Bo Tao, Zhouping Yin and Youlun Xiong
From the viewpoint of degree of cure, the purpose of this paper is to find how to improve the reliability of flip‐chip packaging modules based on an anisotropically conductive…
Abstract
Purpose
From the viewpoint of degree of cure, the purpose of this paper is to find how to improve the reliability of flip‐chip packaging modules based on an anisotropically conductive adhesive film (ACF) interconnection process.
Design/methodology/approach
The work begins by revealing the correlation of adhesive strength and contact resistance of flip‐chip joint interfaces with the degree of cure of the ACF. The effect of different degrees of curing on the electrical and mechanical properties of some typical ACF‐interconnected joints is studied, and the optimum degree of cure is suggested to achieve highly reliable ACF joints, where the performance variations of the adhesion strength and contact resistance are considered simultaneously. First, the degradation data of the contact resistance of some ACF assemblies, bonded with several degrees of cure, is collected during a standard high‐hydrothermal fatigue test. The resistance distribution is verified using a two‐parameter Weibull model and the distribution parameters are estimated, respectively. After that, a reliability analysis method based on the degradation data of contact resistance is achieved, instead of the traditional failure time analysis, and the reliability index, as well as the mean‐time‐to‐degradation of the ACF joints, as a function of the degree of cure, is deduced, through which the optimum degree of cure value and recommend range are suggested.
Findings
Numerical analysis and calculations are performed based on the experiments. Results show that the optimum degree of cure to achieve highly reliable joints is 83 per cent, and the recommend range is from 82 to 85 per cent for the ACF tested (considering a 95 per cent confidence interval).
Originality/value
The paper provides important support for optimizing the curing process for various ACF‐based packaging applications, such as chip‐on‐glass packaging of liquid crystal displays and flip‐chip bonding of radio frequency identification, etc.
Details
Keywords
Tao Bo, Yin Zhouping, Ding Han and Wu Yiping
The purpose of this paper is to present a novel reflow profile optimization method using mechanical reliability estimation of micro‐ball grid array (μBGA) solder joints, based on…
Abstract
Purpose
The purpose of this paper is to present a novel reflow profile optimization method using mechanical reliability estimation of micro‐ball grid array (μBGA) solder joints, based on the heating factor, Qη is introduced, where the coupling effect of reflow temperature and time on the mechanical reliability of μBGA joints is considered.
Design/methodology/approach
The method presented is actualized through vibration fatigue tests. First, a two‐parameter Weibull distribution is used to model the collected data of vibration fatigue lifetime for different Qη. After that, two explicit functions are deduced in a unified mathematic expression form, which give an intuitionistic description of the mean time to failure and reliability of solder joints against induced variable Qη, thus revealing definitely the effect of Qη on the mechanical fatigue lifetime of solder joints suffering from cyclic vibration loading. Finally, for a specified reliability goal, how to choose proper Qη values, based an improved Golden Section Search arithmetic, is discussed.
Findings
Numerical analysis and calculation are performed. The results show that the solder joints made at Qη near 510 have higher mechanical reliability, and those reflowed farther away this optimal value have less reliability.
Originality/value
This paper presents a useful and applicable solution to achieve reflow profile optimization and process control for a quantitative mechanical reliability estimation of μBGA solder joints.
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Keywords
Jun Zhang, Zuqiang Liu, Yanjie Liu and Yong Liu
The purpose of this paper is to apply grey statistical model to identify and classify live fault rupture.
Abstract
Purpose
The purpose of this paper is to apply grey statistical model to identify and classify live fault rupture.
Design/methodology/approach
Based on grey statistical mode, this paper uses eight faults' ripping speed observation data from 1997 to 2001, according to the grey statistics method for analysis, and recognizes active fault rupture situation. Using the conventional methods, namely taking all faults monitoring stations' average dislocation rate to analysis and make judgment, the average results are obtained.
Findings
The results show that the results are closer to reality because the grey statistical evaluation method has considered dislocation rate and other discrete factors.
Practical implications
The method exposed in the paper can be used to monitor and recognize live fault rupture in earthquake prediction.
Originality/value
According to the fault dislocation rate, this paper advances active fault rupture identification and classification method based on grey statistical model.
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Keywords
Yangyang Lai and Seungbae Park
This paper aims to propose a method to quickly set the heating zone temperatures and conveyor speed of the reflow oven. This novel approach intensely eases the trial and error in…
Abstract
Purpose
This paper aims to propose a method to quickly set the heating zone temperatures and conveyor speed of the reflow oven. This novel approach intensely eases the trial and error in reflow profiling and is especially helpful when reflowing thick printed circuit boards (PCBs) with bulky components. Machine learning (ML) models can reduce the time required for profiling from at least half a day of trial and error to just 1 h.
Design/methodology/approach
A highly compact computational fluid dynamics (CFD) model was used to simulate the reflow process, exhibiting an error rate of less than 1.5%. Validated models were used to generate data for training regression models. By leveraging a set of experiment results, the unknown input factors (i.e. the heat capacities of the bulkiest component and PCB) can be determined inversely. The trained Gaussian process regression models are then used to perform virtual reflow optimization while allowing a 4°C tolerance for peak temperatures. Upon ensuring that the profiles are inside the safe zone, the corresponding reflow recipes can be implemented to set up the reflow oven.
Findings
ML algorithms can be used to interpolate sparse data and provide speedy responses to simulate the reflow profile. This proposed approach can effectively address optimization problems involving multiple factors.
Practical implications
The methodology used in this study can considerably reduce labor costs and time consumption associated with reflow profiling, which presently relies heavily on individual experience and skill. With the user interface and regression models used in this approach, reflow profiles can be swiftly simulated, facilitating iterative experiments and numerical modeling with great effectiveness. Smart reflow profiling has the potential to enhance quality control and increase throughput.
Originality/value
In this study, the employment of the ultimate compact CFD model eliminates the constraint of components’ configuration, as effective heat capacities are able to determine the temperature profiles of the component and PCB. The temperature profiles generated by the regression models are time-sequenced and in the same format as the CFD results. This approach considerably reduces the cost associated with training data, which is often a major challenge in the development of ML models.