Methods for referencing related magnetic head microscopy scans to reduce processing requirements for high resolution imaging

1. A method for referencing related magnetic head atomic force microscopy scans, the method comprising:

performing a low resolution pole tip recession scan of a pole tip area of a magnetic head;

performing a high resolution writer pole recession scan of a writer pole area of the magnetic head;

preparing a portion of the low resolution scan for alignment;

performing a rough leveling of the high resolution scan;

aligning the portion of the low resolution scan and the high resolution scan using pattern recognition and a database of features;

subtracting the high resolution scan from the aligned portion of the low resolution scan; and

leveling the high resolution scan based on a result of the subtraction.

2. The method of claim 1 :

wherein the performing the low resolution pole tip recession scan comprises:

leveling and referencing the low resolution pole tip recession scan to an air bearing surface of a pole tip within the low resolution pole tip recession scan;

wherein the preparing the portion of the low resolution scan for alignment comprises:

selecting a first sub-frame of the low resolution scan; and

generating a second sub-frame using interpolation of the first sub-frame, the second sub-frame comprising substantially the same resolution as the high resolution scan; and

wherein the performing the rough leveling of the high resolution scan comprises performing an approximate leveling of the high resolution scan.

3. The method of claim 2 , wherein the generating the second sub-frame using interpolation of the first sub-frame comprises using a two dimensional interpolation.

4. The method of claim 3 , wherein the using the two dimensional interpolation comprises using an interpolation technique selected from the group consisting of a cubic spline interpolation, linear interpolation, and combinations thereof.

5. The method of claim 2 , wherein the performing the approximate leveling of the high resolution scan comprises using a first order line-by-line filter.

6. The method of claim 5 , wherein the using the first order line-by-line filter comprises using a least squares fitting with a first order polynomial for each line in the high resolution scan.

7. The method of claim 2 , further comprising:

removing artifacts indicative of a thermal drift in the high resolution scan;

wherein the aligning the portion of the low resolution scan and the high resolution scan using pattern recognition and the database of features comprises:

performing a pattern recognition to locate a position of a feature in the high resolution scan;

performing a pattern recognition to locate a position of a feature in the second sub-frame;

determining an offset between the position of the feature in the high resolution scan and the position of the feature in the second sub-frame; and

selecting a third sub-frame of the low resolution scan, wherein a center of the third sub-frame is adjusted during the selecting the third sub-frame to substantially eliminate the offset;

wherein the subtracting the high resolution scan from the aligned portion of the low resolution scan comprises subtracting the high resolution scan from the third sub-frame to form an error dataset; and

wherein the leveling the high resolution scan based on the result of the subtraction comprises performing a leveling of the high resolution scan using the error dataset.

8. The method of claim 7 , wherein the feature is a writer pole.

9. The method of claim 7 , wherein the removing the artifacts indicative of the thermal drift in the high resolution scan comprises:

receiving an original image for correction, the original image having been generated using microscopy;

receiving information indicative of a feature selected from within the original image by a user, the selected feature comprising an edge;

storing the original image in a database comprising a plurality of images, each comprising one or more features;

correlating the selected feature with one of the one or more features stored in the database to identify a first plurality of points defining the edge;

removing one or more points of the first plurality of points using an outlier rejection technique;

generating a smoothing spline approximation for a second plurality of points defining the edge; and

generating a corrected image by shifting points of the original image in accordance with the smoothing spline approximation.

10. The method of claim 7 , wherein the performing the pattern recognition to locate the position of the writer pole in the high resolution scan comprises using a normalized cross correlation matching to correlate the position of the feature in the high resolution scan and a position of a corresponding feature in a reference scan stored in the database of writer pole scans.

11. The method of claim 7 , wherein the performing the leveling of the high resolution scan using the error dataset comprises:

performing a line by line leveling of the high resolution scan; and

performing a de-tilt plane leveling of the high resolution scan.

12. The method of claim 11 , wherein the performing the line by line leveling of the high resolution scan comprises:

performing a zero order line by line leveling on the error dataset; and

subtracting values of the leveled error dataset from lines of the high resolution scan.

13. The method of claim 11 , wherein the performing the de-tilt plane leveling of the high resolution scan comprises:

performing a function on the error dataset, the function selected from the group consisting of a first order two dimensional plane fitting on the error dataset and a two dimensional smoothing spline approximation on the error dataset; and

subtracting a result of the function from the high resolution scan.

14. The method of claim 7 , wherein the method is performed on a general purpose computer using software configured to perform complex mathematical computations.

15. The method of claim 7 , wherein the subtracting the high resolution scan from the third sub-frame to form an error dataset comprises substantially eliminating points relating to the feature in the high resolution scan and points relating to the feature in the second sub-frame.

16. The method of claim 1 , wherein the method is performed on a general purpose computer using software configured to perform complex mathematical computations.

17. The method of claim 1 , wherein the performing the low resolution pole tip recession scan comprises scanning the pole tip area with a substantially constant speed.

18. The method of claim 17 , wherein the performing the high resolution writer pole recession scan comprises scanning the writer pole area with a second substantially constant speed.

19. The method of claim 1 , wherein the performing the high resolution writer pole scan comprises scanning the writer pole area with a substantially constant speed.