Resistance Spectroscopy Based Assessment of Degradation in Cu-Al Wire Bond Interconnects

2013 ◽  
Author(s):  
Pradeep Lall ◽  
Yihua Luo
Keyword(s):  
Health Management ◽  
Copper Wire ◽  
Wheatstone Bridge ◽  
Gold Wire ◽  
Remaining Useful Life ◽  
Thermal Cycles ◽  
Leading Indicator ◽  
Wire Bond ◽  
Useful Life ◽  
The Wire

Escalation of the expense of gold has resulted in industry interest in use of copper as alternative wire bonds interconnect material. Copper wire has the advantage of lower price and comparable electrical resistance to gold wire. In this paper, 32-pin copper-aluminum wire bond chip scale packages are aged at three types of environment conditions separately. Environmental conditions included: 200°C for 10 days, 85°C and 85% RH for 8 weeks and −40°C to 125°C for 500 thermal cycles. The resistances of the wire bond are obtained every 24 hours for 200°C environment, every 7 days for 85C/85RH environment and every 5 days (50 thermal cycles) for the thermal cycling environment. A leading indicator has been developed in order to monitor the progression effect of the different thermal aging condition on the package and prognosticate remaining useful life based on the resistance spectroscopy. The Cu-Al wire bond resistance has been measured using a modified Wheatstone bridge. It has been shown previously that precise resistance spectroscopy is able to offer the failure of a leading indicator prior to the traditional definition of failure. The prognostic health management is qualified to be an efficient and accuracy tool for assessment of the remaining life of the wire bond. The ability to predict the remaining useful life of Cu-Al wire bond provides several advantages, including increasing safety by providing warning ahead of time before the failure.

High Temperature Storage and HAST Reliability of Copper-Aluminum Wirebond Interconnects

2014 ◽  
Author(s):  
Pradeep Lall ◽  
Shantanu Deshpande ◽  
Luu Nguyen
Keyword(s):  
High Temperature ◽  
Copper Wire ◽  
Gold Wire ◽  
Wire Bonding ◽  
Remaining Useful Life ◽  
Thermal Resistivity ◽  
Wire Bond ◽  
Trade Offs ◽  
Marquardt Algorithm ◽  
Temperature And Humidity

Gold wire bonding has been widely used as first-level interconnect in semiconductor packaging. The increase in the gold price has motivated the industry search for alternative to the gold wire used in wire bonding and the transition to copper wire bonding technology. Potential advantages of transition to Cu-Al wire bond system includes low cost of copper wire, lower thermal resistivity, lower electrical resistivity, higher deformation strength, damage during ultrasonic squeeze, and stability compared to gold wire. However, the transition to the copper wire brings along some trade-offs including poor corrosion resistance, narrow process window, higher hardness, and potential for cratering. Formation of excessive Cu-Al intermetallics may increase electrical resistance and reduce the mechanical bonding strength. Current state-of-art for studying the Cu-Al system focuses on accumulation of statistically significant number of failures under accelerated testing. In this paper, a new approach has been developed to identify the occurrence of impending apparently-random defect fall-outs and pre-mature failures observed in the Cu-Al wirebond system. The use of intermetallic thickness, composition and corrosion as a leading indicator of failure for assessment of remaining useful life for Cu-al wirebond interconnects has been studied under exposure to high temperature and temperature-humidity. Damage in wire bonds has been studied using x-ray Micro-CT. Microstructure evolution was studied under isothermal aging conditions of 150°C, 175°C, and 200°C till failure. Activation energy was calculated using growth rate of intermetallic at different temperatures. Effect of temperature and humidity on Cu-Al wirebond system was studied using Parr Bomb technique at different elevated temperature and humidity conditions (110°C/ 100%RH, 120°C/ 100%RH, 130°C/ 100%RH) and failure mechanism was developed. The present methodology uses evolution of the IMC thickness, composition in conjunction with the Levenberg-Marquardt algorithm to identify accrued damage in wire bond subjected to thermal aging. The proposed method can be used for quick assessment of Cu-Al parts to ensure manufactured part consistency through sampling.

Leading Indicators for Prognostication of Impending Failures on Cu-Al Interconnects

2013 ◽  
Author(s):  
Pradeep Lall ◽  
Shantanu Deshpande
Keyword(s):  
Thermal Aging ◽  
High Strength ◽  
Gold Wire ◽  
Wire Bonding ◽  
Remaining Useful Life ◽  
Wire Bond ◽  
Wire Bonds ◽  
Marquardt Algorithm ◽  
Useful Life ◽  
Copper Aluminum

Wire bonding is predominant mode of interconnect in electronics packaging. Traditionally material used for wire bonding is gold. But industry is slowly replacing gold wire bond by copper-aluminum wire bond because of the lower cost and better mechanical properties than gold, such as high strength, high thermal conductivity etc. Numerous studies have been done to analyze failure mechanism of Cu-Al wire bonds. Cu-Al interface is a predominant location for failure of the wirebond interconnects. In this paper, the use of intermetallic thickness as leading indicator-of-failure for prognostication of remaining useful life for Cu-Al wire bond interconnects has been studied. For analysis, 32 pin chip scale packages were used. Packages were aged isothermally at 200°C and 250°C for 10 days. Packages were withdrawn periodically after 24 hours and its IMC thickness was measured using SEM. The parts have been prognosticated for accrued damage and remaining useful life in current or anticipated future deployment environment. The presented methodology uses evolution of the IMC thickness in conjunction with the Levenberg-Marquardt Algorithm to identify accrued damage in wire bond subjected to thermal aging. The proposed method can be used for equivalency of damage accrued in Cu-Al parts subjected to multiple thermal aging environments.

Remaining useful life estimation: a review on stochastic process-based approaches

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu
Keyword(s):  
Stochastic Process ◽  
Health Management ◽  
Remaining Useful Life ◽  
Future Trends ◽  
Prognostics And Health Management ◽  
Inverse Gaussian ◽  
Life Estimation ◽  
Inverse Gaussian Process ◽  
Pros And Cons ◽  
Useful Life

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.

Application of Prognostics and Health Management (PHM) to Software System Fault and Remaining Useful Life (RUL) Prediction

2021 ◽  
Author(s):  
Mohammad Rubyet Islam ◽  
Peter Sandborn
Keyword(s):  
Health Management ◽  
Standard Test ◽  
Remaining Useful Life ◽  
Software Systems ◽  
Prognostics And Health Management ◽  
Performance Parameters ◽  
Statistical Validation ◽  
Useful Life ◽  
Test Beds

Abstract Prognostics and Health Management (PHM) is an engineering discipline focused on predicting the point at which systems or components will no longer perform as intended. The prediction is often articulated as a Remaining Useful Life (RUL). RUL is an important decision-making tool for contingency mitigation, i.e., the prediction of an RUL (and its associated confidence) enables decisions to be made about how and when to maintain the system. PHM is generally applied to hardware systems in the electronics and non-electronics application domains. The application of PHM (and RUL) concepts has not been explored for application to software. Today, software (SW) health management is confined to diagnostic assessments that identify problems, whereas prognostic assessment potentially indicates when in the future a problem will become detrimental to the operation of the system. Relevant areas such as SW defect prediction, SW reliability prediction, predictive maintenance of SW, SW degradation, and SW performance prediction, exist, but all represent static models, built upon historical data — none of which can calculate an RUL. This paper addresses the application of PHM concepts to software systems for fault predictions and RUL estimation. Specifically, we wish to address how PHM can be used to make decisions for SW systems such as version update, module changes, rejuvenation, maintenance scheduling and abandonment. This paper presents a method to prognostically and continuously predict the RUL of a SW system based on usage parameters (e.g., numbers and categories of releases) and multiple performance parameters (e.g., response time). The model is validated based on actual data (on performance parameters), generated by the test beds versus predicted data, generated by a predictive model. Statistical validation (regression validation) has been carried out as well. The test beds replicate and validate faults, collected from a real application, in a controlled and standard test (staging) environment. A case study based on publicly available data on faults and enhancement requests for the open-source Bugzilla application is presented. This case study demonstrates that PHM concepts can be applied to SW systems and RUL can be calculated to make decisions on software version update or upgrade, module changes, rejuvenation, maintenance schedule and total abandonment.

Prognostics Health Management Model for LED Package Failure Under Contaminated Environment

2015 ◽  
Author(s):  
Pradeep Lall ◽  
Hao Zhang ◽  
Lynn Davis
Keyword(s):  
Health Management ◽  
Luminous Flux ◽  
The State ◽  
Remaining Useful Life ◽  
Integrating Sphere ◽  
State Estimator ◽  
Acceptable Error ◽  
Color Shift ◽  
Useful Life ◽  
The Impact

The reliability consideration of LED products includes both luminous flux drop and color shift. Previous research either talks about luminous maintenance or color shift, because luminous flux degradation usually takes very long time to observe. In this paper, the impact of a VOC (volatile organic compound) contaminated luminous flux and color stability are examined. As a result, both luminous degradation and color shift had been recorded in a short time. Test samples are white, phosphor-converted, high-power LED packages. Absolute radiant flux is measured with integrating sphere system to calculate the luminous flux. Luminous flux degradation and color shift distance were plotted versus aging time to show the degradation pattern. A prognostic health management (PHM) method based on the state variables and state estimator have been proposed in this paper. In this PHM framework, unscented kalman filter (UKF) was deployed as the carrier of all states. During the estimation process, third order dynamic transfer function was used to implement the PHM framework. Both of the luminous flux and color shift distance have been used as the state variable with the same PHM framework to exam the robustness of the method. Predicted remaining useful life is calculated at every measurement point to compare with the tested remaining useful life. The result shows that state estimator can be used as the method for the PHM of LED degradation with respect to both luminous flux and color shift distance. The prediction of remaining useful life of LED package, made by the states estimator and data driven approach, falls in the acceptable error-bounds (20%) after a short training of the estimator.

scholarly journals Remaining Useful Life Prediction for Rotating Machinery Based on Optimal Degradation Indicator

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Aisong Qin ◽  
Qinghua Zhang ◽  
Qin Hu ◽  
Guoxi Sun ◽  
Jun He ◽  
...  
Keyword(s):  
Health Management ◽  
Rotating Machinery ◽  
Remaining Useful Life ◽  
Programming Algorithm ◽  
Wiener Model ◽  
New Approach ◽  
Degradation Modeling ◽  
Proposed Model ◽  
Useful Life ◽  
Degradation Indicator

Remaining useful life (RUL) prediction can provide early warnings of failure and has become a key component in the prognostics and health management of systems. Among the existing methods for RUL prediction, the Wiener-process-based method has attracted great attention owing to its favorable properties and flexibility in degradation modeling. However, shortcomings exist in methods of this type; for example, the degradation indicator and the first predicting time (FPT) are selected subjectively, which reduces the prediction accuracy. Toward this end, this paper proposes a new approach for predicting the RUL of rotating machinery based on an optimal degradation indictor. First, a genetic programming algorithm is proposed to construct an optimal degradation indicator using the concept of FPT. Then, a Wiener model based on the obtained optimal degradation indicator is proposed, in which the sensitivities of the dimensionless parameters are utilized to determine the FPT. Finally, the expectation of the predicted RUL is calculated based on the proposed model, and the estimated mean degradation path is explicitly derived. To demonstrate the validity of this model, several experiments on RUL prediction are conducted on rotating machinery. The experimental results indicate that the method can effectively improve the accuracy of RUL prediction.

Aircraft Fleet Maintenance Planning Using Combined Cost Benefit Model and Branch and Bound

2013 ◽  
Author(s):  
Behnam Razavi ◽  
Farrokh Sassani
Keyword(s):  
Branch And Bound ◽  
Health Management ◽  
Cost Benefit ◽  
Remaining Useful Life ◽  
Maintenance Planning ◽  
Flight Delays ◽  
Engine Operation ◽  
Maintenance And Repair ◽  
Flight Schedules ◽  
Useful Life

The tasks of maintenance and repair without optimal planning can be costly and result in prolonged maintenance times, reduced availability and possible flight delays. Aircraft manufacturers and maintainers see significant benefits in constantly improving Health Management and Maintenance (HMM) practices by deploying the most effective maintenance planning strategies. The planning of the maintenance and repair is a complex task due to chain dependency of engines to aircraft, and aircraft to the flight schedules. This paper presents a scheduling method for determining the time of maintenance based on the historical engine operation data in order to maximize the use of estimated remaining useful life of the engines as well as lowering the cost and duration of the downtime. The Time-on-Wing (TOW) data is used in conjunction with probability density functions to determine the shape of the respective distribution of the time of maintenance to minimize the loss of expected remaining useful life. Data from each engine with most chance of failure is then selected and fed into an extended Branch and Bound (B&B) routine to determine the best optimum sequence for entering the facility in order to minimize the waiting time.

scholarly journals Remaining Useful Life Estimation of Aircraft Engines Using a Joint Deep Learning Model Based on TCNN and Transformer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hai-Kun Wang ◽  
Yi Cheng ◽  
Ke Song
Keyword(s):  
Deep Learning ◽  
Working Conditions ◽  
Health Management ◽  
Remaining Useful Life ◽  
Aircraft Engines ◽  
Key Technology ◽  
Life Estimation ◽  
Useful Life ◽  
New Generation ◽  
Deep Learning Model

The remaining useful life estimation is a key technology in prognostics and health management (PHM) systems for a new generation of aircraft engines. With the increase in massive monitoring data, it brings new opportunities to improve the prediction from the perspective of deep learning. Therefore, we propose a novel joint deep learning architecture that is composed of two main parts: the transformer encoder, which uses scaled dot-product attention to extract dependencies across distances in time series, and the temporal convolution neural network (TCNN), which is constructed to fix the insensitivity of the self-attention mechanism to local features. Both parts are jointly trained within a regression module, which implies that the proposed approach differs from traditional ensemble learning models. It is applied on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset from the Prognostics Center of Excellence at NASA Ames, and satisfactory results are obtained, especially under complex working conditions.

scholarly journals Remaining Useful Life Estimation Using Deep Convolutional Generative Adversarial Networks Based on an Autoencoder Scheme

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Guisheng Hou ◽  
Shuo Xu ◽  
Nan Zhou ◽  
Lei Yang ◽  
Quanhao Fu
Keyword(s):  
Feature Extraction ◽  
Short Term Memory ◽  
Health Management ◽  
Remaining Useful Life ◽  
Fine Tuning ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Useful Life ◽  
Prediction Approach

Accurate predictions of remaining useful life (RUL) of important components play a crucial role in system reliability, which is the basis of prognostics and health management (PHM). This paper proposed an integrated deep learning approach for RUL prediction of a turbofan engine by integrating an autoencoder (AE) with a deep convolutional generative adversarial network (DCGAN). In the pretraining stage, the reconstructed data of the AE not only participate in its error reconstruction but also take part in the DCGAN parameter training as the generated data of the DCGAN. Through double-error reconstructions, the capability of feature extraction is enhanced, and high-level abstract information is obtained. In the fine-tuning stage, a long short-term memory (LSTM) network is used to extract the sequential information from the features to predict the RUL. The effectiveness of the proposed scheme is verified on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset. The superiority of the proposed method is demonstrated via excellent prediction performance and comparisons with other existing state-of-the-art prognostics. The results of this study suggest that the proposed data-driven prognostic method offers a new and promising prediction approach and an efficient feature extraction scheme.

Wire Bond Shear Test Simulation on Sharp Groove Surface Bond Pad

2012 ◽  
Vol 622-623 ◽  
pp. 647-651 ◽  
Author(s):  
Z. Sauli ◽  
V. Retnasamy ◽  
S. Taniselass ◽  
A.H.M. Shapri ◽  
R. Vairavan
Keyword(s):  
Stress Response ◽  
Shear Test ◽  
Copper Wire ◽  
Gold Wire ◽  
Shear Tests ◽  
Groove Surface ◽  
Wire Bond ◽  
Packaging Industry ◽  
Semiconductor Packaging ◽  
Surface Bond

Wire bonding process is first level interconnection technology used in the semiconductor packaging industry. The wire bond shear tests are used in the industry to examine the bond strength and reliability of the bonded wires. Hence, in this study thesimulation on wire bond shear test is performed on a sharp groove surface bond pad. ANSYS ver 11 was used to perform the simulation. The stress response of the bonded wires are investigated.The effects of three wire materials gold(Au), aluminum(Al) and copper(Cu) on the stress response during shear test were examined. The simulation results showed that copper wire bond induces highest stress and gold wire exhibits the least stress response.

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