Semiconductor Tool Petri Net Modeling for Sequence Optimization: Furnace and PVD Cluster Tool Cases

In the construction of a semiconductor fab., the cost of equipment is above 70% of the total cost. Therefore the production equipment performance is directly reflexes in the invest reward in the industry. This paper is focus on "semiconductor fab.", using the case of Furnace Tools and PVD Cluster tools as example to discuss how to assist equipment engineer to efficiently construct the tool sequence model and validate the significance of model. In the end, the model should be able to provide the optimal or near optimal sequence.
Designing a systematic guideline for tool sequence modeling method, that a general equipment engineers can construct reusable, modules to fast up the tool sequence modeling process. For this propose, the key challenges is (i) to describe the observed internal/external control logic and (ii) to validate, and quick localize error and revise the just constructed module or complete model and (iii) to convert the constructed model into the constraint form for optimization package usage. In respect of these challenges, in this paper suggested following methods, (1) Adopt the method designed by S.C.Chiou Petri Net PVD Cluster tool robot control logic modeling, which consists in the syntax description and function decomposition methodology. This method helps engineers efficiently construct modules; in this paper we enriched this method to specific furnace such as internal/external control logic, Queue time consideration. (2) Applying the software CPN tools to simulate the constructed models, by token flow and variable design to obtain the necessary information for behavior and performance analysis, as Petri Net has a graphic structure; the error can be located easily. (3) Using the method cooperated designed by Inotera, University of Connecticut and National Taiwan University to module and systematically convert the math form PN model into its constraint form.
In this paper, a PVD real fabric operation data is used to validate the constructed model, the error is within 1 sigma (about 2%), and the optimized sequence has the same behavior and performance as real data, it shows that this method can result an optimal sequence of fab. tools, and the real operation sequence is near optimal.