This SBIR Phase 1 project will exploit a new type of neural net to improve the accuracy of images captured using scanning probe microscopes (SPMs). Scanning probe microscopes view materials down to atomic dimensions, and this ability is becoming increasingly important in many industries. A primary source of error in the images obtained from atomic force microscopes is caused by probe geometry. Current `probe calibration` systems yield some benefits but much more can be done. Probe calibration systems use mathematical models to compute the geometry of the probe based on measurements of known samples. If the microscope output indicated the contact between the surface of the probe and the known standard sample when a sample is scanned, then it would be possible to compute the probe shape from the measured sample geometry. In practice, however, this does not work well. The force sensed by the atomic force microscope does not depend only on the interaction between the surface of the probe and the sample. All of the forces involved depend non-linearly on the shape, size, magnetization, electrostatic charge, and on other characteristics of the probe. The Phase 1 effort will validate the feasibility of the fuzzy CMAC based error compensation approach for atomic force microscopes. The beauty of using a neural-net approach, and particularly a Fuzzy CMAC, is that it is not necessary to develop a closed form relationship between the measurements made on the standard sample and a model of the probe geometry. All that is necessary is to scan the standard sample providing enough learning sets to train the net. Likewise it is not necessary to develop closed form relations to exploit a computed error model to correct measured data. The output of the Fuzzy CMAC will generate the correction values in real-time based on the learned relationship between ideal and measured data. Scanning probe microscope sales are increasing worldwide at approximately 40 to 60% per year, and they are being used in an increasing number of industries. This technology will improve the accuracy of these machines and increase a user's ability to observe the atomic structure of whatever they are producing. This firm has a joint development agreement in place with one of the world's leading suppliers of SPMs, thereby providing a natural path to early commercialization.
