The electrical thermal stress tests were performed on the IGBTs using a 50% duty cycle, with a gate voltage of 15 V at a frequency of 1 KHz and a collector emitter voltage of 9 V. The devices were cycled between the case temperatures T min = 100 C and T max = 200 C, with a mean temperature of 150 C. Temperature monitoring was performed using a T-type thermocouple attached to the heat-sink of the TO-220A package. The devices under test were repeatedly switched on and off until the maximum temperature T max was reached. When the maximum temperature was attained, the device switching was stopped until the temperature dropped to T min. Then, switching was resumed again. The stress profile is illustrated by the schematic shown in Fig. 2. This process was continued until device failure. The IGBT failure modes observed during the test were latch-up and failure to turn-on. The failure times ranged from 12 h to 30 h. One example of a latch-up failure mode observed for an NPT IGBT is shown in Fig. 3, where latch-up 1 initiated after 27 h of the thermal electrical stress test as seen by the increase in collector emitter current. The on-state collector emitter voltage (V CE(ON) ) increased for both types of IGBTs, as shown in Fig. 4. X-ray analysis of the IGBTs was performed before and after the tests to determine the degradation in the die attach. The X-ray images of an NPT IGBT are shown in Fig. 5 and an FS IGBT is shown in Fig. 6. In the X-ray images, the larger die attach corresponds to the IGBT, and the smaller die attach to the free-wheeling diode located in the same package. Die attach degradation was observed in all the IGBTs tested. The degraded die attach resulted in increased thermal impedance leading to higher device temperatures. This resulted in latch-up initiation in some of the IGBTs tested. Some of the IGBTs did not latch-up, but failed as a result of increased resistance, which prevented them from turning on. P off I CE (A) Time Fig. 2. Schematic of temperature and switching test profile Time (Hours) Fig. 3. Latch-up of NPT IGBT. 3. Prognostics framework The prognostics framework developed and implemented in this study is shown in Fig. 7. This framework involves the measurement of I CE(ON) and V CE(ON) at a constant temperature using appropriate current and voltage sensors. Mahalanobis distance (MD) is used as a diagnostic parameter with a defined trigger for detection of an anomaly. The MD calculation is made using the V CE(ON) and the I CE(ON). Once an anomaly is detected by the MD approach, the particle filter (PF) process is initiated for time to failure prediction. The particle filter approach implemented in this study uses the V CE(ON) parameter measured at a constant temperature for failure prediction. Although this parameter cannot predict which failure mode (i.e., latch-up or failure to turn-on) is going to occur, it does give an indication of the health of the die attach and wire bonds, as 1 The power supply connected to the collector emitter terminals was cut-off when latch-up was initiated, preventing burn out of the IGBT and allowing further electrical testing.

3 484 N. Patil et al. / Microelectronics Reliability 52 (2012) Fig. 5. Before (left) and after (right) X-ray images of NPT IGBT. Fig. 6. Before (left) and after (right) X-ray images of FS IGBT. Current and Voltage Sensors Extract I CE(ON) and V CE(ON) (Constant Temperature) V CE(ON) Healthy MD Test MD Yes Detection Threshold Anomaly Detected? Particle Filter State Estimation and Tracking State Estimate (for time to failure prediction) Particle Filter Prediction Time to Failure Prediction Failure Threshold Fig. 7. Prognostics framework for IGBTs. degradation in these interfaces is correlated to changes in the V CE(ON) through changes in thermal resistance. In operation, the IGBT undergoes thermal cycling as a result of the devices being switched on and off. These thermal cycles lead to degradation in the solder die attach and wire bonds. This degradation leads to an increase in the device resistance, which causes a change in the V CE(ON). By measuring the V CE(ON) at a fixed gate voltage, collector voltage, and temperature, changes occurring as a result of device degradation are captured. The failure threshold in this approach is selected to be a fixed percentage change in V CE(ON) from its initial value. This failure threshold is general and is applicable to any other IGBT as it is based on the initial V CE(ON) of the device under test Anomaly detection Our approach to anomaly detection is to distinguish between healthy and anomalous data using the distance measure, MD. Monitored data that is known to be healthy is used to calculate the mean and standard deviation for data normalization. This healthy data is also used to compute the correlation matrix. With the mean, standard deviation, and correlation matrix obtained from the healthy data, the MD is calculated for every test data point. The MD values calculated from the healthy data are transformed, using the Box Cox power transformation, into a normal distribution. A detection threshold is calculated based on the mean and standard deviation of the transformed healthy MD data. The calculations are then repeated for every test data point using the mean, standard deviation, correlation matrix, and Box Cox transformation parameter learned from the healthy data. Using this approach, anomalies are detected when the transformed MD for a test data point crosses a detection threshold [9]. The MD approach was used for anomaly detection of NPT and FS IGBTs. MD was calculated using V CE(ON) and I CE(ON) parameters, and a threshold was defined to detect anomalies. To implement the MD approach, V CE(ON) and I CE(ON) data at the mean aging temperature

4 N. Patil et al. / Microelectronics Reliability 52 (2012) were partitioned into healthy data and test data. The initial observations (the first hour of the test) were classified as healthy data. The entire set of observations was used as test data. The parameters that form the input for MD computation are denoted by i, where i =1,2,...,p. In our study, V CE(ON) and I CE(ON) were used as input parameters, hence, p = 2. The number of observations recorded for each parameter is denoted by j, where j =1,2,...,n. X ij denotes the value of parameter i at time instance j. Each individual observation of a given parameter in the data vector was normalized using the mean and standard deviation for the parameter, given by Eq. (1). The mean and standard deviation for the input parameters was computed by using Eq. (2) from the healthy data. Z ij ¼ ðx ij X i Þ S i X i ¼ 1 n X n j¼1 X ij ; S i ¼ s P ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n j¼1 ðx ij X i Þ 2 ðn 1Þ The healthy MD values were computed by using Eq. (3), with the normalized parameters obtained from Eq. (2), where Z j is the normalized I CE(ON) and V CE(ON) at time j. MD j ¼ 1 p ZT j C 1 Z j where C is the correlation matrix. Eq. (4) was used to calculate the correlation matrix from the healthy data. 1 C ¼ ðn 1Þ X n j¼1 Z j Z T j To compute the test MD values, the mean and standard deviation obtained from the healthy MD computation was used to normalize the test I CE(ON) and V CE(ON). These normalized test parameters were then used to compute the test MD by Eq. (3), where the correlation matrix was obtained from the healthy data. The healthy MD values obtained were found to not follow a normal distribution. The Box Cox power transformation was used to transform the healthy MD values into a normal distribution. Transforming the healthy MD data into a normal distribution allows for the use of statistical process control rules to determine if the test data from the device is healthy or unhealthy. The Box Cox transformation is defined by Eq. (5), where x(k) is the transformed vector, x is the original vector, and k is the transform parameter [10]. xðkþ ¼ ðxk 1Þ k 0 k xðkþ ¼lnðxÞ k ¼ 0 The mean (l) and standard deviation (r) of the transformed healthy MD values were used to obtain 3r bounds about the mean. The upper bound (l +3r) was used as the threshold for anomaly detection, as increasing MD values indicate degradation in the IGBT with the parameters getting further away from the characteristics of the healthy data. The test MD values were transformed using Eq. (5), based on the Box Cox transform parameter k learned from the healthy data. When a transformed test MD value crossed the threshold, an anomaly was considered to have occurred Particle filter prediction ð1þ ð2þ ð3þ ð4þ ð5þ The particle filter (PF) approach is a sequential Monte Carlo method that has been used extensively in robotics, automation, and artificial intelligence applications. It is a non-parametric approach to implementing the Bayes filter for state estimation. Estimation of dynamic states using system models and measurements is based on probabilistic laws that exercise the use of conditional independence and the Markov property. The key principle behind particle filters is that they represent distributions by a set of samples drawn from the distribution, therefore making the method non-parametric. This means that the representations are not limited to Gaussian distributions and can be used to represent other distributions, including multi-modal distributions. Another advantage provided by the particle filter s sample-based representation is that it can model non-linear evolutions of the system state, in addition to linear state models. The PF approach is a solution to the non-linear Bayesian tracking problem. The objective of tracking is to estimate the evolution of the system state x k, defined as the state at time k based on control inputs u 1:k and measurements z 1:k, which are the sets of inputs and measurements obtained from time 1 to k. In order to analyze the system, a model describing the evolution of the system state with time (and control inputs, if present) and a model relating the measurements to the system state, known as the system model and the measurement model respectively, are required in a probabilistic state-space formulation. The system state at time k is given by Eq. (6), where f k is the system model, which is a function of the previous state, time, and any control inputs, as well as x k, which is the system noise. x k ¼ f k ðx k 1 ; x k 1 Þ The measurement at time k is related to the system state using the measurement equation shown in Eq. (7), where h k is the measurement model and m k is the measurement noise. z k ¼ h k ðx k ; m k Þ The PF implementation consists of two recursive steps: prediction and update [11]. The prediction stage uses knowledge of the previous state estimate and the system model to predict the current state estimate, known as the prior probability density function (pdf) and denoted as p(x k x k 1 ). This is based on the Markov property that, conditioned on the current system state, the future state is independent of the past states of the system. The update step uses the latest measurement to modify the prior pdf, using the measurement model to obtain the updated system state, known as the posterior pdf, p(x k z k ). This is achieved using Bayes theorem. In the PF approach, the system state pdf is approximated by a set of particles representing sampled values from the prior or posterior distributions, and a set of associated weights denoting discrete probability masses. The particles are generated based on an initial assumption of the system state pdf (at time k = 0) and recursively estimated using the system model, measurement model, and a set of available measurements [12]. Prognostics using particle filters is implemented by predicting future particle states without any additional measurement information. This is achieved by employing the prediction step repeatedly without the update step and using each predicted state as the posterior from which to derive the state at the next instant in time. The prediction step is repeated until the predicted value of the parameter under consideration crosses the failure threshold. In this study, a particle filter approach was implemented for prognostics of NPT and FS IGBTs. As the particle filter requires models developed from known system behavior, as well as system measurements for state estimation, empirical system models were developed from the V CE(ON) data measured at the mean test temperature. To implement the particle filter, the initial state of the system was represented by a set of particles x i 0 where i = 1, 2,..., n is the particle index, and n is the number of particles. The particles were independently propagated using the system model, given by Eq. (8), which provides the samples that represent the prior pdf. x i k ¼ f kðx i k 1 ; x k 1Þ $px i k jxi k 1 ð8þ ð6þ ð7þ

5 486 N. Patil et al. / Microelectronics Reliability 52 (2012) The posterior pdf of each particle was then calculated using Eq. (10), by first updating the associated particle weights using the measurement model. The measurement model is the likelihood of measurement for each particle, as shown in the following equation: z i k ¼ h k x i k ; m k $ pzk jx i k ð9þ w i k ¼ wi k 1 pðz kjx i k Þ ð10þ After the weights were computed, they were normalized using the following equation:, w i k ¼ X n wi k w i k ð11þ i¼1 The posterior pdf was then obtained by: pðx k jz 1:k Þ Xn w i k K x k x i k h i¼1 ð12þ where K is the kernel function (Gaussian) and h is the bandwidth of the kernel function. One of the issues with the particle filter is particle degeneracy, where after a few iterations most of the particles will have negligible weights. To avoid the effects of particle degeneracy, resampling was performed periodically. Extent of particle degeneracy is evaluated by Eq. (13) through calculation of number of effective particles: ^n eff., X n ^n eff ¼ 1 ðw i k Þ2 ð13þ i¼1 If the number of effective particles calculated in Eq. (13) was less than a threshold value, resampling was performed. The resampling process involves sampling with replacement, wherein particles with low weights are replaced with a new set of particles drawn from the posterior pdf. The new set of particles is assigned uniform weights at the end of the resampling step, as shown in the following equation: n o x i k ; wi k () x i k ; 1=n ð14þ 4. Results and discussion Using the MD approach, anomalies were identified in the NPT and FS IGBTs. In Fig. 8, the anomaly detection results are shown for an NPT IGBT. The transformed MD threshold for anomaly detection was 2.7, the anomaly was detected at 4.4 h from the beginning of the test, and the time for parametric failure, which is the Transformed MD Transformed MD threshold Transformed MD crosses threshold Time (Hours) Fig. 8. Anomaly detection using MD. Fig. 9. Predicted failure distribution at the time of anomaly detection for an NPT IGBT. increase in V CE(ON) by 20% 2, was 13.7 h. Therefore, the MD approach in this case was able to detect anomalous behavior at a time 68% before parametric failure. Once the anomaly was detected, the PF algorithm was initiated to predict the time to parametric failure. The system model used for particle filter prediction was based on the on-state collector emitter voltage V CE(ON). A 2nd order least squares regression of the V CE(ON) parameter at 150 C was obtained from the electrical thermal stress tests performed on two IGBTs of each type. The coefficients obtained from the least squares regression for each device were averaged to obtain the coefficients of the system model for each type of IGBT. The system model from the regression is given by: V CEðONÞk ¼ V CEðONÞk 1 þ 2at k ðt k t k 1 Þþbðt k t k 1 Þþx k 1 ð15þ where a = and b = for NPT IGBTs and a = 0.006, b = 0.01 for FS IGBTs. The measurement model was the actual measured voltage given by: z k ¼ V CEðONÞk þ m k ð16þ To estimate the system noise, the system model was used to estimate the V CE(ON) for two IGBTs of each type, NPT and FS. For each of the estimates, the residuals were obtained by calculating the difference between the estimates from the system model and the data from the aging experiments. The standard deviation of the residuals was then used as the standard deviation of the system noise, which was assumed to be Gaussian. The standard deviation of the measurement noise was obtained based on the noise of the voltage measurements performed by the oscilloscope. The system model obtained from the aging data was used for RUL estimation of test IGBTs. The number of particles used was 30, and a threshold on number of effective samples for resampling was set to 10. For the purpose of RUL estimation, the system model was used with a 20% increase in the V CE(ON) as the failure threshold. In order to calculate the RUL from time of anomaly detection k, the prediction step of the particle filter was implemented without the weight update step. The predictions were calculated until the value of the predicted V CE(ON) increased above the failure threshold. The results of the particle filter prediction for an NPT IGBT are shown in Fig. 9. The particle filter algorithm prediction is shown at time t = 4.4 h, which is the time the MD approach identified an anomaly. The parametric failure occurred at 13.7 h. The mean predicted time to failure given by the particle filter was 10.6 h, which is an error in prediction of 22%. It is to be noted that if the particle filter is initiated early, the failure pdf is wide, as seen in Fig. 9. At the particle filter prediction step, say t p, the developed system 2 IGBT failure criteria are typically defined based on the change in V CE(ON), hence V CE(ON) parameter was selected for prediction by particle filters in this study.

6 N. Patil et al. / Microelectronics Reliability 52 (2012) Fig. 10. Predicted failure distribution at the time of anomaly detection for an FS IGBT. model is used to predict future states at times t p+1, t p+2 and so on until the predicted voltage crosses the failure threshold. After each prediction, the system noise adds up. Hence, a prediction performed in the initial stages for a given particle size will have a wide distribution. With more knowledge of the system and as the device approaches failure, the time to failure pdf narrows as a result of improved confidence in the prediction. In Fig. 10, the remaining useful life estimate obtained for an FS IGBT is shown. The anomaly was detected by the MD approach at 4.5 h, the mean predicted time to failure was 9.8 h, and the time to parametric failure was 12.5 h. Hence, the error in prediction at the time of anomaly detection was 21%. It is observed that the mean of the state estimates, denoted by particle mean in Figs. 9 and 10, tracks the measurements through the test of the IGBT until the detection of an anomaly by the MD approach. The RUL pdf is represented using a mixture of Gaussians of the particle distribution at the predicted failure time. 5. Summary and conclusions In this study, a prognostics framework for IGBTs was developed and implemented for IGBTs that uses the Mahalanobis distance approach for anomaly detection and particle filters for time to failure prediction. The particle filter approach, developed using the system model based on the V CE(ON), was demonstrated to provide estimates of IGBT remaining useful life with an error of approximately 20% at the time of anomaly detection. The MD based probabilistic threshold approach implemented in this work was able to detect anomalies in the IGBTs before either parametric failure or functional failure. It was determined that die attach degradation contributed to the changes observed in the precursor parameters with aging. The V CE(ON) and I CE(ON) parameters were found to be precursors to IGBT failure. The V CE(ON) parameter was used as precursor parameter at a constant temperature to develop the particle filter system model. The advantage of using the V CE(ON) is that the change in this parameter can be correlated to degradation in the IGBT. Analyzing the data at one constant temperature ensures that the changes in the precursor parameter are as a result of degradation in the device and not due to changes in the operating temperature. The failure threshold selected was a 20% change in the V CE(ON) from the initial measurement made at the same temperature. This approach was used to predict the failure of NPT and FS IGBTs at the time of anomaly detection. The prediction error was found to be approximately 20% for the IGBTs analyzed in this study. This approach can also be implemented using the I CE(ON) parameter, which would provide equivalent prediction results to the V CE(ON) and, as such, either parameter can be used for the particle filter prediction. We have shown that the proposed particle filter framework is able to detect and predict IGBT failures under test conditions but we propose further research to implement the framework in application conditions. The degradation trend of the V CE(ON) is technology dependent (i.e., it varies for punch through, non-punch through, and field stop IGBTs). Additionally, the trends will be different for different classes (e.g., power or voltage rating) of IGBTs within the same technology. Hence, to develop the system model, one would need to perform tests for each variation of IGBT. One solution to this problem is to begin the PF implementation using a system model developed from previous similar IGBTs and then update the system model parameters for each new class of IGBT as more information from the new IGBT is obtained. The model parameters will be updated by reducing the error obtained by the difference between the voltage predicted by the system model and the actual measured voltage of the new IGBT. Since the system model is developed for a fixed temperature, the V CE(ON) will need to be measured in the application at the temperature used to obtain the system model. One method of resolving this issue would be to determine a temperature dependent correction parameter for the V CE(ON) system model to account for changes in temperature. The results from this study will help IGBT and IGBT module manufacturers and IGBT users in power systems to build in necessary circuits and sensors to monitor the identified precursor parameters. The IGBT modules of future can include the sensors and on-board processing algorithms for individualized assessment of anomaly thresholds and estimation of prognostics algorithm parameters based on this framework. Acknowledgments The authors would like to thank the more than 100 companies and organizations that support research activities at the Center for Advanced Life Cycle Engineering at the University of Maryland annually. The authors would also like to thank the members of the Prognostics and Health Management Consortium at CALCE for their support of this work. The authors thank Dr. Jose Celaya for his assistance in the setup of the IGBT experimental test bed and Dr. Bhaskar Saha for his guidance on particle filter implementation. The authors also thank Andreas Bobrow and Jonathan Howarth for their help in performing the experiments and Mark Zimmerman for his assistance with editing. References [1] Baliga B. Fundamentals of power semiconductor devices. 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