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A life‐cycle assessment (LCA) is based on the attention given to the environment protection and concerning the possible impact while producing, making, and consuming products. It includes all environmental concerns and the potential impact of a product’s life cycle from raw material procurement, manufacturing, usage, and disposal (that is, from cradle to grave). This study assesses the environmental impact of the ultra pure water process of semiconductor manufacturing by a life‐cycle assessment in order to point out the heavy environmental impact process for industry when attempting a balanced point between production and environmental protection. The main purpose of this research is studying the development and application of this technology by setting the ultra pure water of semiconductor manufacturing as a target. We evaluate the environemntal impact of the Precoat filter process and the Cation/Anion (C/A) filter process of an ultra pure water manufacturing process. The difference is filter material used produces different water quality and waste material, and has a significant, different environmental influence. Finally, we calculate the cost by engineering economics so as to analyze deeply the minimized environmental impact and suitable process that can be accepted by industry. The structure of this study is mainly combined with a life‐cycle assessment by implementing analysis software, using SimaPro as a tool. We clearly understand the environmental impact of ultra pure water of semiconductor used and provide a promotion alternative to the heavy environmental impact items by calculating the environmental impact during a life cycle. At the same time, we specify the cost of reducing the environmental impact by a life‐cycle cost analysis.

This investigation applied a hybrid method combining a trained artificial neural network (ANN) and the sequential quadratic programming (SQP) method to determine an optimal parameter setting for a reflow soldering process of ball grid array packages in printed circuit boards.

Nine experiments based on an orthogonal array table with three‐controlled inputs and average shear forces of solder spheres as a quality target were utilized to train the ANN and then the SQP method was implemented to search for an optimal setting of parameters.

The ANN can be utilized successfully to predict the shear force under different reflow soldering conditions after being properly trained and the identified optimal parameter setting are capable of striking the balance between the average shear forces and the manufacturing cycle time.

The reflow time and the peak temperature were found to be the most significant factors for the reflow process via analysis of variance.

This study provided an algorithm integrating a black‐box modeling approach (i.e. the ANN predictive model) with the SQP method to resolve an optimization problem. This algorithm offered an effective and systematic way to identify an optimal setting of the reflow soldering process. Hence, the efficiency of designing the optimal parameters was greatly improved.

The investigation uses design of experiments (DOE) approach to determine the optimal parameters of photo resist (PR) coating process for photolithography in wafer manufacturing.

Plans of experiments via nine experimental runs are based on the orthogonal arrays to determine the optimum factor condition. In this study, the mean thickness and the uniformity of thickness of the PR are adopted as the quality targets of the PR coating process. This partial factorial design of the DOE method provides an economical and systematic method of determining the optimal process parameter.

A model for the mathematical prediction of the mean thickness and the uniformity of thickness for the PR has been developed in terms of the PR temperature, the chamber humidity, the spinning rate, and the dispensation rate by means of the DOE method. The PR temperature and the chamber humidity are found to be the most significant factors in both the mean thickness and the uniformity of thickness for a PR coating process.

This analysis is valid of dynamic dispensing of the specific type of PR with constant material properties and applying onto a wafer size of eight inches.

A systematic method has been developed to find suitable combinations of process parameters; hence, the traditional approach such as the trial‐and‐error method that is very time‐consuming can be avoided. Furthermore, the efficiency of designs the parameter and the quality of the products are greatly improved.

This approach can be easily applied to design an optimal parameter setting to meet various requirements of different types of products.

This paper highlights cooperation as an important moderating condition of competitive action and response. Drawing on a new perspective of collective identity on competitive dynamics, the purpose of this paper is to stress the impacts of market commonalities and resource similarities on competitive actions and responses and focus on the moderating effect of cooperation on the relationships mentioned above.

This study employs logistic regression analysis to test the hypotheses in the Taiwanese flour industry at the period 2002–2005.

The results indicate market commonalities and resource similarities have a negative effect on the likelihood of a price-competitive action and a price-competitive response. Moreover, the level of cooperation among firms moderates the relationships among market commonalities, resource similarities, price-competitive actions, and price-competitive responses.

To understand and predict competitive behavior help firms to control and avoid unnecessary rivalry and therefore maintain mutual forbearance with competitors.

This study provides a new angle on cooperation-level analysis, contributing the use of collective identity theory to analyze the moderating effects of cooperation on competitive actions and responses.