Qualification Management in Wafer Fabs: Optimization Approach and Simulation-Based Performance Assessment

Kopp, Denny; Mönch, Lars; Pabst, Detlef; Stehli, Marcel

doi:10.1109/tase.2019.2935469

Cited by 16 publications

(3 citation statements)

References 39 publications

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“…While these methods provide accurate estimates of solutions, they require high computational costs because of their high-precision modeling capability (Asmundsson et al, 2009;Fowler & Mönch, 2017). Therefore, many references only use simulation models to evaluate the solution obtained by mathematical models (Bang & Kim, 2010;Kopp et al, 2019;Rashmi & Mathirajan, 2018;Ziarnetzky, (2015). Some approaches (Missbauer, 2020;Wolosewicz et al, 2015;Albey & Bilge, 2011;Puergstaller & Missbauer, 2011;Kim & Lee, 2016;Kim & Kim, 2001) use the iterative simulation and linear programming method to solve release planning problems.…”

Section: Introductionmentioning

confidence: 99%

Multi-fidelity simulation optimization for production releasing in re-entrant mixed-flow shops

Zhang¹,

Guan²,

Yue³

2023

10.5267/j.ijiec

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This research focuses on production releasing and routing allocation problems in re-entrant mixed-flow shops. Since re-entrant mixed flow shops are complex and dynamic, many studies evaluate release plans by developing discrete event simulation models and selecting the optimal solution according to the estimation results. However, a high-accurate discrete event simulation model requires a lot of computation time. In this research, we develop an effective multi-fidelity optimization method to address product release planning problems for re-entrant mixed-flow shops. The proposed method combines the advantages of rapid evaluation of analytical models and accurate evaluation of simulation models. It conducts iterative optimization using a low-fidelity mathematical estimation model to find good solutions and searches for the optimal solution via a high-fidelity simulation estimation model. Computational results of large-scale production releasing and routing allocation problems illustrate that the proposed approach is good at addressing large-scale problems in re-entrant mixed-flow shops.

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Section: Introductionmentioning

confidence: 99%

Multi-fidelity simulation optimization for production releasing in re-entrant mixed-flow shops

Zhang¹,

Guan²,

Yue³

2023

10.5267/j.ijiec

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“…There is a mix of different processing modes, for instance, single-wafer processes and batch processes where multiple lots are processed together at the same time on the same tool. The wafer fabs are considered as a repeatably job shops, that is, the tools of a tool group are revisited by the same lot because the chips are built up layer by layer on the wafer surface (Kopp, 2020). This is a high Capital Expenditure industrial investment which would require USD$1.8 billion to USD$3 billion and the process technology is usually sustainable for more than 20 years (Rothrock, 2019; Leachman et al, 2007;Hahn, 2012).…”

Section: Introductionmentioning

confidence: 99%

Salicide Residues Defects Characterization and Reduction in Front-End Pre-Metal Cleaning Process Wafer Fabrication

Johann

2022

etj

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Wafer fabrication for integrated circuit is one of the most complicated process in semiconductor manufacturing industry. High yield is always the ultimate goal to achieve hence a good defect management is the key to ensure the goal is met. Salicide residue is a major defect wet etching process. The defect is contributing a total of 1% loss in overall wafer fab sort yield, that is an equivalent to USD$ 5 million loss per year. The objective for this research is to identify the root cause and determine the element of the residue defect in the wafer substrate at Salicide Pre-Clean wafer fabrication process. Once this has been achieved, next is to determine a novelty solution to reduce the residue defects and finally validate the effectiveness of the novel solution in reducing the salicide residue defects. An investigation of one factor at the time has been conducted with various experiments including screening all the hardware resources available by using ANOVA studies. The result has concluded that the salicide residue consists of carbon defect is observed after Salicide Pre-Clean step when the standard diluted hydrofluoric acid (dHF) is used by the wet station equipment to clean the product wafers. This study has discovered an innovated solution to minimize the chemical contact on the wafer has resulted in reducing the post clean defects residue by 80% and eliminate 1% of the total product sort yield loss issue.

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“…There are several studies on simulation-based optimization (SBO) in fab-production planning and scheduling problems [1]- [5]. For the production-plan decision, the previous studies formulate the planning problem using linear programming (LP, or a mixed integer programming) approaches to optimize the release quantities of wafer-lot in a period.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Abstraction-Level Conversion Framework for Accelerated Discrete-Event Simulation in Smart Semiconductor Manufacturing

et al. 2020

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Speeding up the simulation of discrete-event wafer-fabrication models is essential for fast decision-making to handle unexpected events in smart semiconductor manufacturing because decisionparameter optimization requires repeated simulation execution based on the current manufacturing situation. In this paper, we present a runtime abstraction-level conversion approach for discrete-event fab models to gain simulation speedup. During the simulation, if the fab's machine group model reaches a steady state, then the proposed method attempts to substitute this group model with a mean-delay model (MDM) as a high abstraction level model. The MDM abstracts detailed event-driven operations of subcomponents in the group into an average delay based on the queuing modeling, which can guarantee acceptable accuracy in predicting the performance of steady-state queuing systems. To detect the steadiness, the proposed abstraction-level converter (ALC) observes the queuing parameters of low-level groups to identify the statistical convergence of each group's work-in-progress (WIP) level. When a group's WIP level is converged, the output-to-input couplings between the models are revised to change a wafer-lot process flow from the low-level group to a MDM. When the ALC detects lot-arrival changes or any wafer processing status change (e.g., a machinedown), the high-level model is switched back to its corresponding low-level group model. During highto-low level conversion, the ALC generates dummy wafer-lot events to re-initialize the machine states. The proposed method was applied to various case studies of wafer-fab systems and achieved simulation speedups up to about 4 times with 0.6 to 8.3% accuracy degradations.

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Qualification Management in Wafer Fabs: Optimization Approach and Simulation-Based Performance Assessment

Cited by 16 publications

References 39 publications

Multi-fidelity simulation optimization for production releasing in re-entrant mixed-flow shops

Multi-fidelity simulation optimization for production releasing in re-entrant mixed-flow shops

Salicide Residues Defects Characterization and Reduction in Front-End Pre-Metal Cleaning Process Wafer Fabrication

Adaptive Abstraction-Level Conversion Framework for Accelerated Discrete-Event Simulation in Smart Semiconductor Manufacturing

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