Cargando…
Development of ANN-Based Warpage Prediction Model for FCCSP via Subdomain Sampling and Taguchi Hyperparameter Optimization
This study aims to establish an accurate prediction model using artificial neural networks (ANNs) to effectively and efficiently predict the process-induced warpage of a flip-chip chip-scale package (FCCSP). To enhance model performance, a novel subdomain-based sampling strategy and Taguchi hyperpar...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383066/ https://www.ncbi.nlm.nih.gov/pubmed/37512636 http://dx.doi.org/10.3390/mi14071325 |
_version_ | 1785080815359623168 |
---|---|
author | Cheng, Hsien-Chie Ma, Chia-Lin Liu, Yang-Lun |
author_facet | Cheng, Hsien-Chie Ma, Chia-Lin Liu, Yang-Lun |
author_sort | Cheng, Hsien-Chie |
collection | PubMed |
description | This study aims to establish an accurate prediction model using artificial neural networks (ANNs) to effectively and efficiently predict the process-induced warpage of a flip-chip chip-scale package (FCCSP). To enhance model performance, a novel subdomain-based sampling strategy and Taguchi hyperparameter optimization are proposed in the ANN algorithm. To simulate the warpage behavior the FCCSP during fabrication, a process modeling approach is proposed, where the viscoelastic behavior of the epoxy molding compound is included, in which the viscoelastic properties are determined using dynamic mechanical measurement. In addition, the temperature-dependent thermal-mechanical properties of the materials in the FCCSP are assessed through thermal-mechanical analysis and dynamic mechanical analysis. The modeled warpage results are verified by the warpage measurement. Next, warpage parametric analysis is performed to identify the key factors most affecting warpage behavior for use in the construction of the warpage prediction model. Moreover, the advantages of the proposed sampling and hyperparameter tuning approaches are proved by comparing with other existing models, and the validity of the developed ANN-based deep learning warpage prediction model is demonstrated through a validation dataset. |
format | Online Article Text |
id | pubmed-10383066 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103830662023-07-30 Development of ANN-Based Warpage Prediction Model for FCCSP via Subdomain Sampling and Taguchi Hyperparameter Optimization Cheng, Hsien-Chie Ma, Chia-Lin Liu, Yang-Lun Micromachines (Basel) Article This study aims to establish an accurate prediction model using artificial neural networks (ANNs) to effectively and efficiently predict the process-induced warpage of a flip-chip chip-scale package (FCCSP). To enhance model performance, a novel subdomain-based sampling strategy and Taguchi hyperparameter optimization are proposed in the ANN algorithm. To simulate the warpage behavior the FCCSP during fabrication, a process modeling approach is proposed, where the viscoelastic behavior of the epoxy molding compound is included, in which the viscoelastic properties are determined using dynamic mechanical measurement. In addition, the temperature-dependent thermal-mechanical properties of the materials in the FCCSP are assessed through thermal-mechanical analysis and dynamic mechanical analysis. The modeled warpage results are verified by the warpage measurement. Next, warpage parametric analysis is performed to identify the key factors most affecting warpage behavior for use in the construction of the warpage prediction model. Moreover, the advantages of the proposed sampling and hyperparameter tuning approaches are proved by comparing with other existing models, and the validity of the developed ANN-based deep learning warpage prediction model is demonstrated through a validation dataset. MDPI 2023-06-28 /pmc/articles/PMC10383066/ /pubmed/37512636 http://dx.doi.org/10.3390/mi14071325 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cheng, Hsien-Chie Ma, Chia-Lin Liu, Yang-Lun Development of ANN-Based Warpage Prediction Model for FCCSP via Subdomain Sampling and Taguchi Hyperparameter Optimization |
title | Development of ANN-Based Warpage Prediction Model for FCCSP via Subdomain Sampling and Taguchi Hyperparameter Optimization |
title_full | Development of ANN-Based Warpage Prediction Model for FCCSP via Subdomain Sampling and Taguchi Hyperparameter Optimization |
title_fullStr | Development of ANN-Based Warpage Prediction Model for FCCSP via Subdomain Sampling and Taguchi Hyperparameter Optimization |
title_full_unstemmed | Development of ANN-Based Warpage Prediction Model for FCCSP via Subdomain Sampling and Taguchi Hyperparameter Optimization |
title_short | Development of ANN-Based Warpage Prediction Model for FCCSP via Subdomain Sampling and Taguchi Hyperparameter Optimization |
title_sort | development of ann-based warpage prediction model for fccsp via subdomain sampling and taguchi hyperparameter optimization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383066/ https://www.ncbi.nlm.nih.gov/pubmed/37512636 http://dx.doi.org/10.3390/mi14071325 |
work_keys_str_mv | AT chenghsienchie developmentofannbasedwarpagepredictionmodelforfccspviasubdomainsamplingandtaguchihyperparameteroptimization AT machialin developmentofannbasedwarpagepredictionmodelforfccspviasubdomainsamplingandtaguchihyperparameteroptimization AT liuyanglun developmentofannbasedwarpagepredictionmodelforfccspviasubdomainsamplingandtaguchihyperparameteroptimization |