Reliability Enhancement Through Optimal Burn-In

Burn-in is an important screening method used in predicting, achieving, and enhancing field reliability. Although electronics burn-in has been studied qualitatively, no comprehensive quantitative approach exists for determining optimal burn-in periods. This paper presents a cost-optimization model from a system viewpoint, with burn-in periods for the components as the decision variables. This model is applied to an electronic product recently developed which uses many ICs. State-of-the-art ICs have high early-failure rates and long infant mortality periods. Proper use of burn-in reduces early failure rates and reduces system deployment costs. The total cost to be minimized is formulated as a function of the mean costs of the component, device burn-in, shop repair, and field repair, which in turn are functions of the mean number of failures during and after burn-in. Component and system reliability are constraints that have to be satisfied. The early device failures are assumed to have a Weibull distribution. The formulated problem, with failure rates and cost factors, is optimized. Some basic properties of reliability and cost functions are discussed.     

电子元器件老炼试验技术

老炼是对电子元器件施加应力,剔除有缺陷的元器件的过程。长时间的老炼会对一些原本健康的器件寿命产生影响,但是时间过短却又不能起到很好地剔除有缺陷的元器件的目的。在相关文章的基础上总结了哪类电子元器件适合进行老炼,以及元器件老炼时间的优化问题,提出电子元器件老炼的3个准则,电子元器件最优老炼时间的确定问题,以及元器件老炼发展方向。     

Minimizing cost-functions related to both burn-in and field-operation under a generalized model

Summary and Conclusions-Burn-in is a method used to improve the quality of products. In field operation, only those units which survived the burn-in procedure will be used. This paper considers various additive cost structures related to both burn-in procedure and field operation under a general failure model. The general failure model includes two types of failures. Type I (minor) failure is removed by a minimal repair, whereas type II failure (catastrophic failure) is removed only by a complete repair (replacement). We introduce the following cost structures: (i) the expenses incurred until the first unit surviving burn-in is obtained; (ii) the minimal repair costs incurred over the life of the unit during field use; and (iii) either the gain proportional to the mean life of the unit in field operation or the expenditure due to replacement at a catastrophic failure during field operation. We also assume that, before undergoing the burn-in procedure, the unit has a bathtub-shaped failure rate function with change points t/sub 1/ & t/sub 2/. The optimal burn-in time b/sup */ for minimizing the cost function is demonstrated to be always less than t/sub 1/. Furthermore, a large initial failure rate is shown to justify burn-in, i.e. b/sup */>0. A numerical example is presented.     

Classifying Weak, and Strong Components Using ROC Analysis With Application to Burn-In

Any population of components produced might be composed of two sub-populations: weak components are less reliable, and deteriorate faster whereas strong components are more reliable, and deteriorate slower. When selecting an approach to classifying the two sub-populations, one could build a criterion aiming to minimize the expected mis-classification cost due to mis-classifying weak (strong) components as strong (weak). However, in practice, the unit mis-classification cost, such as the cost of mis-classifying a strong component as weak, cannot be estimated precisely. Minimizing the expected mis-classification cost becomes more difficult. This problem is considered in this paper by using ROC (Receiver Operating Characteristic) analysis, which is widely used in the medical decision making community to evaluate the performance of diagnostic tests, and in machine learning to select among categorical models. The paper also uses ROC analysis to determine the optimal time for burn-in to remove the weak population. The presented approaches can be used for the scenarios when the following information cannot be estimated precisely: 1) life distributions of the sub-populations, 2) mis-classification cost, and 3) proportions of sub-populations in the entire population.     

Burn-in optimization under reliability and capacity restrictions

Burn-in is a method to screen out early failures of electronic components. The burn-in problems that minimize the system life-cycle cost have been investigated reasonably well in many applications, but physical constraints during the decision process have not been considered. The authors search for optimal burn-in time and develop a cost-optimization model. Two types of constraint are to be satisfied during decision making: (1) the minimum system reliability requirement, and (2) the maximum capacity available for burn-in. Guidelines are suggested for making burn-in decisions. An example is given to illustrate a practical application. The model generalizes the burn-in problems that were oversimplified in a previous study     

A dual burn-in policy for defect-tolerant memory products using the number of repairs as a quality indicator

In most existing studies on the optimization of burn-in for semiconductor products, all chips are treated equally and subjected to burn-in of the same duration (i.e. a single burn-in (SBI) policy is employed). However, the quality levels of chips before burn-in are not the same in general, and therefore, it may be more advantageous to treat chips differently at the burn-in process based on appropriate quality indicators. This paper considers defect-tolerant memory products and develops a dual burn-in (DBI) policy in which the chips submitted to burn-in are classified into two groups according to the number of repairs, a quality indicator that can be obtained from the wafer probe test results, and different burn-in durations are applied to different groups of chips. Then, cost models are developed for the SBI and DBI policies, and their relative performances are compared in terms of the expected total cost per chip. The effectiveness of the proposed DBI policy is demonstrated using the actual data for a certain type of 256M DRAM products.     

On optimal burn-in procedures - a generalized model

Burn-in is a manufacturing technique that is intended to eliminate early failures. In this paper, burn-in procedures for a general failure model are considered. There are two types of failure in the general failure model. One is Type I failure (minor failure), which can be removed by a minimal repair or a complete repair; and the other is Type II failure (catastrophic failure), which can be removed only by a complete repair. During the burn-in process, two types of burn-in procedures are considered. In Burn-In Procedure I, the failed component is repaired completely regardless of the type of failure; whereas, in Burn-In Procedure II, only minimal repair is done for the Type I failure, and a complete repair is performed for the Type II failure. Under the model, various additive cost functions are considered. It is assumed that the component before undergoing the burn-in process has a bathtub-shaped failure rate function with the first change point t/sub 1/, and the second change point t/sub 2/. The two burn-in procedures are compared in cases when both the procedures are applicable. It is shown that the optimal burn-in time b/sup */ minimizing the cost function is always before t/sub 1/. It is also shown that a large initial failure rate justifies burn-in, i.e., b/sup */>0. The obtained results are applied to some examples.     

A nonparametric approach to estimate system burn-in time

System burn-in can get rid of more residual defects than component and subsystem burn-ins because incompatibility exists not only among components, but also among different subsystems and at the system level. There are two major disadvantages for performing the system burn-in: the high burn-in cost and the complicated failure rate function. This paper proposes a nonparametric approach to estimate the optimal system burn-in time. The Anderson-Darling statistic is used to check the constant failure rate (CFR), and the pool-adjacent-violator (PAV) algorithm is applied to “unimodalize” the failure rate curve. Given experimental data, the system burn-in time can be determined easily without going through complex parameter estimation and curve fittings     

Benefits of Integrated-Circuit Burn-In to Obtain High Reliability Parts

The technique of integrated circuit (IC) burn-in is applied industry-wide with the assumption that burned-in ICs have a much lower failure rate during operating life than ICs which are not burned-in. Several years ago this approach was valid for all ICs, but today burn-in procedures for some ICs provide little, if any, benefit. However, some customers still request burned-in ICs, assuming that this will produce better reliability. This paper provides historical data for linear ICs and presents a procedure to help the user determine if burn-in is worthwhile. An example for linear ICs where minimum benefit produced from burn-in is provided. By repeating this exercise with any other parts, the user can decide whether burn-in will decrease the failure rate appreciably for his application. This article deals specifically with decreases in failure rates through burn-in. It is not within the scope of this paper to describe general factors that could decrease failure rates.     

Modeling and maximizing burn-in effectiveness

System burn-in can get rid of many residual defects left from component and subsystem burn-in since incompatibility exists not only among components but also among different subsystems and at the system level. Even if system, subsystem, and component burn-in are performed, the system reliability often does not achieve the requirement. In this case, redundancy is a good way to increase system reliability when improving component reliability is expensive. This paper proposes a nonlinear model to: estimate the optimal burn-in times for all levels, and determine the optimal amount of redundancy for each subsystem. For illustration, a bridge system configuration is considered; however, the model can be easily applied to other system configurations. Since there are few studies on system, subsystem, and component incompatibility, reasonable values are assigned for the compatibility factors at each level     

Cost-Optimized Burn-In Duration for Repairable Electronic Systems

A mathematical model permits determining the duration of cost-optimized burn-in and evaluating the resultant saving for repairable electronics systems. Infant mortality failures occur according to a nonhomogeneous Poisson Process; repair actions restore the system to a bad-as-old condition. The s-expected costs associated with factory and field failures are traded-off with the costs of implementing a burn-in program. Under the constraints of the model, the optimum burn-in duration and consequent cost saving are independent of the eventual life of the system in the field. A numerical example illustrates these concepts.     

Effect of CMOS technology scaling on thermal management during burn-in

Burn-in is a quality improvement procedure challenged by the high leakage currents that are rapidly increasing with IC technology scaling. These currents are expected to increase even more under the new burn-in environments leading to higher junction temperatures, possible thermal runaway, and yield loss during burn-in. The authors estimate the increase in junction temperature with technology scaling. Their research shows that under normal operating conditions, the junction temperature is increasing 1.45/spl times//generation. The increase in junction temperature under the burn-in condition was found to be exponential. The range of optimal burn-in voltage and temperature is reduced significantly with technology scaling.     

Optimal System Reliability Parameters with Minimum Costs

This paper gives a procedure for a repairable complex system which will determine the failure and repair rates as well as the number of redundant components to achieve minimum total s-expected cost for a given availability. The total s-expected cost is the s-expected investment and operating costs. The capital investment cost function is related to the steady-state availability of each component and the operating cost function to the steady-state probability of the system's being in a particular state.     

Effect of plasma operation pressure on burn-in efficiency of an SIP-PVD chamber

The effect of the plasma operation pressure on the burn-in efficiency of a self-ionized-plasma (SIP) physical vapor deposition (PVD) system for growing TiN liner films was examined. The experiments in this study were designed to obtain the proper plasma operation pressure and to set up a simplified procedure for optimization of the productivity and yield. Our results indicated that the number of particles falling on the wafer started to decrease when increasing the burn-in plasma pressure above 10 mTorr and a comparatively stable condition could be maintained at a high burn-in plasma pressure over 20 mTorr. It is believed that the increase of Ar pressure can influence the plasma spatial distribution within the chamber, and thereby enable the plasma to eliminate the particle sources concealed in the chamber narrow space. By proper Ar pressure the conventional burn-in procedure could be simplified without changing the TiN film properties. The simplified burn-in method can not only enhance the front-end yield, but it can also benefit the cost and productivity for production line consideration.     

Burn-In of Incandescent Sign Lamps

This paper describes an experiment in which it was determined that properly designed burn-in can not only eliminate a weak subpopulation, but also stabilize the healthy one. Accelerated life tests have been carried out on 30W sign lamps to show that the time-to-failure pattern is bimodal. The accelerated life data have been used to design burn-in process parameters, and to determine the deterioration caused in the healthy subpopulation by the chosen burn-in. The results showed no decrease in mean life of the healthy lamps. Rather, increased stability (lower standard deviation in life) has been observed. The burn-in procedure with parameters 240V and duration in the range 15-20s was successfully applied to the 30W sign lamps in order to eliminate the weak subpopulation. Due to fast turnaround of the procedure it can be implemented at the end of the production line. The high temperature thermal treatment of filaments during burn-in seems to relax structural stresses caused by fabrication process. The fact that the power coefficient obtained here differs from the one quoted by GE Technical Information TP-110 as well as by IES Lighting Handbook (Reference Volume 1984) should alert users of accelerated reliability tests to be wary of uncritical application of experimental factors taken from the literature, including this paper.     

A relation model of gate oxide yield and reliability

The relationship between yield and reliability is obviously important for predicting and improving reliability during the early production stage, especially for new technologies. Previous research developed models to relate yield and reliability when reliability is defined as the probability of a device having no reliability defects. This definition of reliability is not a function of mission time and thus is not consistent with reliability estimated from the time-to-first-failure data which is commonly used. In this paper, we present a simple model to tie oxide yield to time-dependent reliability by combining the oxide time to breakdown model with a defect size distribution. We show that existing models become special cases when a single mission time is considered. As the proposed reliability function has a decreasing failure rate, the result is useful for a manufacturer seeking to find an optimal burn-in policy for burn-in temperature, burn-in voltage, and burn-in time.     

An overview of manufacturing yield and reliability modeling forsemiconductor products

This paper presents an overview of yield, reliability, burn-in, cost factors, and fault coverage as practiced in the semiconductor manufacturing industry. Reliability and yield modeling can be used as a foundation for developing effective stress burn-in, which in turn can warranty high-quality semiconductor products. Yield models are described and their advantages and disadvantages are discussed. Both yield reliability relationships and relation models between yield and reliability are thoroughly analyzed in regard to their importance to semiconductor products     

Cost optimal, parallel reliability systems with fixed repair sanction

Optimisation methods under varied criteria for different parameters in stochastic reliability systems are being increasingly developed and have been reported in recent literature. The large interest evinced in this fascinating area is primarily due to its applicational value and operational role in the decision making process. Recently a parallel system has been considered and the optimal number of units discussed, as well as optimal replacement times for the system based on acquisition and replacement costs.In this paper we consider an improved version of the model formulation, by bringing in additionally the maintenance and per unit repair time costs, and develop a procedure to obtain the optimal number of components in the system with the condition that the system is allowed to undergo a prefixed maximum number of repairs, after which the system is to be replaced.The applicational use of the results is illustrated through numerical work, specialising to some known laws governing the system parameters and corresponding to different fixed number of repair sanctions.     

Diagnostic-strategy selection for series systems

The selection of efficient testing strategies for repairable systems composed of components arranged in series is considered. Two cost models (for perfect and imperfect testing) represent the consequences of possible test realizations. The probability that any particular component is responsible for the failure is derived and used as a basis for the two models. The model for perfect testing is solved exactly. In the optimal perfect-test sequence the components are tested in decreasing order of the ratio of: [probability that the component is responsible for the system failure] to [component test cost]. For imperfect testing, possible diagnostic errors are included in a model for which two heuristic solution strategies are provided. The model represents the consequences of both false-positive and false-negative component-test outcomes. The heuristic strategies yield efficient test sequences. Under reasonable assumptions, the second heuristic strategy is guaranteed to locate the optimal test sequence. The model can quantitatively evaluate the benefits of test-accuracy enhancement plans. These models and algorithms provide convenient methods for selecting efficient test-sequences. This is illustrated by representative examples