METHOD AND APPARATUS OF AUTOMATIC SCANNING PROBE IMAGING

• US 20090077697A1
• Filed: 09/12/2008
• Published: 03/19/2009
• Est. Priority Date: 09/12/2007
• Status: Active Grant

• Current Assignee: Bruker Nano, Inc. (Bruker Corporation)
• Original Assignee: Bruker Nano, Inc. (Bruker Corporation)
• Inventors: Su, Chanmin, Belikov, Sergey
• Granted Patent: US 7,865,966 B2
• Time in Patent Office: Days
• US Class Current: 850/52
• CPC Class Codes: G01Q 10/06 Circuits or algorithms therefor
• CPC Class Codes: G01Q 30/04 Display or data processing devices
• CPC Class Codes: Y10S 977/85 Scanning probe control process

Abstract

A method of operating a scanning probe microscope (SPM) includes scanning a sample as a probe of the SPM interacts with a sample, and collecting sample surface data in response to the scanning step. The method identifies a feature of the sample from the sample surface data and automatically performs a zoom-in scan of the feature based on the identifying step. The method operates to quickly identify and confirm the location of features of interest, such as nano-asperities, so as to facilitate performing a directed high resolution image of the feature.

Claims

1. A method of operating a scanning probe microscope (SPM) to identify and provide greater resolution for one or more sample features, the method including:

scanning a sample at a first resolution as a probe of the SPM interacts with the sample;

collecting sample surface data in response to the scanning step;

during collection of the sample surface data,

identifying a sub-section of the sample including a feature detected based on the sample surface data using at least two identification parameters; and

automatically scanning the sub-section of the sample at a second resolution as a probe of the SPM interacts with the sub-section of the sample, the second resolution being greater than the first resolution.

2. The method of claim 1, wherein the identifying step includes confirming an authenticity of the feature.

3. The method of claim 2, wherein the confirming step includes analyzing trace and re-trace data sets of the sample surface data.

4. The method of claim 3, wherein the data sets include at least one signal corresponding to feature height, probe amplitude, probe phase, probe harmonic response, probe deflection, and friction, electric response and magnetic response between a probe tip expand and the sample.

5. The method of claim 2, wherein the confirming step is based on a principle component analysis of a neural network.

6. The method of claim 2, wherein the confirming step includes comparing a known pattern with the feature using pattern recognition analysis.

7. The method of claim 1, further including changing one or more parameters of the SPM operation based on one or more characteristics of the detected feature prior to scanning at the second resolution.

8. The method of claim 1, wherein identifying a sub-section of the sample includes identifying a sub-section of the samples including a nano-asperity.

9. The method of claim 1, wherein the second resolution is determined based on one or more characteristics of the detected feature.

10. The method of claim 1, wherein identifying a sub-section of the sample includes performing a retrace operation to eliminate detected features based on noise.

11. A probe-based instrument for identifying and providing greater resolution for one or more sample features, the instrument comprising:

a scanning probe microscope (SPM), the SPM including a probe having a cantilever bearing a tip;

a feature identification engine configured to identify a sub-section of the sample, the sub-section including a feature detected based on the sample surface data using at least two identification parameters; and

a feature scanning controller, the feature scanning controller configured to provide control signals to the SPM to scan the sub-section of the sample at a second resolution as a probe of the SPM interacts with the sub-section of the sample, the second resolution being greater than the first resolution.

12. The instrument of claim 11, wherein identifying a sub-section of the sample includes confirming an authenticity of the feature.

13. The instrument of claim 12, wherein confirming an authenticity of the feature includes analyzing trace and re-trace data sets of the sample surface data.

14. The instrument of claim 13, wherein the data sets include at least one signal corresponding to feature height, probe amplitude, probe phase, probe harmonic response, probe deflection, and friction, electric response and magnetic response between a probe tip expand and the sample.

15. The instrument of claim 12, wherein confirming an authenticity of the feature is based on a principle component analysis of a neural network.

16. The instrument of claim 12, wherein confirming an authenticity of the feature includes comparing a known pattern with the feature using pattern recognition analysis.

17. The instrument of claim 11, wherein the feature scanning controller is configured to change one or more parameters of the SPM operation based on one or more characteristics of the detected feature prior to scanning at the second resolution.

18. A method of operating a scanning probe microscope (SPM) to identify and provide greater resolution for one or more sample features, the method including:

scanning a sample to generate sample surface data at a first resolution based on interaction between a cantilevered probe of the SPM and the sample;

identifying a sub-section of the sample including a feature detected based on the sample surface data;

verifying the sub-section based on at least two verification tests including at least one of feedback inverse correlation testing, pattern match testing, and reverse symmetry testing; and

scanning the verified sub-section of the sample at a second resolution as a probe of the SPM interacts with the sub-section of the sample, the second resolution being greater than the first resolution.

19. The method of claim 18, wherein the identifying step includes confirming an authenticity of the feature.

20. The method of claim 19, wherein the confirming step includes analyzing trace and re-trace data sets of the sample surface data.

21. The method of claim 20, wherein the data sets include at least one signal corresponding to feature height, probe amplitude, probe phase, probe harmonic response, probe deflection, and friction, electric response and magnetic response between a probe tip expand and the sample.

22. The method of claim 19, wherein the confirming step is based on a principle component analysis of a neural network.

23. The method of claim 19, wherein the confirming step includes comparing a known pattern with the feature using pattern recognition analysis.

24. The method of claim 18, further including changing one or more parameters of the SPM operation based on one or more characteristics of the detected feature prior to scanning at the second resolution.

25. A method of operating a scanning probe microscope (SPM) to identify and provide greater resolution for one or more sample features, the method including:

performing a survey scan of a sample at a frequency of at least 5 Hz to generate sample surface data at a first resolution based on interaction between a probe of the SPM and the sample;

identifying a sub-section of the sample including a feature having a height greater than about 1 nanometer;

verifying the sub-section to identify the feature from noise using at least two verification tests including at least one of feedback inverse correlation testing, pattern match testing, and reverse symmetry testing; and

performing a data scan of the verified sub-section of the sample at a second resolution as a probe of the SPM interacts with the sub-section of the sample, the second resolution being greater than the first resolution.

26. The method of claim 25, wherein the verifying step includes analyzing trace and re-trace data sets of the sample surface data to eliminate sub-sections where the feature does not have spatial recurrence.

27. The method of claim 26, further including identifying hysteresis in the trace and re-trace data sets such that spatial recurrence is defined according to a range based on the identified hysteresis.

28. The method of claim 26, wherein the data sets include at least one signal corresponding to feature height, probe amplitude, probe phase, probe harmonic response, probe deflection, and friction, electric response and magnetic response between a probe tip expand and the sample.

29. The method of claim 25, wherein the verifying step is based on a principle component analysis of a neural network.

30. The method of claim 25, wherein the verifying step includes comparing a known pattern with the feature using pattern recognition analysis.

31. The method of claim 25, further including changing one or more parameters of the SPM operation based on one or more characteristics of the detected feature prior to scanning at the second resolution.