Dual beam focused ion beam/scanning electron microscopy (FIB/SEM) is a critical characterization technique that is used as inline metrology from early stages of process developments until high volume manufacturing (HVM) of magnetic read/write heads in hard disk drive (HDD) due to the complex three-dimensional geometry . Despite its destructive nature, FIB/SEM metrology is critical to support high throughout manufacture process for advanced process control during HVM in HDD industry. Final cross-sectional SEM images typically include several CD measurements and embedded or standalone standard machine vision applications are used as part of the metrology process. However, these applications are typically not able to accommodate various process changes during the rapid process development, and manual engineer assistance are often needed for the accurate cut placement and SEM search. On the other hand, optimization of machine vision application typically requires a reasonable number of images to allow training and optimization of edge finder and pattern recognition functions. Reducing the training and optimization time needed for machine vision applications reduces the learning time during new process development. In this work, we are introducing a machine learning based metrology application that minimizes the need for engineer involvement for recipe optimization during the rapid process development . By addition of the process margin entities to the machine learning model, the recipe robustness is significantly improved at the time of transition to new product introduction (NPI) and high volume manufacturing (HVM). We compare the new machine learning based metrology application against the legacy machine vision application and study its impact on recipe writing time, wafer to wafer variations, and total measurement uncertainty (TMU). The new application allows recipes capable of cross-design metrology.
In contrast to semiconductor manufacturing where features are mostly lines or contact holes, the disk drive reader has a complex, nonlinear 3D geometry. Metrology of such geometries is challenging; especially with regard to repeatability of measurements. New methods were needed to keep up with production requirements for metrology regarding uncertainty of critical dimensions (CD). We report a new method developed for CD metrology of the disk drive writer pole. The method demonstrated improved uncertainties compared to the regularly used CD-SEM algorithms and also has capability for side wall angle (SWA) metrology for process control.
The method utilizes multiple steps: a) extract contours from SEM images, b) identify exact locations on a curvilinear feature where CD should be measured, and c) provide CD measurements at these locations. SEM images from a variety of production wafers were used for evaluation of the developed method. Multiple series of SEM images were processed using a software utilizing advanced algorithms without using regular brightness threshold CD-SEM methodologies. It was found that CD repeatability was improved by a factor of three compared to the results of the regular threshold based CD-SEM method.
TEM imaging is used to measure side wall angle; it takes a lot of efforts for sample preparation and the feedback is slow. If extraction of SWA is possible from top down SEM images it would provide instant feedback to manufacturing and reduce cost. Monte Carlo simulations were used to understand the sensitivity of SWA to the trench CD and depth. Height of features was measured using AFM. A method to extract SWA from top down images was developed. Numerous SEM images were processed; results were compared to experiment and analyzed. It was shown that the 3-sigma repeatability of SWA measurement was 0.15 degree. It was also found that the left and the right SWA were different on multiple wafers, the results were very consistent from one image series to another one, at the same time the SWA difference between the left and right walls was considerably larger than the uncertainty of SWA measurement.