A novel optimization method to automated wet-etch station scheduling in semiconductor manufacturing systems

2011 ◽  
Vol 35 (12) ◽  
pp. 2960-2972 ◽  
Author(s):  
Adrián M. Aguirre ◽  
Carlos A. Méndez ◽  
Pedro M. Castro
Keyword(s):  
Semiconductor Manufacturing ◽  
Manufacturing Systems ◽  
Optimization Method ◽  
Wet Etch

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

2020 ◽  
pp. 13-29
Author(s):  
Da-Yin Liao
Keyword(s):  
Semiconductor Manufacturing ◽  
Manufacturing Systems ◽  
Edge Computing ◽  
Legacy Systems ◽  
Software Framework ◽  
Computer Integration ◽  
Computing Architecture ◽  
Disruptive Technologies ◽  
Distributed Ledger ◽  
Operational Structure

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.

On-the-Fly Substrate Eccentricity Recognition for Robotized Manufacturing Systems

2005 ◽  
Vol 127 (1) ◽  
pp. 206-216 ◽  
Author(s):  
Martin Hosek ◽  
Jan Prochazka
Keyword(s):  
Optical Sensors ◽  
Semiconductor Manufacturing ◽  
Manufacturing Systems ◽  
Robotic Manipulator ◽  
End Effector ◽  
Transfer Path ◽  
The Difference ◽  
Manufacturing Applications ◽  
Actual Location

This paper describes a method for on-the-fly determination of eccentricity of a circular substrate, such as a silicon wafer in semiconductor manufacturing applications, carried by a robotic manipulator, where eccentricity refers to the difference between the actual location of the center of the substrate and its desired position on the end-effector of the robotic manipulator. The method utilizes a pair of external optical sensors located along the substrate transfer path. When moving a substrate along the transfer path, the robotic manipulator captures the positions and velocities of the end-effector at which the edges of the substrate are detected by the sensors. These data along with the expected radius of the substrate and the coordinates of the sensors are used to determine the eccentricity of the substrate. This information can be used by the robotic manipulator to compensate for eccentricity of the substrate when performing a place operation, resulting in the substrate being placed centered regardless of the amount and direction of the initial eccentricity. The method can also be employed to detect a defect, such as breakage, of a circular substrate and report an error condition which can abort or otherwise adjust operation of the robotic manipulator.

Simulation-Based Optimization of Electricity Demand Response for Sustainable Manufacturing Systems

2014 ◽  
Author(s):  
Leah Cuyler ◽  
Zeyi Sun ◽  
Lin Li
Keyword(s):  
Demand Response ◽  
Manufacturing Systems ◽  
Manufacturing Sector ◽  
Sustainable Manufacturing ◽  
Optimization Method ◽  
Electricity Demand ◽  
System Throughput ◽  
Commercial Building ◽  
Simulation Based ◽  
Simulation Based Optimization

Electricity demand response is considered a promising tool to balance the electricity demand and supply during peak periods. It can effectively reduce the cost of building and operating those peaking power generators that are only run a few hundred hours per year to satisfy the peak demand. The research on the electricity demand response implementation for residential and commercial building sectors has been very mature. Recently, it has also been extended to the manufacturing sector. In this paper, a simulation-based optimization method is developed to identify the optimal demand response decisions for the typical manufacturing systems with multiple machines and buffers. Different objectives, i.e. minimizing the power consumption under the constraint of system throughput, and maximize the overall earnings considering the tradeoff between power demand reduction and potential production loss, are considered. Different energy control decisions are analyzed and compared regarding the potential influence on the throughput of manufacturing system due to the different control actions adopted by throughput bottleneck machine.

scholarly journals Genetic Algorithm for Job Scheduling with Maintenance Consideration in Semiconductor Manufacturing Process

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Seungchul Lee ◽  
Jun Ni
Keyword(s):  
Genetic Algorithm ◽  
Semiconductor Manufacturing ◽  
Job Scheduling ◽  
Optimization Methods ◽  
Optimization Method ◽  
Scheduling Problems ◽  
Simulation Based ◽  
Sequencing Problems ◽  
Maintenance Activities ◽  
Single Cluster

This paper presents wafer sequencing problems considering perceived chamber conditions and maintenance activities in a single cluster tool through the simulation-based optimization method. We develop optimization methods which would lead to the best wafer release policy in the chamber tool to maximize the overall yield of the wafers in semiconductor manufacturing system. Since chamber degradation will jeopardize wafer yields, chamber maintenance is taken into account for the wafer sequence decision-making process. Furthermore, genetic algorithm is modified for solving the scheduling problems in this paper. As results, it has been shown that job scheduling has to be managed based on the chamber degradation condition and maintenance activities to maximize overall wafer yield.

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