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Flexible medical endoscopes currently used in medicine have many problems and a fundamental tradeoffs. Either resolution or field of view is sacrificed when the scope diameter is less than 3 mm, since the minimum pixel size is usually at least 4 microns in a pixel-array such as a camera or fiber bundle. Previous work has shown the design of a micromachined cantilever beam is able to realize a 100 µm wide, one dimension scanning pattern. First mode resonances of the cantilever scanner are found between 16-52 kHz with response amplitudes ranging from 62.5 to 420 µm. Since cantilever waveguides with resonant frequencies above 20 kHz are potentially suitable for video rate scanning, these devices may be used for image acquisition and display. Described in this work is an alternative method of design for a micro-optical scanning endoscope. The endoscope consists of an optical waveguide microfabricated using SU-8 photoresist. SU-8 is a high contrast, negative tone, chemically amplified, epoxy based photoresist chosen for its high aspect ratio (~15:1) for imaging near vertical sidewalls. With the use of SU-8, we were able to fabricate a much larger waveguide (~85 µm) as compared to the previous silicon oxide method (~3 µm) An overall larger device makes coupling a fiber into the waveguide much easier and increases the amount of light coupled into the cantilever beams. The neagactively toned epoxy resin based SU-8 also increases the device durability and simplifies the fabrication process.

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A flexible high-resolution sensor capable of measuring the distribution of both shear and pressure at the plantar interface are needed to study the actual distribution of this force during daily activities, and the role that shear plays in causing plantar ulceration. We have previously developed a novel means of transducing plantar shear and pressure stress via a new microfabricated optical system. However, a force image algorithm is needed to handle the complexity of construction of two-dimensional planar pressure and shear images. Here we have developed a force image algorithm for a micromachined optical bend loss sensor. A neural network is introduced to help identify different load shapes. According to the experimental result, we can conclude that once the neural network has been well trained, it can correctly identify the loading shape. With the neural network, our micromachined optical bend loss Sensor is able to construction the two-dimensional planar force images.