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

Reliability Analysis of Nanocrystal Embedded High-k Nonvolatile Memories

The evolution of the MOSFET technology has been driven by the aggressive shrinkage of the device size to improve the device performance and to increase the circuit density. Currently, many research demonstrated that the continuous polycrystalline silicon film in the floating-gate dielectric could be replaced with nanocrystal (nc) embedded high-k thin film to minimize the charge loss due to the defective thin tunnel dielectric layer. This research deals with both the statistical aspect of reliability and electrical aspect of reliability characterization as well. In this study, the Zr-doped HfO2 (ZrHfO) high-k MOS capacitors, which separately contain the nanocrystalline zinc oxide (nc-ZnO), silicon (nc-Si), Indium Tin Oxide (nc-ITO) and ruthenium (nc-Ru) are studied on their memory properties, charge transportation mechanism, ramp-relax test, accelerated life tests, failure rate estimation and thermal effect on the above reliability properties. C-V hysteresis result show that the amount of charges trapped in nanocrystal embedded films is in the order of nc-ZnO\u3enc-Ru\u3enc-Si~nc-ITO, which might probably be influenced by the EOT of each sample. In addition, all the results show that the nc-ZnO embedded ZrHfO non-volatile memory capacitor has the best memory property and reliability. In this study, the optimal burn-in time for this kind of device has been also investigated with nonparametric Bayesian analysis. The results show the optimal burn-in period for nc-ZnO embedded high-k device is 5470s with the maximum one-year mission reliability

QUALITY AND PRODUCTIVITY IMPROVEMENTS IN ADDITIVE MANUFACTURING

Additive manufacturing (AM) is a relatively new manufacturing technology compared to the traditional manufacturing methods. Even though AM processes have many advantages, they also have a series of challenges that need to be addressed to adapt this technology for a wide range of applications and mass production. AM faces a number of challenges, including the absence of methods/models for determining whether AM is the best manufacturing process for a given part. The first study of this thesis proposes a framework for choosing specific AM processes by considering the complexity level of a part. It has been proven that the method works effectively through numerical experiments. Optimization of process parameters through expensive and time-consuming experiments is another issue with AM. To address this issue, an empirical model is presented in the second study to optimize parameters for minimizing building costs through maximizing the trade-off between productivity and quality. The proposed model proves to be effective in reducing building costs at any quality level. The results indicate that process parameters can be optimized quickly and accurately, as compared to the time-consuming and expensive experimental methods. Another limitation of AM is the lack of capability to use multiple materials, which is a concern when adapting this technology to mass production. To address this issue, a new scheduling model with considering multi-material types is introduced in the third study. Based on the numerical results, the proposed model can provide optimal sequence by maximizing the trade-off between tardiness and material switching cost

Dynamic Control for Batch Process Systems Using Stochastic Utility Evaluation

Most research studies in the batch process control problem are focused on optimizing system performance. The methods address the problem by minimizing single criterion such as cycle time and tardiness, or bi-criteria such as cycle time and tardiness, and earliness and tardiness. This research demonstrates the use of Stochastic Utility Evaluation (SUE) function approach to optimize system performance using multiple criteria. In long production cycles, the earliness and tardiness weight (utility) of products vary depending on the time. As the time approaches the due-date, it affects contractual penalties, loss of customer goodwill and the storage period for the completed products. It is necessary to reflect the weight of products for earliness and tardiness at decision epochs to decide on the optimal strategy. This research explores how stochastic utility function using stochastic information can be derived and used to strategically improve existing approaches for the batch process control problem. This research first explores how SUE function can be applied to existing model for bi-objective problem such as cycle time and tardiness. Benchmark strategies using SUE function (NACH-SUE, MBS-SUE, No idle and full batch) are compared to each other. The experimental results show that NACH-SUE effectively improves mean cycle time and tardiness performance respectively than other benchmark strategies. Next, SUE function for earliness and tardiness is used in an existing model to develop a tri-objective problem. Typically, this problem is very complex to solve due to its trade-off relationship. However SUE function makes it relatively easy to solve the tri-objective problem since SUE function can be incorporated in an existing model. It is observed that SUE function can be effectively used for solving a tri-objective problem. Performance improvement for averaged value of cycle time, earliness and tardiness is observed under a comprehensive set of experimental conditions

Dynamic Control of Serial-batch Processing Systems

This research explores how near-future information can be used to strategically control a batch processor in a serial-batch processor system setting. Specifically, improved control is attempted by using the upstream serial processor to provide near-future arrival information to the batch processor and further meet the re-sequencing requests to shorten critical products? arrival times to the batch processor. The objective of the research is to reduce mean cycle time and mean tardiness of the products being processed by the serial-batch processor system. This research first examines how mean cycle time performance of the batch processor can be improved by an upstream re-sequencing approach. A control strategy is developed by combining a look-ahead control approach with an upstream re-sequencing approach and is then compared with benchmark strategies through simulation. The experimental results indicate that the new control strategy effectively improves mean cycle time performance of the serial-batch processor system, especially when the number of product types is large and batch processor traffic intensity is low or medium. These conditions are often observed in typical semiconductor manufacturing environments. Next, the use of near-future information and an upstream re-sequencing approach is investigated for improving the mean tardiness performance of the serial-batch processor system. Two control strategies are devised and compared with the benchmark strategies through simulation. The experimental results show that the proposed control strategies improve the mean tardiness performance of the serial-batch processor system. Finally, the look-ahead control approaches that focus on mean cycle time and mean tardiness performances of the serial-batch processor system are embedded under a new control strategy that focuses on both performance measures simultaneously. It is demonstrated that look-ahead batching can be effectively used as a tool for controlling batch processors when multiple performance measures exist

Quality embedded intelligent remanufacturing

This thesis is motivated from the four keywords: remanufacturing, quality, multi-agent and intelligence. Recent years' environmental problems caused tightening the regulations and legislations for used products. Therefore remanufacturing is getting more attention. The quality of used products is uncertain and even dynamically changes during the remanufacturing process, and each used product should be individually handled in a different way depending on its quality. Fortunately recent developing wireless technologies like radio frequency identification (RFID) may enable remanufacturing control systems to identify, track, and control each used product and disassembled subassembly/part (PDSP) automatically. The multi-agent approach can be a good solution for the individual control of each PDSP, because a centralized control system is not eligible to managing so many elements in the remanufacturing system. The objective of this thesis is to propose a quality embedded remanufacturing system (QRS) which comprises a multi-agent framework and a scheduling mechanism. First, this thesis discusses the fundamental concepts for the proposed modeling tools and scheduling mechanism: the QRS quality characteristics and the multi-agent framework. As the second step, this thesis proposes QRS modeling tools which support the PDSP/resource quality representation and comprise: intuitive remanufacturing system representation (IRSR) and dynamic token two-level colored Petri-nets (DTPN). The former is designed from the user-side perspective and the latter is from the system-side perspective. The multi-agent framework is constructed based on the model represented with the proposed tools. Last, this thesis proposes a real-time scheduling mechanism for the QRS which enables the constructed framework to execute. The scheduling mechanism embeds a communication protocol among agents and dispatching rules formulated depending on the PDSP/resource quality. A knowledge-based approach is adopted to increase efficiency of the scheduling mechanism, where the knowledge is learned by simulations. A heuristic method is also proposed to reduce the simulation time