| Author: | Fok, Ka-keung |
| Title: | Using a Neural-Fuzzy modeling approach for intelligent process control of manufacturing Perpendicular Magnetic Recording heads |
| Degree: | Eng.D. |
| Year: | 2011 |
| Subject: | Magnetic recorders and recording -- Heads Intelligent control systems Hong Kong Polytechnic University -- Dissertations |
| Department: | Faculty of Engineering |
| Pages: | 1 v. (various pagings) : ill. ; 30 cm. |
| Language: | English |
| Abstract: | The work presented in this thesis is related to a proposed Neural-Fuzzy model applied to the control of manufacturing Perpendicular Magnetic Recording (PMR) heads, with aim of improving the process yield, process lead time and product quality. A Performance Trade-off Function is used to measure the effectiveness of this model based on the magnitude of the reduction of the cost of material and reduction of the loss of quality. The increasing demand for Hard Disk Drive (HOD) products with ever greater capacity and lower cost is forcing manufacturers to produce a key component, the magnetic recording head, which is capable of achieving higher areal density at a lower manufacturing cost. PMR head is one of the key recording head technologies which can deliver products with higher areal density. However, the complicated structure of the PMR head results in increasing difficulty in controlling the Magnetic Write Width (MWW) target to maintain a stable Dynamic Electrical Test (DET) yield. The industry is currently using a pretest feedback system to determine the process settings to improve the accuracy of the MWW targeting and to increase the manufacturing yield. However, the major drawback with such a process is the significant increase in process lead time and at the same time the targeting accuracy is also affected by various factors (wafer uniformity, MWW to lapping process setting sensitivity assumption and testing accuracy). A Neural Fuzzy modeling approach (Neural Fuzzy Decision Support System, NFDSS) is introduced in this Thesis to replace the existing pretest feedback system in the industry as a better means for MWW targeting to provide a higher DET test yield, shorter process lead time and a better quality product. Instead of using the pretest result to feedback the required process settings and achieve the target MWW, a neural network trained by past manufacturing data is used to set up the rules for the fuzzy inference process to determine the required process settings for achieving the target MWW. A case study is performed in the company (SAE Magnetics) using this Neural Fuzzy modeling approach to control the manufacturing process of the PMR head and the result indicates that this approach offers a significant reduction in process lead time (reduce from 14 days to 7 days) as well as improvement in MWW mean control accuracy (from 2.6% off target to 0.2% off target) with similar manufacturing yield (87.9% Vs 88.7%). A Perfonnance Trade-off Function (PToF) is applied to measure the effectiveness of this approach as compared with the conventional blind lapping process and the existing pretest feedback system. The result also indicates that the proposed model provides the best average performance loss index (defined by a weighted average of cost of material loss and cost of quality loss). Finally the application of this approach to enhancing the product development process in the magnetic recording head industry is proposed as the future research direction in this industry. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b2412767x.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 20.71 MB | Adobe PDF | View/Open |
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