Reliability prediction modeling of semiconductor light emitting device

High quality in systems and products is a wide term which include : reliability, initial performance, easy to use, security and compatibility under different environments. The most important characteristic of quality is reliability because of its impact in productivity, maintenance and costs. This paper shows the failure distribution estimation at component level for a non repairable item(considering only first failure) using Matlab. Failure distribution is important for reliability prediction and optimal maintenance policy. Although reliability prediction is important in the early stages of the design is also important in studies of maintenance requirements and reliability growth. The failure data for mechanical components sample was recorded and then the statistical analysis was done using Minitab, fitting the best statistical model between Normal, Lognormal, Exponencial and Weibull distribution. The minimum Anderson Darling coeficient was found for Weibull Distribution. Because the Minitab program assume the location parameter in zero which can deal to a wrong conclusion, a program for nonlinear programming was written in Matlab to find the parameters for Weibull distribution. The three parameters founded were â=3.6 , c=3284.5, a=1140.8. The common functions in reliability engineering were generated: failure density function, reliability function, mean time between failures, and failure rate function. The reliability estimation at any given stage as well as prediction of future reliability can be accomplished once the failure distribution function has been calculated . The statistical models not only represent the system , but enable the manager to assess progress and evaluate the quality of a product at a given point in time.

Predicting the reliability is an important task of product life cycle analysis, especially during the product development stages. The uncertainty in the operating conditions and the presence of multistress factors makes this reliability prediction more difficult. In this work, a probabilistic reliability prediction framework is proposed using the linear damage accumulation and degradation modeling for multistress conditions. The multistress-life (S-L) curve for corresponding multistress has been developed using the equivalent stress. The multi S-L model allows extrapolating the expected life product under given operating or test conditions as well as provides input to estimate the reliability as a function of time for both a single multistress factor level and a sequence of multistress-factor levels. The proposed methodology has been demonstrated with newly developed flexible hybrid electronics products.

As a popular means for capturing behavioural requirements, scenarios show how components interact to provide system-level functionality. If component reliability information is available, scenarios can be used to perform early system reliability assessment. In previous work we presented an automated approach for predicting software system reliability that extends a scenario specification to model (1) the probability of component failure, and (2) scenario transition probabilities. Probabilistic behaviour models of the system are then synthesized from the extended scenario specification. From the system behaviour model, reliability prediction can be computed. This paper complements our previous work and presents a sensitivity analysis that supports reasoning about how component reliability and usage profiles impact on the overall system reliability. For this purpose, we present how the system reliability varies as a function of the components reliabilities and the scenario transition probabilities. Taking into account the concurrent nature of component-based software systems, we also analyse the effect of implied scenarios prevention into the sensitivity analysis of our reliability prediction technique.

Reliability prediction and modeling of 30 V-12 V to 5 V voltage regulator for automotive application using IEC-62380 reliability prediction standard

Accurate prediction of surface deformation due to the extraction of underground coal seams is a significant challenge in geotechnical engineering. This task is further compounded by the growing trend for coal to be extracted under buildings by leaving coal pillars in place. To rationalize the design of mining schemes to ensure effective control of surface deformation, precise 3D numerical models of different mining schemes were developed in this paper. Two schemes were compared and analyzed based on the results of numerical simulations. Also, to verify the reliability of the prediction results from numerical simulations, the probability integral method was used for this prediction. The results show that the prediction results of the numerical simulations are close to those calculated by the probabilistic integral method, which proves the reliability of the numerical simulation prediction. The optimal mining scheme was determined by comparing the predicted results from the analysis of theoretical calculations and numerical simulations.

A method for reliability prediction, called SYRP (synergetic reliability prediction), is presented, based on a combined fuzzy-logic & physics-of-failure approach. SYRP is used for a semiconductor device (thermal sensors) and the estimations, compared with experimental results, prove to be accurate enough. Also, the problems to be solved for using this method for MEMS are presented. The specific case of a MEMS Fabry-Perot interferometer is analyzed and the failure rate estimations are discussed. The method seems to be particularly useful for the reliability analysis made by virtual prototyping

A robust reliability prediction method using Weighted Least Square Support Vector Machine equipped with Chaos Modified Particle Swarm Optimization and Online Correcting Strategy

This paper is to investigate both deterministic and statistical aspects of thermal reliability of solder joints of surface mount leadless components (SMLCs). The emphasis is on bridging deterministic with statistical reliability prediction. A reliable methodology has been established to predict the failure rate at accelerated life tests (ALTs) and field failed rate in terms of key statistical parameters of design, environmental condition, and material selection due to the uncertainty from the component manufacturing/assembly, temperature profile of ALTs and field environmental conditions, and material property. Analytical equations and solutions of inelastic strain range and fatigue life for simplified joint geometry have been developed from deterministic approach. They are furthermore utilized to obtain the failure functions of thermal fatigue caused by both crack initiation and crack propagation from multivariable distributions. First Order Reliability Model (FORM) has been extended by combining Taylor series in technique with central limit theorem (CLT). An important outcome is that the statistical fatigue life is a lognormal distribution in which its parameters can be analytically evaluated by the approximate method with satisfactory accuracy for small COVs (COV=mean/deviation) of random variables (RVs). Specifically, SMLCs have been investigated on inelastic strain distribution, fatigue life distribution, failure and reliability functions, and failure rate prediction based on the statistical distributions of the solder joint height, solder paste size, temperature profile, and the experimental property of the eutectic solder alloy. Moreover, the component failure under two failure modes, i.e., both crack initiation and crack propagation, has been performed to illustrate the significance of failure criteria selection and address the data collection in field. Additionally, the simulation of realistic solder joint geometry and damage-based failure processes will be also presented. The developed methodology can be directly used for the board-level reliability prediction of advanced electronic packages such as BGAs, CSPs, QFPs, and Flip-chips.

Obtaining accurate runoff prediction results and quantifying the uncertainty of the forecasting are critical to the planning and management of water resources. However, the strong randomness of runoff makes it difficult to predict. In this study, a hybrid model based on XGBoost (XGB) and Gaussian process regression (GPR) with Bayesian optimization algorithm (BOA) is proposed for runoff probabilistic forecasting. XGB is first used to obtain point prediction results, which can guarantee the accuracy of forecast. Then, GPR is constructed to obtain runoff probability prediction results. To make the model show better performance, the hyper-parameters of the model are optimized by BOA. Finally, the proposed hybrid model XGB-GPR-BOA is applied to four runoff prediction cases in the Yangtze River Basin, China and compared with eight state-of-the-art runoff prediction methods from three aspects: point prediction accuracy, interval prediction suitability and probability prediction comprehensive performance. The experimental results show that the proposed model can obtain high-precision point prediction, appropriate prediction interval and reliable probabilistic prediction results on the runoff prediction problems.

Photovoltaic power prediction under insufficient historical data based on dendrite network and coupled information analysis

Harmonic reducers exhibit non-stationary and phase-dependent degradation behavior during long-term service, challenging the ability of classical stochastic degradation models to accurately assess reliability. To address phase-dependent differences in degradation behavior, this paper proposes a reliability assessment model based on a two-phase hybrid stochastic degradation process. In the proposed framework, the Wiener process is employed to characterize early-phase gradual degradation dominated by stochastic fluctuations, while the Inverse Gaussian process is used to describe later-phase monotonically accelerated degradation driven by cumulative damage. The framework allows for sample-level variability in transition times to more realistically capture individual degradation behavior. The Schwarz Information Criterion is also adopted to detect change points. Maximum likelihood estimation is performed for model parameter inference, and analytical expressions for the reliability function, cumulative distribution function, and probability density function are derived. Numerical results indicate that a change point exists for each tested product and that the proposed model achieves the best goodness of fit among the considered candidates, demonstrating its superiority in capturing phase-dependent characteristics of harmonic reducer degradation. In terms of reliability assessment bias, the proposed model (0.06%) significantly outperforms the Wiener degradation model (32%) and the IG degradation model (9.9%). These results further confirm that, under an identical failure threshold, the proposed approach yields more accurate and realistic reliability assessment outcomes.

Controlled degradation of hydrogels using multi-functional cross-linking molecules

In practice, the getting product data types have ordinary real number, interval-valued in real number field, fuzzy set in real number field. In order to dispose these data simultaneously, a new generalized closeness degree is proposed in this paper. And then make it applied to reliability prediction. The new generalized closeness degree is getting through the weighted fusion with two of commonly used closeness degree. lattice close-degree Based on membership degree, and measure close-degree Based on area ratio. According calculating the characteristic factors’ close-degree between the awaiting predicting product and the case products, for every attribute value distributing a weight, prediction result is obtained by integrating the data.

: This report presents reliability prediction models for high density microcircuits including magnetic bubble and charge-coupled device memories for inclusion in MIL-HDBK-217C, 'Reliability Prediction of Electronic Equipment'. The approach in this investigation was unique in that the models were developed utilizing only early device life cycle data, since there was only limited field data available. Two separate models were developed. An additive hybrid type failure rate prediction model was formulated for Magnetic Bubble Memories and for the Charge-Coupled Memory devices it was found that the NMOS Dynamic RAM model currently in MIL-HDBK-217C, Notice 1 was applicable. Both models are presented in the standard MIL-HDBK-217C format, are relatively simple to use, and allow for refinements based on field and life test data as it becomes available. Finally the models exhibit reasonable correlation with existing field data. (Author)

Research on software reliability prediction is of great practical importance. Failure characteristic of large and complex software depends on the operations of individual components and their architecture. Complexity of the components as well as their dependency provides a greater impact on overall reliability of the software. Reliability prediction in the early stage of component based software requires the knowledge of interconnection between the components as well as propagation of errors between the components. In this paper we propose a reliability prediction model which not only considers the control flow of the component it also considers data sharing between the components and their deployment details. We propose a new graphical structure Data Flow Dependency Graph to estimate the effective reliability of the data processed by the components and then use the operational profile to predict the reliability.