An algorithm to convert wafer to calendar-based preventive maintenance schedules for semiconductor manufacturing systems

2004 ◽  
Author(s):  
J.A. Ramirez-Hernandez ◽  
E. Fernandez-Gaucherand
Keyword(s):  
Preventive Maintenance ◽  
Semiconductor Manufacturing ◽  
Manufacturing Systems ◽  
Maintenance Schedules

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.

Reliability/risk centered cost effective preventive maintenance planning of generating units

2018 ◽  
Vol 35 (9) ◽  
pp. 2052-2079 ◽  
Author(s):  
Umamaheswari E. ◽  
Ganesan S. ◽  
Abirami M. ◽  
Subramanian S.
Keyword(s):  
Preventive Maintenance ◽  
Optimization Technique ◽  
Solution Space ◽  
Fuzzy Decision Making ◽  
Content Type ◽  
Multi Objective ◽  
Statistical Measures ◽  
Highly Nonlinear ◽  
Optimal Maintenance ◽  
Maintenance Schedules

Purpose Finding the optimal maintenance schedules is the primitive aim of preventive maintenance scheduling (PMS) problem dealing with the objectives of reliability, risk and cost. Most of the earlier works in the literature have focused on PMS with the objectives of leveling reserves/risk/cost independently. Nevertheless, very few publications in the current literature tackle the multi-objective PMS model with simultaneous optimization of reliability, and economic perspectives. Since, the PMS problem is highly nonlinear and complex in nature, an appropriate optimization technique is necessary to solve the problem in hand. The paper aims to discuss these issues. Design/methodology/approach The complexity of the PMS problem in power systems necessitates a simple and robust optimization tool. This paper employs the modern meta-heuristic algorithm, namely, Ant Lion Optimizer (ALO) to obtain the optimal maintenance schedules for the PMS problem. In order to extract best compromise solution in the multi-objective solution space (reliability, risk and cost), a fuzzy decision-making mechanism is incorporated with ALO (FDMALO) for solving PMS. Findings As a first attempt, the best feasible maintenance schedules are obtained for PMS problem using FDMALO in the multi-objective solution space. The statistical measures are computed for the test systems which are compared with various meta-heuristic algorithms. The applicability of the algorithm for PMS problem is validated through statistical t-test. The statistical comparison and the t-test results reveal the superiority of ALO in achieving improved solution quality. The numerical and statistical results are encouraging and indicate the viability of the proposed ALO technique. Originality/value As a maiden attempt, FDMALO is used to solve the multi-objective PMS problem. This paper fills the gap in the literature by solving the PMS problem in the multi-objective framework, with the improved quality of the statistical indices.

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