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Abstract. Microscopic Imaging Ellipsometry is an optical technique that uses an objective and sensing procedure to measure the ellipsometric parameters Ψ and Δ in the form of microscopic maps. This technique is well known for being non-invasive and label-free. Therefore it can be used to detect and characterize biological species without any impact. In this work MIE was used to measure the optical response of dried Streptococcus mutans cells on a glass substrate. The ellipsometric Ψ and Δ maps were obtained with Optrel Multiskop system for specular reflection in the visible range (λ= 450nm -750nm). The Ψ and Δ images at 500nm, 600nm, and 700nm were analyzed using three different theoretical models with single-bounce, twobounce, and multi-bounce light paths to obtain the optical constants and height distribution. The obtained images of the optical constants show different aspects when comparing the single-bounce analysis with the two-bounce or multi-bounce analysis in detecting S. mutans samples. Furthermore, the height distributions estimated by two-bounce and multi-bounce analysis of S. mutans samples were in agreement with the thickness values measured by AFM, which implies that the two-bounce and multi-bounce analysis can provide information complementary to that obtained by single-bounce light path.

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Through polarized light interacting with samples, imaging ellipsometry is capable of aiding in the study of semitransparent biological cells microscopically; it is also possible to find applications in marker-free nondestructive disease diagnosis. Often a living biological cell is sensitive to environmental conditions, and fast measurement is preferred. Fast and accurate locating of the focal plane is important for biosensing. By analyzing our previous published through-focus ellipsometry images for S. mutans cells on Au film, we have found an efficient method of locating the focal plane position, i.e., through edge detection of cells in ellipsometry images. The method is not sample-dependent. As the edges are decided only by a sample’s own features, the method is robust against noise or small shifts of images. It is also easy to use without the need to choose a threshold value as in the Laplace filtering method. The method can be further useful for biosensing applications.

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