Modeling Lifetime of High Power IGBTs in Wind Power Applications – An overview

Cristian Busca

Department of Energy Technology Aalborg University, Pontoppidanstraede 101, 9220, Denmark

E-mail: cbu@et.aau.dk

Abstract-The wind power industry is continuously developing bringing to the market larger and larger wind turbines. Nowadays reliability is more of a concern than in the past especially for the offshore wind turbines since the access to offshore wind turbines in case of failures is both costly and difficult. Lifetime modeling of future large wind turbines is needed in order to make reliability predictions about these new wind turbines early in the design phase. By doing reliability prediction in the design phase the manufacturer can ensure that the new wind turbines will live long enough. This paper represents an overview of the different aspects of lifetime modeling of high power IGBTs in wind power applications. In the beginning, wind turbine reliability survey results are briefly reviewed in order to gain an insight into wind turbine subassembly failure rates and associated downtimes. After that the most common high power IGBT failure mechanisms and lifetime prediction models are reviewed in more detail.

I. INTRODUCTION

The wind power industry has made great progress during the past decades. Nowadays wind energy is an attractive alternative to conventional energy sources. Wind turbines do not pollute the environment during their operation and according to the European Wind Energy Association (EWEA) a wind turbine harvests the equivalent amount of energy that is required in order to manufacture, operate and recycle the turbine (at the end of its life) in about three to six months of operation [1].

According to the EWEA in 1985 the largest wind turbines had power ratings of up to 50 kW and rotor diameters of up to 15 m. In 2009 the power rating of the largest wind turbines has reached a maximum of 7 MW while rotor diameters reached a maximum of 126 m [1]. There is a large increase in both the maximum rated power and maximum rotor diameter during the period from 1985 to 2009. In terms of maximum wind turbine rated power the increase is from 50 kW to 7 MW which means an increase by a factor of 140. In terms of maximum wind turbine rotor diameter the increase is from 15 m to 126 m which means an increase by a factor of more than 8. Recently (30 March 2011) Vestas has announced its new dedicated offshore wind turbine the V164-7.0 MW which has a rotor diameter of 164 m. The construction of the first V164 prototypes is expected in the fourth quarter of 2012 [31].

The total cumulative wind energy capacity installed in the European Union (EU) by the end of 2008 was 64.9 GW from which 1.5 GW is offshore and 63.4 GW is onshore [2]. According to the 2009 Renewable Energy Directive by the

end of 2020 EU should cover 20% of its energy needs with renewable energy. Wind power would account for about 14% to 17% and it means 230 GW of total installed wind power capacity from which 190 GW is onshore and 40 GW is offshore [2]. By looking at the numbers one can tell that there is a large predicted growth in installed wind power capacity during the period from 2008 to 2020. The EU countries with the highest amount of installed wind power capacity by the end of 2008 were Germany with 23.9 GW, Spain with 16.7 GW, Italy with 3.7 GW, France with 3.4 GW, United Kingdom with 3.2 GW and Denmark with 3.18 GW [2].

It is predicted that the offshore installed wind power capacity will increase from 1.5 GW to 40 GW by 2020 which is an increase by a factor of more than 26. Offshore wind power is very attractive due to the facts that the average wind speed is much higher offshore than on most onshore locations and the wind is steadier (less turbulent) leading to a greater wind turbine capacity factor. However in case of failure or scheduled maintenance the access to offshore wind turbines is more costly and difficult than in the case of onshore turbines. The countries with the highest amount of offshore wind power capacity by the end of 2008 were United Kingdom with 574 MW, Denmark with 426.6 MW, Netherlands with 247 MW and Sweden with 134 MW [3].

Lifetime modeling of high power IGBTs is required in order to add confidence to the design of new wind turbine power converters. By using an adequate lifetime model, the expected life of the power converter can be calculated taking into consideration the mission profile of the wind turbine. The mission profile of the wind turbine comprises all the variables such as wind speed, wind direction, temperature, vibration, cosmic radiation level, humidity etc. The mission profile of the IGBTs can be obtained by feeding the mission profile of the wind turbine into an adequate wind turbine model.

This paper is an overview of the different aspects of lifetime prediction of high power IGBTs in wind power applications. The second chapter deals with wind turbine reliability and the results of several reliability studies are briefly reviewed. In the third chapter the dominant failure mechanisms of high power IGBTs are reviewed. The fourth chapter deals with lifetime prediction and it reviews the most commonly used models for the lifetime prediction of high power IGBTs. The fifth chapter is the conclusions.

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II. WIND TURBINE RELIABILITY

Reliability represents a characteristic of an item which is expressed by the probability that the item will perform its function under given conditions (mission profile) for a stated time interval (mission time) [4].

Numerous reliability studies have been carried out based on statistical data from WMEP, LWK, WindStats, VTT, Elforsk, EPRI and NEDO [5], [6]. The statistical data was obtained by monitoring a large number of wind turbines usually for a long period of time. Generally the statistical data does not give details about the failure mode or cause of the subassemblies.

Modern onshore wind turbines have an availability of about 95% to 99% which means a downtime of about 3 to 18 days/turbine/year [5], [7]. It must be taken into consideration that this high availability is achieved not only because of good reliability but also because of frequent and fast service [7]. The goal of the wind turbine industry is to achieve higher reliability with decreased maintenance [8].

Fig. 1 is based on the LWK survey and it compares the average failure rates of different wind turbine models grouped by size. The failure rates were averaged over 11 year period

from 1993 to 2004 [5]. By looking at Fig. 1 it may seem that wind turbines from the large group tend to have higher failure rates than the turbines in the small and medium groups. This is an interesting finding considering the facts that both the size and the number of the installed wind turbines is growing. It must be pointed out that the apparently high failure rate of large wind turbines should be attributed to the immaturity of the manufacturing technology of these turbines rather than their size. It is therefore becoming important to accurately predict the reliability of future large wind turbines in the design phase in order to ensure that the end product will live long enough.

Fig. 2 shows a comparison of the LWK and WindStats (WSD and WSDK) surveys in terms of failure rate distribution between the wind turbine subassemblies [9]. The wind turbines from the WSDK survey have a lower overall failure rate than the turbines from the WSD and LWK surveys. The lower failure rate of the Danish wind turbines

was mainly attributed to the greater age (mature manufacturing technology) of these turbines [9]. The WSD and LWK turbines have similar failure rates even if they do not include the same wind turbines. As it may be seen in Fig. 2 the subassemblies which have high average failure rates are the electrical system, rotor blades and electrical control (power converter) [9]. Similar results have been obtained in a survey carried out in Sweden where it was concluded that the subassemblies which fail the most often are the electrical system, sensors and blade/pitch mechanisms [7]. Another survey based on the WMEP database has found that the most failure prone subassemblies are the electrical system, control system (power converter) and sensors [6]. However it must be pointed out that the power converter has an apparently high failure rate because it is often just the component that detects the failures. In reality the failure rate of power converters in wind turbines is much lower than suggested by the statistical analysis.

Fig. 3 is based on the WMEP database and it shows the failure rate of wind turbine subassemblies versus the

downtime produced per failure [10]. As it may be observed the failure of the electrical system (grid), electronic control (power converter), sensors and hydraulic system produce average downtimes of less than two days. The failure frequencies of the drive train, generator and gearbox are very

Fig. 3. Failure rates of different wind turbine subassemblies and theirassociated downtime [10].

Fig. 1. Comparison of the average failure rates of different wind turbine models grouped by size [5].

Fig. 2. Distribution of the average failure rates between subassemblies inwind turbines from the WSD, WSDK and LWK surveys [9].

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low but the resulting downtimes are high (around one week) [10]. The future large wind turbines especially the offshore ones should have a very high reliability due to the high value of the lost production and high repair cost in case of failures.

III. HIGH POWER IGBT FAILURE MECHANISMS

Insulated Gate Bipolar Transistors (IGBTs) are relatively new devices when compared with diodes and thyristors. IGBTs combine the advantages of bipolar devices and Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) [11]. The IGBTs are preferred over thyristors and Gate Turn-off Thyristors (GTOs) due to their higher switching frequency capability and easier controllability.

Fig. 4 shows the internal multilayered structure of a typical IGBT module [12]. As it may be seen the semiconductor chip

is soldered on a Direct Copper Bonded (DCB) ceramic substrate. The DCB itself is soldered on a metallic base plate and it has the role of electric insulator and thermal conductor at the same time. The Silicon (Si) chip is connected by means of aluminum bond wires [12]. The multilayered structure consists of several layers with different thermo-mechanical properties. The heat generated inside the semiconductor chip is conducted through the multilayered structure into the heat sink and it is then transferred to the ambient or cooling fluid (depending on the chosen cooling solution).

Three well known weak points inside a power module are the bond wires – Si chip interface and the two solder joints (Si chip to DCB and DCB to base plate) [12]. The main issue with power modules is the different Coefficient of Thermal Expansion (CTE) of the materials used to manufacture the module (Al, Si, Cu, DCB, solder, etc.) [11].

Fig. 5 is a close up on some of the bond wires of a power module and as it may be observed most of the bond wires are lifted-off (probably as a consequence of power cycling).

Bond wire lift-off is one of the most common failure mechanisms of power IGBT modules [14]. The reason of this failure mechanism is the mismatch of CTEs between Si and Al which is rather high being approximately 3 to 22 ppm/°C. There are technological countermeasures against bond wire lift-off like the use of a strain buffer which is mounted on the top of the chip in order to distribute the CTE mismatch across a thick layer. Another solution is to glue the bond wires to the chip with a coating layer [14].

Another important failure mechanism of IGBT modules is the solder joint fatigue [14]. In a typical multilayered IGBT module there are two solder joints: the Si chip – DCB solder joint and the DCB – base plate solder joint. This failure mechanism is associated with the thermo-mechanical fatigue of the solder material. The most critical solder joint is the one located between the DCB and the base plate because of the high mismatch in CTEs [14]. The fatigue cracks are usually found in the vicinity of the DCB substrate due to the higher temperature as it may be seen in Fig. 6. Voids and cracks in

the solder joint can greatly reduce the heat dissipating ability of the device leading to significantly increased peak junction temperatures. The increased peak junction temperatures thus further accelerates the evolution of different failure mechanisms in the device [14].

Another failure mechanism which can occur in IGBT modules is the bond wire heel cracking [14]. Fig. 7 indicates the probable location of bond wire heel cracking. Bond wire

heel cracking usually occurs after long power cycling tests and especially in cases where the bonding process was not optimized. Usually bond wire heel cracking is slower than the bond wire lift-off failure mechanism [14].

The reconstruction of the metallization is a degradation mechanism which can occur during thermal of power cycling of IGBT modules [14]. Pictures of the emitter metallization of an IGBT module before and after power cycling are shown in

Fig. 4. Internal multilayered structure of an IGBT module [12].

Fig. 5. Multiple bond wire lift-off [13].

Fig. 6. Solder joint fatigue (cracking) [15].

Fig. 7. Bond wire heel cracking [15].

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Fig. 8. It can be seen that after thermal cycling, aluminum

grains are extruded from the surface of the metallization (the surface becomes rough). Aluminum reconstruction reduces the effective cross section of the metallization and leads to an increased electrical resistance [14].

The press-pack packaging technology eliminates the bond wire lift-off and the solder joint fatigue failure mechanisms which are dominant in IGBT modules [17]. By eliminating these failure mechanisms the reliability of the device is considerably increased. The press-pack technology has been widely used for manufacturing Injection Enhanced Gate Transistors (IEGTs), Integrated Gate Commutated Thyristors (IGCTs), Gate Turn-off Thyristors (GTOs), high power diodes, etc. IGBTs are preferred over the mentioned semiconductor devices in high switching frequency applications due to their lower switching losses [18].

Fig. 9 shows the exploded view of a conventional round shaped press-pack IGBT. Press-pack devices have large-area

electrical contact and allow dual sided cooling which leads to an increased power density compared to IGBT modules [17]. In a press-pack IGBT the contacts to the gate, collector and emitter is made through pressure contact alone. The press-pack IGBT shown in Fig. 9 contains no bond wires or solder joints at all [20].

The driving force behind the press-pack IGBT degradation seems to be the CTE mismatch between the different layers of materials which are used to build the device [21]. When a press-pack IGBT is subjected to high enough temperature variations its internal subassemblies will experience different amounts of expansion/contraction thus leading to reciprocating sliding between the subassemblies. This reciprocating sliding can lead to the degradation of the

electrical and thermal properties of the subassemblies. The degradation of the electrical or thermal properties may be observed as an increase in the junction temperature. If the thermal properties of the subassemblies are degraded than the heat generated by the Si chip is not conducted so efficiently to the cooling circuit. If the electrical properties of the subassemblies are degraded than there will be a higher power loss leading also to an increased junction temperature [21].

In press-pack IGBTs the contact to the gate of each IGBT chip is made through sprung pins which connect each IGBT chip’s gate to a common gate track. These sprung pins have a spring inside which could introduce additional failure mechanisms which are not present in IGBT modules such as spring fatigue, spring stress relaxation, wear and fretting [22]. Spring fatigue can occur due to the fact that during power cycling the spring may experience repeated compression/expansion cycles. Although the temperature changes during power cycling will not cause large changes in the spring stress, with time the damage can accumulate and lead to the failure of the spring [22]. Spring stress relaxation can lead to an increased contact resistance which can cause localized heating. Stress relaxation is material, time and temperature dependent. Mechanical vibration could cause relative motion between the spring pin and gate die leading to gate die wear [22].

Cosmic rays can affect all kind of devices like diodes, thyristors, GTOs, IGBTs, etc. However IGBTs show an increased sensitivity to cosmic rays in respect to thyristors, GTOs and diodes [14]. Primary cosmic rays are high-energy particles that are found in space and originate from supernovae. According to Reference [23], primary cosmic rays usually do not reach the surface of the earth but collides with atmospheric particles and disintegrates into several other high-energy particles like pions, muons, neutrons, etc. Most of these particles are harmless for semiconductor devices but neutrons can be lethal for them [23]. The failure mode caused by cosmic rays consists in a localized breakdown in the bulk of the devices. The breakdown location on the Si wafer is random [24].

IV. HIGH POWER IGBT LIFETIME PREDICTION

A lifetime model predicts the expected life of an item considering the stresses that act on the device. For the IGBTs these stresses can be variables such as current, voltage, temperature, vibration, humidity, cosmic radiation level, etc.

Analytical lifetime models predict the number of cycles to failure of an IGBT device based on the parameters of the experienced temperature cycles (the stress is the temperature). These parameters can be amplitude, duration, frequency, mean value, dwell time, maximum and minimum [12]. The main issue with analytical lifetime models is the procedure of extracting the number, amplitude and duration of the thermal cycles from the temperature profile (mission profile) [25]. There are different thermal cycle definitions proposed in Reference [25] which can be used to extract the number and amplitude of thermal cycles from a temperature profile. A

Fig. 8. Metallization reconstruction caused by power cycling [16].

a) before power cycling b) after power cycling

Fig. 9. Exploded view of a round type press-pack IGBT [19].

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commonly employed algorithm used to extract the number and amplitude of thermal cycles from a temperature profile is the rain-flow counting algorithm. There are also other approaches which can be used for the extraction of the temperature cycles like the polynomial approach or the evaluation of the local maxima and minima [26]. IGBT analytical lifetime modeling is achieved by means of using Miner’s rule for damage accumulation. Miner’s rule assumes that each temperature cycle produces some degree of damage to the IGBT and thus contributes to the life consumption of the device [12].

The number of cycles to failure of a bond wire can be modeled using the simple Coffin-Manson relationship, as shown in (1) where Nf represents the number of cycles to failure, a and n are parameters obtained by numerical simulations or experimental measurements. This simple model takes into consideration only the temperature variation ΔT and is able to predict the lifetime of bond wires if the temperature does not exceed 120°C [14]. ∆ (1)

Another mathematical formulation of the Coffin-Manson model which besides the temperature variation ΔTj takes also into consideration the medium temperature Tm is shown in (2) [12]. In (2) k is the Boltzmann constant and Ea is the activation energy parameter which can be determined by test [27]. ∆ / (2)

Another model derived from the Coffin-Manson relationship is the Norris-Landzberg model whose mathematical formulation is given in (3). The Norris-Landzberg model includes the frequency parameter f but neglects the effects of other parameters such as heating and cooling time [12]. ∆ / (3)

A second model derived from the Coffin-Manson relationship is the Bayerer model which has multiple parameters as it may be seen in (4). The Bayerer model includes the influence of various parameters of power cycling tests and also power module characteristics [12]. In (4) Tj-max is the maximum junction temperature, ton is the heating time, I is the applied DC current, D is the diameter of the bond wire and V is the blocking voltage. The constants K and β are extracted from large amount of data collected in the long term reliability experiments [12]. ∆ / (4)

A third model derived from the Coffin-Manson relationship which can be used to model the number of cycles to failure of large solder joints is shown in (5). The parameter L represents

the typical lateral size of the solder joint, Δα is the CTE mismatch between the two surfaces which surround the solder joint, ΔT is the temperature variation, c is the fatigue exponent, x is the thickness of the solder layer and γ is the ductility factor of the solder [14]. 0.5 T (5)

The Schafft model is based on the power law and it can be used to predict the number o cycles to failure due to bond wire heel cracking. The mathematical formulation of Schafft’s model is shown in (6) where A and n are constants for a particular material and the wire strain is calculated as shown in (7) [14].

(6)

In (7) Δα is the CTE mismatch between Aluminum and Si, ΔT the temperature variation while r, ψ0, ρ0 are geometrical parameters defined as shown in Fig. 10 [14].

Ψ ∆ ∆Ψ

1 (7)

Physical lifetime models do not require the knowledge of the number and amplitude of thermal cycles experienced by a device in order to predict its life expectancy [12]. Physical lifetime models are based on the knowledge of stress – strain deformations within the device which can be obtained by simulations or experiments. For these models the failure and deformation mechanisms need to be priorly known [12].

The physical lifetime models for the estimation of the solder joint lifetime can be split into energy, damage, stress and strain based methods [12]. Energy based models tend to give better results than other models since these models capture test conditions with more accuracy. In the energy based methods it is assumed that a device reaches its end of life once the deformation energy accumulated in the device reaches a critical value Wtot [12].

Two energy based lifetime models capable of predicting the lifetime of the solder joint in an IGBT module are presented in Reference [28]. Reference [22] presents several models which can be used to predict the lifetime of spring interconnects in press-pack devices.

IGBT devices have a relatively long life at normal use conditions therefore accelerated life testing is used in order to determine their life expectancy at use level conditions and also for lifetime model parameterization [29].

Fig. 10. Bond wire lifetime model parameter definition [14].

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An example of an overstress acceleration method is the Calibrated Accelerated Life Testing (CALT). CALT is a sequential accelerated life test which reduces the test on time and sample size in comparison with other methods but still achieves useful lifetime estimation [30]. The CALT process is illustrated in Fig. 11. First the upper stress level needs to be

find either by asking product manufacturers or by running qualitative accelerated tests on the product. At this upper stress level the product fails really fast. A stress level 10% to 15% below the upper stress level is chosen for the first test and two samples are tested [30]. After that another stress level 10% to 15% lower than the first stress level is chosen and another two samples are tested. Using the data from the first two tests a preliminary life-stress plot can be obtained and the third stress level can be chosen based on the available time for the test [30]. At the third stress level is best to test more than two samples since the likelihood of observing a failure at this stress level is much lower in comparison to the first two stress levels [30].

V. CONCLUSIONS

Nowadays the reliability of wind turbines is more of a concern than in the past especially in the case of offshore wind turbines. Lifetime modeling of high power IGBTs is required in order to add confidence to the design of new wind turbine power converters. There are numerous lifetime models in the literature which can be used to model the dominant failure mechanisms of IGBT modules. Some of the models are very simple and do not require many parameters while other complex models require numerous parameters. Usually the lifetime models are parameterized using field or accelerated life testing data.

ACKNOWLEDGMENT

This work has been sponsored by the Vestas Power Programme (Vestas Wind Systems A/S – Aalborg University collaboration).

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Fig. 11. Illustration of the CALT process [30].

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