Deep machine learning based Image classification in hard disk drive manufacturing

Narender Rana; Chester Chien

doi:10.1117/12.2300952

23 April 2018 Deep machine learning based Image classification in hard disk drive manufacturing

Narender Rana, Chester Chien

Proceedings Volume 10585, Metrology, Inspection, and Process Control for Microlithography XXXII; 105850Y (2018) https://doi.org/10.1117/12.2300952
Event: SPIE Advanced Lithography, 2018, San Jose, California, United States

Abstract

A key sensor element in a Hard Disk Drive (HDD) is the read-write head device. The device is complex 3D shape and its fabrication requires over thousand process steps with many of them being various types of image inspection and critical dimension (CD) metrology steps. In order to have high yield of devices across a wafer, very tight inspection and metrology specifications are implemented. Many images are collected on a wafer and inspected for various types of defects and in CD metrology the quality of image impacts the CD measurements. Metrology noise need to be minimized in CD metrology to get better estimate of the process related variations for implementing robust process controls. Though there are specialized tools available for defect inspection and review allowing classification and statistics. However, due to unavailability of such advanced tools or other reasons, many times images need to be manually inspected. SEM Image inspection and CD-SEM metrology tools are different tools differing in software as well. SEM Image inspection and CD-SEM metrology tools are separate tools differing in software and purpose. There have been cases where a significant numbers of CD-SEM images are blurred or have some artefact and there is a need for image inspection along with the CD measurement. Tool may not report a practical metric highlighting the quality of image. Not filtering CD from these blurred images will add metrology noise to the CD measurement. An image classifier can be helpful here for filtering such data. This paper presents the use of artificial intelligence in classifying the SEM images. Deep machine learning is used to train a neural network which is then used to classify the new images as blurred and not blurred. Figure 1 shows the image blur artefact and contingency table of classification results from the trained deep neural network. Prediction accuracy of 94.9 % was achieved in the first model. Paper covers other such applications of the deep neural network in image classification for inspection, review and metrology.

Conference Presentation

Citation Download Citation

Narender Rana and Chester Chien "Deep machine learning based Image classification in hard disk drive manufacturing", Proc. SPIE 10585, Metrology, Inspection, and Process Control for Microlithography XXXII, 105850Y (23 April 2018); https://doi.org/10.1117/12.2300952

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