Scheduling of Batch Processors in Semiconductor Manufacturing – A Review

In this paper a review on scheduling of batch processors (SBP) in semiconductor manufacturing (SM) is presented. It classifies SBP in SM into 12 groups. The suggested classification scheme organizes the SBP in SM literature, summarizes the current research results for different problem types. The classification results are presented based on various distributions and various methodologies applied for SBP in SM are briefly highlighted. A comprehensive list of references is presented. It is hoped that, this review will provide a source for other researchers/readers interested in SBP in SM research and help simulate further interest.Singapore-MIT Alliance (SMA

Simulation-Based Analysis on Operational Control of Batch Processors in Wafer Fabrication

[EN] In semiconductor wafer fabrication (wafer fab), wafers go through hundreds of process steps on a variety of processing machines for electrical circuit building operations. One of the special features in the wafer fabs is that there exist batch processors (BPs) where several wafer lots are processed at the same time as a batch. The batch processors have a significant influence on system performance because the repetitive batching and de-batching activities in a reentrant product flow system lead to non-smooth product flows with high variability. Existing research on the BP control problems has mostly focused on the local performance, such as waiting time at the BP stations. This paper attempts to examine how much BP control policies affect the system-wide behavior of the wafer fabs. A simulation model is constructed with which experiments are performed to analyze the performance of BP control rules under various production environments. Some meaningful insights on BP control decisions are identified through simulation results.This work was supported by the Pukyong National University Research Abroad Fund (C-D-2016-0843).Koo, P.; Ruiz García, R. (2020). Simulation-Based Analysis on Operational Control of Batch Processors in Wafer Fabrication. Applied Sciences. 10(17):1-17. https://doi.org/10.3390/app10175936S1171017Wang, L.-C., Chu, P.-C., & Lin, S.-Y. (2019). Impact of capacity fluctuation on throughput performance for semiconductor wafer fabrication. Robotics and Computer-Integrated Manufacturing, 55, 208-216. doi:10.1016/j.rcim.2018.03.005Ham, M. (2012). Integer programming-based real-time dispatching (i-RTD) heuristic for wet-etch station at wafer fabrication. International Journal of Production Research, 50(10), 2809-2822. doi:10.1080/00207543.2011.594816Mathirajan, M., & Sivakumar, A. I. (2006). A literature review, classification and simple meta-analysis on scheduling of batch processors in semiconductor. The International Journal of Advanced Manufacturing Technology, 29(9-10), 990-1001. doi:10.1007/s00170-005-2585-1FOWLER, J. W., HOGG, G. L., & PHILLIPS, D. T. (2000). Control of multiproduct bulk server diffusion/oxidation processes. Part 2: multiple servers. IIE Transactions, 32(2), 167-176. doi:10.1080/07408170008963889Van Der Zee, D. J. (2002). Adaptive scheduling of batch servers in flow shops. International Journal of Production Research, 40(12), 2811-2833. doi:10.1080/00207540210136559Wang, J., Zheng, P., & Zhang, J. (2020). Big data analytics for cycle time related feature selection in the semiconductor wafer fabrication system. Computers & Industrial Engineering, 143, 106362. doi:10.1016/j.cie.2020.106362Neuts, M. F. (1967). A General Class of Bulk Queues with Poisson Input. The Annals of Mathematical Statistics, 38(3), 759-770. doi:10.1214/aoms/1177698869Deb, R. K., & Serfozo, R. F. (1973). Optimal control of batch service queues. Advances in Applied Probability, 5(2), 340-361. doi:10.2307/1426040Gurnani, H., Anupindi, R., & Akella, R. (1992). Control of batch processing systems in semiconductor wafer fabrication facilities. IEEE Transactions on Semiconductor Manufacturing, 5(4), 319-328. doi:10.1109/66.175364Avramidis, A. N., Healy, K. J., & Uzsoy, R. (1998). Control of a batch-processing machine: A computational approach. International Journal of Production Research, 36(11), 3167-3181. doi:10.1080/002075498192355Fowler, J. W., Phojanamongkolkij, N., Cochran, J. K., & Montgomery, D. C. (2002). Optimal batching in a wafer fabrication facility using a multiproduct G/G/c model with batch processing. International Journal of Production Research, 40(2), 275-292. doi:10.1080/00207540110081489Glassey, C. R., & Weng, W. W. (1991). Dynamic batching heuristic for simultaneous processing. IEEE Transactions on Semiconductor Manufacturing, 4(2), 77-82. doi:10.1109/66.79719Fowler, J. W., Phillips, D. T., & Hogg, G. L. 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International Journal of Production Research, 33(7), 1849-1869. doi:10.1080/00207549508904785NEALE, J. J., & DUENYAS, I. (2000). Control of manufacturing networks which contain a batch processing machine. IIE Transactions, 32(11), 1027-1041. doi:10.1080/07408170008967459SOLOMON, L., FOWLER, J. W., PFUND, M., & JENSEN, P. H. (2002). THE INCLUSION OF FUTURE ARRIVALS AND DOWNSTREAM SETUPS INTO WAFER FABRICATION BATCH PROCESSING DECISIONS. Journal of Electronics Manufacturing, 11(02), 149-159. doi:10.1142/s0960313102000370Çerekçi, A., & Banerjee, A. (2015). Effect of upstream re-sequencing in controlling cycle time performance of batch processors. Computers & Industrial Engineering, 88, 206-216. doi:10.1016/j.cie.2015.07.005Yeong-Dae, K., Dong-Ho, L., Jung-Ug, K., & Hwan-Kyun, R. (1998). A simulation study on lot release control, mask scheduling, and batch scheduling in semiconductor wafer fabrication facilities. Journal of Manufacturing Systems, 17(2), 107-117. doi:10.1016/s0278-6125(98)80024-1Bahaji, N., & Kuhl, M. E. (2008). A simulation study of new multi-objective composite dispatching rules, CONWIP, and push lot release in semiconductor fabrication. International Journal of Production Research, 46(14), 3801-3824. doi:10.1080/00207540600711879Li, Y., Jiang, Z., & Jia, W. (2013). An integrated release and dispatch policy for semiconductor wafer fabrication. International Journal of Production Research, 52(8), 2275-2292. doi:10.1080/00207543.2013.854938SPEARMAN, M. L., WOODRUFF, D. L., & HOPP, W. J. (1990). CONWIP: a pull alternative to kanban. International Journal of Production Research, 28(5), 879-894. doi:10.1080/00207549008942761Wein, L. M. (1988). Scheduling semiconductor wafer fabrication. IEEE Transactions on Semiconductor Manufacturing, 1(3), 115-130. doi:10.1109/66.4384Glassey, C. R., & Resende, M. G. C. (1988). Closed-loop job release control for VLSI circuit manufacturing. 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Scheduling in an assembly-type production chain with batch transfer

Author name used in this publication: T. C. E. Cheng2006-2007 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Batching Problems with Constraints

There is an increasing demand for a phenomenon that can manifest benefits gained from grouping similar jobs together and then scheduling these groups efficiently. Batching is the decision of whether or not to put the jobs into same group based on certain criteria. Batching plays a major role in job scheduling in Information Technology, traffic controlling systems, and goods-flow management. A list batching problem refers to batching a list of jobs in the same order or priority as given in the problem. In this thesis we consider a one-machine list batching problem under weighted average completion. Given sequence of jobs are scheduled on single machine into distinct batches. Constraint is to batch these jobs into a fixed but arbitrary number ‘k’ of batches. Each batch can have any number of jobs (within the given list) grouped without changing the order of jobs. We call it a k-Batch problem. This is offline form of the batching problems, and is solved by reducing to a shortest path problem. We give an improved and faster version of the algorithm to solve k-Batch problem in O(n2) time

Two-machine flowshop batching and scheduling

Author name used in this publication: T. C. E. Cheng2004-2005 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Performance of a Serial-Batch Processor System with Incompatible Job Families under Simple Control Policies

A typical example of a batch processor is the diffusion furnace used in wafer fabrication facilities (otherwise known as wafer fabs). In diffusion, silicon wafers are placed inside the furnace, and dopant is flown through the wafers via nitrogen gas. The higher the temperature, the faster the dopant penetrates the wafer surface. Then, a thin layer of silicon dioxide is grown, to help the dopant diffuse into the silicon. This operation can take 10 hours or more to finish processing, as compared to one or two hours for other wafer fab operations, according to Uzsoy [8]. Diffusion furnaces typically can process six to eight lots concurrently; we call the lots processed concurrently a batch. The quantity of lots loaded into the furnace does not affect the processing time. Only lots that require the same chemical recipe and temperature may be batched together at the diffusion furnace. We wish to control the production of a manufacturing system, comprised of a serial processor feeding the batch processor. The system produces different job types, and each job can only be batched together with jobs of the same type. More specifically, we explore the idea of controlling the production of the serial processor, based on the wip found in front of the batch processor. We evaluate the performance of our manufacturing system under several simple control policies under a range of loading conditions and determine which control policies perform better under which conditions. It is hoped that the results obtained from this small system could be extended to larger systems involving a batch processor, with particular emphasis placed on the applicability of such policies in wafer fabrication.Singapore-MIT Alliance (SMA

Serial-batch scheduling – the special case of laser-cutting machines

The dissertation deals with a problem in the field of short-term production planning, namely the scheduling of laser-cutting machines. The object of decision is the grouping of production orders (batching) and the sequencing of these order groups on one or more machines (scheduling). This problem is also known in the literature as "batch scheduling problem" and belongs to the class of combinatorial optimization problems due to the interdependencies between the batching and the scheduling decisions. The concepts and methods used are mainly from production planning, operations research and machine learning

Mathematical Models for a Batch Scheduling Problem to Minimize Earliness and Tardiness

Purpose: Today’s manufacturing facilities are challenged by highly customized products and just in time manufacturing and delivery of these products. In this study, a batch scheduling problem has been addressed to enable on-time completion of customer orders in a lean manufacturing environment. The problem is optimizing the partitioning of product components into batches and scheduling of the resulting batches where each customer order is received as a set of products made of various components. Design/methodology/approach: Three different mathematical models for minimization of total earliness and tardiness of customer orders are developed to provide on-time completion of customer orders and also, to avoid excess final product inventory. The first model is a non-linear integer programming model whereas the second is a linearized version of the first. Finally, to solve larger sized instances of the problem, an alternative linear integer model is presented. Findings: Computational study using a suit set of test instances showed that the alternative linear integer model is able to solve all test instances in varying sizes within quite shorter computer times compared to the other two models. It has also been showed that the alternative model is able to solve moderate sized real-world problems. Originality/value: The problem under study differentiates from existing batch scheduling problems in the literature owing to the inclusion of new circumstances that are present in real-world applications. Those are: customer orders consisting of multi-products made of multi-parts, processing of all parts of the same product from different orders in the same batch, and delivering the orders only when all related products are completed. This research also contributes to the literature of batch scheduling problem by presenting new optimization models.Peer Reviewe