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The purpose of this paper is to develop a new approach called advanced overlapping production planning (AOPP) model which considers multi‐site process selection, sequential constraints, and capacity constraints in a manufacturing supply chain environment (MSCE). AOPP model may determine the capacity plan and order margin allocation for each site and machines in an MSCE and provide the capacity information for a production planner to effectively adjust the production strategies (e.g. outsourcing, overtime, or adding a work shift) of overloading resources.

First, an AOPP model is presented to model the production scheduling problem in a supply chain with the objective of minimizing the fulfilling cycle time of each order and the overloads of each machine group. Second, a genetic algorithm (GA)‐based approach for solving the AOPP model is developed. Finally, a heuristic adjustment approach is proposed for planners to adjust the production plan whenever there is an exception of production occurring.

The production schedule obtained from the GA‐based AOPP approach retains order margins in each operation against other overlapping operations, and it satisfies the capacity constraints of each machine group in an MSCE and results in a better performance in process planning and production planning with finite capacity. In practice, the overloading problem can be solved by adding a work shift or working overtime. The GA‐based AOPP model provides useful information for production planners to make such decisions.

Production planners need a more flexible production plan with order margins to compensate for the uncertainties which frequently occur in the supply and demand sides. This research develops a model to help planners manage the order margin of production planning in an MSCE and showing that order margins become a crucial factor for achieving effective production objective in terms of short OTD (or order cycle) time.

The overlapping production planning approach is a useful finite capacity planning approach for handling the capacity and order margin management in certain manufacturing environment (e.g. flow shop), but less on overcoming multi‐site process selection, sequential constraints, and capacity constraints in an MSCE. In addition, the capacity plan and order margin allocation information for each site and facilities are very important for a planner to effectively adjust the production strategies (e.g. outsourcing, overtime, or adding a work shift) of overloading resources. This research addresses both issues.

Researchers continue to seek understanding of industrialization as a state managed process. How to create and implement new industries based on advanced knowledge is on the policy agenda of many advanced nations. Measures that promote these developments include national capacity building in science and technology, the formation of technology transfer systems, and the establishment of industrial clusters. What these templates often overlook is an analysis of use. This chapter aims to increase the understanding of the processes that embed new solutions in structures from an industrial network perspective. The chapter describes an empirical study of high-technology industrialization in Taiwan that the researcher conducts to this end. The study shows that the Taiwanese industrial model is oversimplified and omits several important factors in the development of new industries. This study bases its findings on the notions that resource combination occurs in different time and space, the new always builds on existing resource structures, and the users are important as active participants in development processes.

The purpose of this paper is to present a novel photomask auto‐correction method for the area‐forming rapid prototyping (RP) system.

A digital light processing (DLP) projector was used in this research as a light source to generate the photomask image. A set of optical lenses were mounted in front of the DLP to rescale the photomask image. The rescaled photomask image was collected into a computer via a camera. By using the technique of image processing, the actual size of the photomask was then calculated. The designed size of the photomask image was eventually achieved by adjusting the relative locations of the lenses.

It was found that this proposed photomask auto‐correction method can produce a more accurate dimension of the photomask image and perform with higher efficiency than the manual calibration processes.

The paper is believed to be the first work to use the image‐processing technique to calibrate the photomask of an area‐forming RP system, as well as to employ a method of adjusting the relative position between the lenses to rescale the photomask image size.

Bumpless Cu/SiO₂ hybrid bonding, which this paper aims to, is a key technology of three-dimensional (3D) high-density integration to promote the integrated circuits industry’s continuous development, which achieves the stacks of chips vertically connected via through-silicon via. Surface-activated bonding (SAB) and thermal-compression bonding (TCB) are used, but both have some shortcomings. The SAB method is overdemanding in the bonding environment, and the TCB method requires a high temperature to remove copper oxide from surfaces, which increases the thermal budget and grossly damages the fine-pitch device.

In this review, methods to prevent and remove copper oxidation in the whole bonding process for a lower bonding temperature, such as wet treatment, plasma surface activation, nanotwinned copper and the metal passivation layer, are investigated.

The cooperative bonding method combining wet treatment and plasma activation shows outstanding technological superiority without the high cost and additional necessity of copper passivation in manufacture. Cu/SiO₂ hybrid bonding has great potential to effectively enhance the integration density in future 3D packaging for artificial intelligence, the internet of things and other high-density chips.

To achieve heterogeneous bonding at a lower temperature, the SAB method, chemical treatment and the plasma-assisted bonding method (based on TCB) are used, and surface-enhanced measurements such as nanotwinned copper and the metal passivation layer are also applied to prevent surface copper oxide.