Automation and Integration in Semiconductor Manufacturing

Liao, Da-Yin

doi:10.5772/8569

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…Lee [26] reviews automation requirements and technologies for semiconductor manufacturing, including fab integration architectures and fab operations with automated material-handling systems (AMHS), communications and networking, fab control application integration, and fab control and management. Liao [9] deals with the automation and integration problems in semiconductor manufacturing and proposes an intelligent AMHS management framework to optimize the integration of fab operations with AMHS. To our best knowledge, neither papers nor research results have been published so far on the design and applications of distributed ledger and edge computing technologies for automation and computer integration in semiconductor manufacturing.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The implementation of existing semiconductor manufacturing automation usually involves the integration of three levels of functions [9]. They are Fab Automation at the top, Cell Automation in the middle, and Tool Automation at the bottom.…”

Section: Automation and Computer Integration In Semiconductor Manufacturingmentioning

confidence: 99%

“…Effective manufacturing of semiconductor products demands a high level of automation and computer integration in allocation, coordination, and collaboration among system dynamics as well as flows of data, information, command, control, communication, and materials. Automation in semiconductor manufacturing uses a hierarchical control architecture design [9]. In the lower level of the hierarchy, there are embedded controllers that provide real-time control and analysis of fabrication equipment.…”

Section: Introductionmentioning

confidence: 99%

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A Distributed-Ledger, Edge-Computing Architecture for Automation and Computer Integration in Semiconductor Manufacturing

Liao¹

2020

GJCST

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Contemporary 300mm semiconductor manufacturing systems have highly automated and digitalized cyber-physical integration. They suffer from the profound problems of integrating large, centralized legacy systems with small islands of automation. With the recent advances in disruptive technologies, semiconductor manufacturing has faced dramatic pressures to reengineer its automation and computer integrated systems. This paper proposes a Distributed-Ledger, Edge-Computing Architecture (DLECA) for automation and computer integration in semiconductor manufacturing. Based on distributed ledger and edge computing technologies, DLECA establishes a decentralized software framework where manufacturing data are stored in distributed ledgers and processed locally by executing smart contracts at the edge nodes. We adopt an important topic of automation and computer integration for semiconductor research &development (R&D) operations as the study vehicle to illustrate the operational structure and functionality, applications, and feasibility of the proposed DLECA software framework.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Automation and Computer Integration In Semiconductor Manufacturingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Distributed-Ledger, Edge-Computing Architecture for Automation and Computer Integration in Semiconductor Manufacturing

Liao¹

2020

GJCST

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“…Thus, productivity improvement is very important for factories. Cluster tools consist of several single-wafer processing chambers, for diverse semiconductor fabrication processes, shorter cycle time, faster process development, and better yield for less contamination [1]. Based on experiences over the past decade, this kind of equipment has been widely used for wafer fabrication processes and occupy over 60% capacity of modern wafer fabrication factories.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Scheduling Model of Cluster Tool in Wafer Fabrication

2021

Mathematics

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Since last decade, the cluster tool has been mainstream in modern semiconductor manufacturing factories. In general, the cluster tool occupies 60% to 70% of production machines for advanced technology factories. The most characteristic feature of this kind of equipment is to integrate the relevant processes into one single machine to reduce wafer transportation time and prevent wafer contaminations as well. Nevertheless, cluster tools also increase the difficulty of production planning significantly, particularly for shop floor control due to complicated machine configurations. The main objective of this study is to propose a short-term scheduling model. The noteworthy goal of scheduling is to maximize the throughput within time constraints. There are two modules included in this scheduling model—arrival time estimation and short-term scheduling. The concept of the dynamic cycle time of the product’s step is applied to estimate the arrival time of the work in process (WIP) in front of machine. Furthermore, in order to avoid violating the time constraint of the WIP, an algorithm to calculate the latest time of the WIP to process on the machine is developed. Based on the latest process time of the WIP and the combination efficiency table, the production schedule of the cluster tools can be re-arranged to fulfill the production goal. The scheduling process will be renewed every three hours to make sure of the effectiveness and good performance of the schedule.

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“…Contemporary semiconductor manufacturing is tending to increase cluster tools, each of which consists of several single-wafer processing chambers, for diverse semiconductor fabrication processes, shorter cycle time, faster process development, and better yield for less contamination [1]. Based on the experiences over the past decade, the main stream of production equipment are cluster tools which almost occupy 60% capacity of modern wafer fabrication factories and continuous to increase for advanced technology.…”

Section: Introductionmentioning

confidence: 99%

Throughput Estimation Model of Cluster Tool in Semiconductor Manufacturing

2019

KEM

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Semiconductor manufacturing management system was developed and grown up over the past decades. In order to increase the product yield and enhance the production productivity, cluster tools became the main stream in modern wafer fabrication factories which occupies over 50% of production equipment. Generally, cluster tools are integrated by several components including robots, vacuum chambers (Load locks) and single-wafer process chambers in a module and can be treated as a small factory. The throughput estimation before recipe release is very difficult. However, it is necessary and important for the planning activity. In this work, a throughput estimation model for cluster tools is proposed. The Multiple Regression Analysis is applied to develop a set of throughput estimation equations. A simulation model of cluster equipment including 3 single-wafer process chambers are built to get the historical throughput data for the regression analysis. From the Multiple Regression Analysis, it reveals that different numbers of recipes processed in the same time have to develop different regression model. The major factors in the regression model include numbers of load ports and process time of each recipe. Furthermore, a set of recipes are used to test the accuracy of estimation. Based on the testing results, they revealed that the MAPE is under 3% and the estimation model is accepted in practice to forecast the throughput of recipes for the planning activities.

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Automation and Integration in Semiconductor Manufacturing

Cited by 5 publications

References 12 publications

A Distributed-Ledger, Edge-Computing Architecture for Automation and Computer Integration in Semiconductor Manufacturing

A Distributed-Ledger, Edge-Computing Architecture for Automation and Computer Integration in Semiconductor Manufacturing

Short-Term Scheduling Model of Cluster Tool in Wafer Fabrication

Throughput Estimation Model of Cluster Tool in Semiconductor Manufacturing

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