Search query: semiconductor assembly process

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abstract: Abstract The delamination in die attach layer is a problem that results in defects of semiconductor products. There are several elements and parameters, which are difficultly observed, causing delamination during the assembly process. Thus, it's critical to identify abnormal factors immediately for the real time correction and improvement. This paper proposes a two stage analysis framework to achieve two goals: identifying the key factors affecting delamination via variable selection and predicting the ratio of the delamination area in a die via the prediction models.
author_names: Shao-Yen Hung

Yung-Lun Lin

Chia-Yen Lee
title: Data Mining for Delamination Diagnosis in the Semiconductor Assembly Process
year: 2017

abstract: Increasingly, products with customized features are manufactured in small batches based on demand by unique customers. Such diversity leads to high product mix, thus severely impacting both production planning and scheduling activities such as frequent equipment setup and conversion. Despite increased complexity, production continues being pressured on minimal cycle time to meet on time delivery requirements. Focusing on the most challenging process, which is assembly, this paper presents batch sizing and lot splitting strategies to improve production cycle times. In the event of too short cycle time, overlapping technique is the most applicable, while "parallel" technique applies to normal cycle time. The commonly known "batch queue" technique works well for long cycle time such as new product qualification lot.
author_names: Zakiyah Zain

Siti Mariam Man

Mohd Kamal Mohd Nawawi
title: Batch Sizing and Lot Splitting Strategies to Reduce Cycle Time in Semiconductor Assembly Process
year: 2020

abstract: The transformation of wafers into chips is a complex manufacturing process involving literally thousands of equipment parameters. Delamination, a leading cause of defective products, can occur between die and epoxy molding compound (EMC) epoxy and substrate, lead frame and EMC, and so on. Troubleshooting is generally on a case by case basis and is both time consuming and labor intensive. We propose a three phase data science (DS) framework for process prognosis and prediction. The first phase is for data preprocessing. The second phase uses least absolute shrinkage and selection operator (LASSO) regression and stepwise regression to identify the key variables affecting delamination. The third phase develops a backpropagation neural network (BPNN) support vector regression (SVR) partial least squares (PLS) and gradient boosting machine (GBM) to predict the ratio of the delamination area in a die. We also investigate the imbalance between a false positive rate and a false negative rate after quality classification with BPN and GBM models to improve the tradeoff between the two types of risks. We conducted an empirical study of a semiconductor manufacturer, and the results show that the proposed framework provides an effective delamination prediction supporting the troubleshooting. In addition, for online prediction, it is necessary to determine the batch size for the timing of retraining the model, and we suggest the cost oriented method to solve the issue.
author_names: Shao-Yen Hung

Chia-Yen Lee

Yung-Lun Lin
title: Data Science for Delamination Prognosis and Online Batch Learning in Semiconductor Assembly Process
venue: IEEE Transactions on Components, Packaging and Manufacturing Technology
year: 2020

author_names: Dong Zhao-hong

Jang Bo-Kai

Chia-Yen Lee
title: Equipment Health Monitoring in the Semiconductor Assembly Process.
year: 2017

abstract: Objectives: The purpose of this study was to evaluate the characteristics of worker exposure to hazardous chemical substances and propose the direction of work environment management for protecting worker's health in the semiconductor assembly process. Methods: Four assembly lines at two semiconductor manufacturing companies were selected for this study. We investigated the types of chemicals that were used and generated during the assembly process, and evaluated the workers' exposure levels to hazardous chemicals such as benzene and formaldehyde and the current work environment management in the semiconductor assembly process. Results: Most of the chemicals used at the assembly process are complex mixtures with high molecular weight such as adhesives and epoxy molding compounds(EMCs) These complex mixtures are stable when they are used at room temperature. However workers can be exposed to volatile organic compounds(VOCs) such as benzene and formaldehyde when they are used at high temperature over 100degC. The concentration levels of benzene and formaldehyde in chip molding process were higher than other processes. The reason was that by products were generated during the mold process due to thermal decomposition of EMC and machine cleaner at the process temperature(180degC) Conclusions: Most of the employees working at semiconductor assembly process are exposed directly or indirectly to various chemicals. Although the concentration levels are very lower than occupational exposure limits, workers can be exposed to carcinogens such as benzene and formaldehyde. Therefore, workers employed in the semiconductor assembly process should be informed of these exposure characteristics.
author_names: Seung-Hyun Park

Hae Dong Park

In Jae Shin
title: Exposure Characteristics for Chemical Substances and Work Environmental Management in the Semiconductor Assembly Process
year: 2014

title: Research of Yield Improvement for Semiconductor Assembly Process Using Equipment Trouble Prediction
year: 2015

author_names: Muruhathasan Yogathasan
title: Simulation based analysis of cycle time and throughput in semiconductor assembly process integration
year: 2002

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author_names: Hiroshi Haji

Kiyoshi Kadoma Arita
title: Plasma Cleaning and Applications for Semiconductor Assembly Process
year: 2000

abstract: This paper considers a semiconductor assembly and test factory which is a three segment Constant Work in Process (CONWIP) system with overlapping machines. In the system, three types of carts circulate for meeting the physical requirements. The optimization problem in setting the suitable total Work in Process (WIP) level and the distribution in the three loops from the view of the trade off between the throughput and the WIP level for the system is addressed. In the proposed model, the system is firstly modeled as a three loop closed queue network and we propose an approximate method to evaluate the performance. The accuracy of the evaluation method was illustrated by numerical experiments, indicating that the method is fairly precise. Secondly, a Genetic Algorithm is designed to obtain near optimal results based on the performance evaluation. The semiconductor assembly and test system case as well as the application procedure were carried out in detail.
author_names: Na Li

Shiqing Yao

George Liu

Caihua Zhuang
title: Optimization of a multi Constant Work in Process semiconductor assembly and test factory based on performance evaluation
venue: Comput. Ind. Eng.
year: 2010

abstract: Hybrid nanoparticles have intrinsic advantages to achieve better activity in photocatalysis compared to single component materials, as it can synergistically combine functional components, which promote light absorption, charge transportation, surface reaction, and catalyst regeneration. Through functional modular assembly, a rational and stepwise approach has been developed to construct Fe3O4 CdS Au trimer nanoparticles and its derivatives as magnetically separable catalysts for photothermo catalytic hydrogen evolution from water. In a typical step by step synthetic process, Fe3O4 Ag dimers, Fe3O4 Ag2S dimers, Fe3O4 CdS dimers, and Fe3O4 CdS Au trimers were synthesized by seeding growth, sulfuration, ion exchange, and in situ reduction consequently. Following the same reaction route, a series of derivative trimer nanoparticles with alternative semiconductor and metal were obtained for water reduction reaction. The experimental results show that the semiconductor acts as an active component for photocatalysis, the metal nanoparticle acts as a cocatalyst for enhancement of charge separation, and the Fe3O4 component helps in the convenient separation of catalysts in magnetic field and improves photocatalytic activity under near infrared illumination due to photothermic effect.
author_names: Fei Pang

Ruifang Zhang

Dengpeng Lan

Jianping Ge
title: Synthesis of Magnetite Semiconductor Metal Trimer Nanoparticles through Functional Modular Assembly: A Magnetically Separable Photocatalyst with Photothermic Enhancement for Water Reduction.
venue: ACS applied materials interfaces
year: 2018