This paper presents a novel approach by combining SOM and fuzzy rule base for flow time prediction in semiconductor manufacturing factory. Flow time of a new order is highly related to the shop floor status; however, the semiconductor manufacturing processes are highly complicated and involve more than hundred of production steps. There is no governing function identified so far among the flow time of a new order and these shop flow status. Therefore, a simulation model which mimics the production process of a real wafer fab located in Hsin-Chu Science-based Park of Taiwan is built and flow time and related shop floor status are collected and fed into the SOM for classification. Then, corresponding fuzzy rule base is selected and applied for flow time prediction. Genetic process is further applied to fine-tune the composition of the rule base. Finally, using the simulated data, the effectiveness of the proposed method is shown by comparing with other approaches.

Combining SOM and fuzzy rule base for flow time prediction in semiconductor manufacturing factory

This paper presents a fuzzy modeling method proposed by Wang and Mendel for generation of fuzzy rules using data generated from a simulated model that is built from a real factory located in Hsin-Chu science-based park of Taiwan, R.O.C. The fuzzy modeling method is further evolved by a genetic algorithm for due-date assignment problem in manufacturing. By using simulated data, the effectiveness of the proposed method is shown and compared with two other soft computing techniques: multi-layer perceptron neural networks and case-based reasoning. The comparative results indicate that the proposed method is consistently superior to the other two methods.

Evolving fuzzy rules for due-date assignment problem in semiconductor manufacturing factory

https://ir.nctu.edu.tw:443/bitstream/11536/27531/1/000184714000007.pdf

Heuristic PAC model for hybrid MTO and MTS production environment

Output time prediction is a critical task to a wafer fab (fabrication plant). To further enhance the accuracy of wafer lot output time prediction, the concept of input classification is applied to Chen's fuzzy back propagation network (FBPN) in this study by pre-classifying input examples with the self-organization map (SOM) classifier before they are fed into the FBPN. Examples belonging to different categories are then learned with the same FBPN but with different parameter values. Production simulation is also applied in this study to generate test examples. According to experimental results, the prediction accuracy of the proposed methodology was significantly better than those of two existing approaches, FBPN without example classification, and evolving fuzzy rules (EFR), in most cases by achieving a 15%-45% (and an average of 31%) reduction in the root-mean-squared-error (RMSE).

http://www.wseas.us/e-library/conferences/2006hangzhou/papers/531-265.pdf

A hybrid SOM-FBPN approach for output time prediction in a wafer fab

Due-date assignment (DDA) is the first important task of shop floor control in wafer fabrication. Due-date related performance is impacted by the quality of the DDA rules. Assigning order due dates and timely delivering the goods to the customer will enhance customer service and competitive advantage. A new methodology for lead-time prediction, artificial neural network (ANN) prediction is considered in this work. An ANN-based DDA rule combined with simulation technology and statistical analysis is developed. Besides, regression-based DDA rules for wafer fabrication are modelled as benchmarking. Whether neural networks can outperform conventional and regression-based DDA rules taken from the literature is examined. From the simulation and statistical results, ANN-based DDA rules perform a better job in due-date prediction. ANN-based DDA rules have a lower tardiness rate than the other rules. ANN-based DDA rules have better sensitivity and variance than the other rules. Therefore, if the wafer fab information is not difficult to obtain, the ANN-based DDA rule can perform better due-date prediction. The SFM_sep and JIQ in regression-based and conventional rules are better than the others.

https://ir.nctu.edu.tw:443/bitstream/11536/26821/1/000221468200019.pdf

Due-date assignment in wafer fabrication using artificial neural networks

High work-in-process (WIP) level, long manufacturing lead time, high lead time variation, and poor due date performance are the major problems for wafer fabrication factories. An order's due date relates to its releasing time and flow time. Due date performance will not be improved if WIP level is high and material flow is unstable. Too high WIP level cannot increase throughput but lengthens lead time. Find a suitable WIP level and then control the material release by fixed-WIP policy. In this paper, we construct a due date assignment model (DDAM) by using the simulation method and queuing theory. Observing the results of simulations, the lowest system WIP level corresponding to the desired up-time machine utilization rate of the bottleneck resource or the capacity constraint resource (CCR) and the shortest mean flow time can be found. To make the material flow stable, we also propose the methodology of determining a wafer's WIP level, daily moves, and flow time for each product type and for each circuitry layer; all of these can be used as the parameters for controlling flow time. Demonstration of the DDAM is provided with actual data. Comparing the performance of our DDAM with others, the results reveal that DDAM performs well in each performance criterion.

Cho-Ming Cheng

Papers

The design of due date assignment model and the determination of flow time control parameters for the wafer fabrication factories