Fatigue life and resistance analysis of COG assemblies under hygrothermal aging

Zhang, Wenguo; Ma, Jian‐Nong; Gao, Lilan; Zhang, Zhe; Gao, Hong

doi:10.1016/j.microrel.2015.01.007

Cited by 8 publications

(5 citation statements)

References 32 publications

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“…It was noted that the shear strength and life gradually decreased at first, then quickly decreased and, finally, the rate of decrease slowed down again with a further increase of hygrothermal aging time. Zhang et al (2015) investigated the fatigue life and electrical properties of hygrothermally aged (85°C/85 per cent RH) COG assemblies exposed to temperature, electrical current and hygrothermal stress. The results showed that the fatigue life decreased and the relative resistance increased with an increase of hygrothermal aging time.…”

Section: Adhesion Property Of Acf Jointsmentioning

confidence: 99%

Recent advances in mechanical properties of anisotropic conductive adhesive film for microelectronic packaging

Gao

Chen

2015

Soldering &Amp; Surface Mount Technology

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Purpose -This review paper aims to provide a better understanding of formulation and processing of anisotropic conductive adhesive film (ACF) material and to summarize the significant research and development work for the mechanical properties of ACF material and joints, which helps to the development and application of ACF joints with better reliability in microelectronic packaging systems. Design/methodology/approach -The ACF material was cured at high temperature of 190°C, and the cured ACF was tested by conducting the tensile experiments with uniaxial and cyclic loads. The ACF joint was obtained with process of pre-bonding and final bonding. The impact tests and shear tests of ACF joints were completed with different aging conditions such as high temperature, thermal cycling and hygrothermal aging. Findings -The cured ACF exhibited unique time-, temperature-and loading rate-dependent behaviors and a strong memory of loading history. Prior stress cycling with higher mean stress or stress amplitude restrained the ratcheting strain in subsequent cycling with lower mean stress or stress amplitude. The impact strength and adhesive strength of ACF joints increased with increase of bonding temperature, but they decreased with increase of environment temperature. The adhesive strength and life of ACF joints decreased with hygrothermal aging, whereas increased firstly and then decreased with thermal cycling. Originality/value -This study is to review the recent investigations on the mechanical properties of ACF material and joints in microelectronic packaging applications.

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Section: Adhesion Property Of Acf Jointsmentioning

confidence: 99%

Recent advances in mechanical properties of anisotropic conductive adhesive film for microelectronic packaging

Gao

Chen

2015

Soldering &Amp; Surface Mount Technology

Self Cite

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“…26 Nonlinear stiffness degradation models have been presented for predicting fatigue damage development and service life in fiberglass and carbon fiber composites for over two decades. [27][28][29][30][31][32] Wu et al developed a stiffness degradation model that handles stresses from random vibrations by considering loading sequences. 27 Despite their simplicity, the models above require precise definitions of complex failure criteria, which are impossible when loading conditions are unknown.…”

Section: Introductionmentioning

confidence: 99%

LSTM-based fatigue monitoring of fiberglass composites using laser-induced graphene

Chen,

Childress,

Nasser

et al. 2024

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVIII

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This study aims to advance the field o f c omposite m aterial f atigue p rognosis b y e mploying L ong Short-Term Memory (LSTM) neural networks for in-situ damage progression monitoring under random dynamic loading conditions. A unique approach is adopted, wherein Laser-Induced Graphene (LIG) interlayers are embedded into fiberglass c omposites. T hese L IG i nterlayers a re i nnovative s ensors owing t o t heir p iezoresistive properties, enabling real-time measurement of fatigue damage monitoring. The crux of this research lies in applying LSTM neural networks, specifically designed to handle time-series data, making them ideal for modeling the stochastic and unpredictable nature of fatigue loading in composite materials. Contrasting the performance of LSTM with traditional Multilayer Perceptrons (MLP), it is observed that LSTM yields superior prediction accuracy in estimating the remaining useful life (RUL) of LIG interlayered fiberglass c omposites. B y u tilizing predefined electrical resistance damage parameters, the LSTM algorithm correlates the rate of fatigue damage buildup to the impending decline in mechanical performance. This research establishes that integrating piezoresistive LIG interlayers with LSTM neural networks culminates in a robust, reliable, and closed-loop system for structural fatigue monitoring and lifecycle prediction in composite materials subjected to random dynamic loading.

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“…37 Thus, several linear and non-linear stiffness degradation models have been developed to accurately forecast fatigue life. [38][39][40][41][42] Recently, artificial intelligence (AI) technologies like neural networks (NNs) have become reliable tools for predicting the mechanical behavior and reaction of structural materials like metals 43,44 and fiber-reinforced composites 45,46 in a number of applications. These NN approaches have better extrapolation accuracy, material and mechanism independence, and the capacity to offer new insights into the studied response.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser-induced graphene interlayered fiberglass composites

Chen

Childress

Nasser

et al. 2023

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVII

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Fiber-reinforced polymer matrix composites deteriorate mechanically due to fatigue degradation during cyclic stress. The progressive decrease in elastic stiffness over fatigue life is well-established and investigated, yet many dynamic engineering systems that use composite materials are subjected to random and unexpected loading circumstances, making it impossible to continually monitor such structural changes. LIG can detect strain and damage in fiberglass composites under quasi-static and dynamic loads. ANNs and traditional phenomenological models may assess damage development and fatigue life utilizing LIG interlayered fiberglass composites’ piezoresistivity. Passive experiments monitor LIG interlayered fiberglass composite elastic stiffness and electrical resistance during tension–tension fatigue stress. Electrical resistance-based damage metrics follow similar trends to elastic stiffness-based parameters and may accurately depict damage development in LIG interlayered fiberglass composites over fatigue life. In specimen-to-specimen and cycle-to-cycle schemes, trained ANNs and phenomenological degradation and accumulation models predict fiberglass composite fatigue life and damage state. In a specimen-to-specimen scheme, a two-layer Bayesian regularized ANN with 40 neurons per layer beats phenomenological degradation models by at least 60%, with R2 values more than 0.98 and RMSE values less than 10−3 . A two-layer Bayesian regularized ANN with 25 neurons per layer exhibits R2 values more than 0.99 and RMSE values less than 2×10−4 when more than 30% of the original data is used in a cycle-to-cycle method. Piezoresistive LIG interlayers and ANNs can correctly and constantly predict fatigue life in multifunctional composite structures.

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Fatigue life and resistance analysis of COG assemblies under hygrothermal aging

Cited by 8 publications

References 32 publications

Recent advances in mechanical properties of anisotropic conductive adhesive film for microelectronic packaging

Recent advances in mechanical properties of anisotropic conductive adhesive film for microelectronic packaging

LSTM-based fatigue monitoring of fiberglass composites using laser-induced graphene

Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser-induced graphene interlayered fiberglass composites

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