Patent Description:
<CIT> relates to an assembly for making thickness measurements in a thin film structure. The assembly comprises a chemical-mechanical planarization (CMP) subassembly for effecting topographical changes in a thin film structure; and, a measuring subassembly for detecting the thickness of a thin film structure, the measuring subassembly interposed with the CMP subassembly so that a thickness measurement can be made during and independent of CMP process operations.

<CIT> concerns an ophthalmological analysis instrument for measuring a topography of a surface of an eye includes a projection apparatus and a monitoring apparatus. The projection apparatus has at least one illumination device and an aperture device. The illumination device has a first light source. The aperture device images an image pattern on a surface of an eye. The monitoring apparatus has a camera and an objective lens, wherein images of the imaged image pattern being recordable by the monitoring apparatus, and a topography of the surface being derivable from the images.

<CIT> relates to a system for performing a tear film structure measurement and evaporation rate.

The article <NPL>, relates to a method for the estimation of the lipid layer thickness.

The article<NPL>, relates to a method for mapping of thin film thickness.

Dry eye has become one of the most common causes for ophthalmological doctor visits. Dry eye is a multifactorial disease of the ocular surface that is related to the tear film. As illustrated in <FIG>, the tear film <NUM> comprises the outer layers of the eye including a lipid layer <NUM> that is about <NUM> thick, a muco-aqueous layer <NUM> (also referred to as a mucous and/or aqueous layer) that is about <NUM> thick, and the cornea <NUM>, which is about <NUM> thick, follows the muco-aqueous layer. Currently, few technologies exist for imaging and analyzing the layers of the tear film <NUM> (e.g., determining layer thickness) to objectively assist dry eye diagnosis.

Interferometric techniques are among the currently available non-invasive measurements. Of these, one approach relies on the correlation between an image color and the lipid layer thickness, either quantitatively or qualitatively. Theoretically, the analysis is performed based on two-dimensional (2D) images, while typically only the average thickness within a fairly large area is presented. However, this approach is usually limited for relative lipid layer thickness estimates and may be susceptible to phase ambiguity and uncertainty in absolute thickness measurement.

More rigorous numerical analysis of the reflection spectra can be performed based on physics models using Fourier transform/least square fitting techniques. However, this typically requires a spectrometer, which limits the measurement at a single spot and makes the system design complicated and more expensive.

Optical coherence tomography (OCT) has also been used for tear film thickness measurement by combining high-end ultrahigh resolution OCT and sophisticated statistical decision theory to determine thicknesses of various layers. Theoretically, 2D measurements can be achieved with a proper scanning mechanism, but practically ultrahigh resolution OCT systems are very expensive.

Lastly, fluctuation analysis by spatial image correlation has also been applied to quantify the thickness of the pre-corneal tear film. However, this technique has still yet to demonstrate the capability for lipid layer thickness measurement.

According to the invention, a method for measuring layer thicknesses of a multi-layer structure according to claim <NUM> is provided.

In various embodiments of the above example, the method further comprises generating additional 2D images with incident light in additional discrete narrow spectral bands, wherein a total number of narrow spectral bands is equal to or greater than a number of determined layer thicknesses; the method further comprises generating a third 2D image with incident light in a third discrete narrow spectral band, wherein the first, second, and third discrete narrow spectral bands are evenly distributed across a spectral bandwidth of measurement; the method further comprises generating a third 2D image with incident light in a third discrete narrow spectral band, wherein the first, second, and third discrete narrow spectral bands are unevenly distributed across a spectral bandwidth of measurement; the thickness for the at least one layer of the structure is determined by solving a system of equations for layer thickness of the at least one layer of the structure, and each of the equations represents a measured intensity of reflected light from incident light on the structure at wavelengths corresponding to the first and second discrete narrow spectral bands, and is further a function of indices of refraction for the at least one layer of the structure; the measured intensity of reflected light is determined according to the following equation: <MAT>, wherein λ represents wavelength within the first or second discrete narrow spectral band, α, β, and γ are predetermined factors, n<NUM> and n<NUM> are the indices of refraction for first and second layers of the structure, respectively, and d<NUM> and d<NUM> are the layer thicknesses for the first and second layers of the structure, respectively; at least one of the measured intensities is adjusted for a spectral response of an optical system that provides the first and second incident lights to the structure and light reflected by the structure to the light sensor, or for a spectral response of the light sensor; the thickness for the at least one layer of the structure is determined by solving a system of equations for layer thickness of the at least one layer of the structure, each of the equations representing a summation of measured intensities of reflected light from incident lights on the structure at wavelengths corresponding to the first and second discrete narrow spectral bands determined according to: <MAT>, wherein: I(λ) is a measured intensity of reflected light for an incident wavelength of light λ, and is further a function of the layer thickness of the at least one layer of the structure, and the indices of refraction for the at least one layer of the structure, λ<NUM> and λ<NUM> are lower and upper limits for a respective one of the discrete narrow spectral bands, Eoptics is a spectral response of an optical system that provides the first and second incident lights to the structure and light reflected by the structure to the light sensor, and Esensor (λ) is a spectral response of the light sensor; the thickness for the at least one layer of the structure is determined by inputting a measured intensity of reflected light from incident light on the structure to a trained machine learning system; the machine learning system is trained in a supervised setting to output a thickness to relate a measured intensity to a layer thickness based on fully resolved spectrometric reflectance for a wavelength of incident light corresponding to the measured intensity, measurements of a physical tear film model, and/or numerical models or simulations; the locations of the structure corresponding to each pixel of the first and second 2D images are illuminated with the first incident light prior to the locations of the structure corresponding to each pixel of the first and second 2D images being illuminated with the second incident light; the location of the structure corresponding to a first pixel of the first and second 2D images is illuminated with the first incident light and the second incident light prior to the location of the structure corresponding to a second pixel being illuminated with the first incident light and the second incident light; for each location of the structure corresponding to a pixel of the first and second 2D images, the pixel location is illuminated with the first and second incident light simultaneously and the intensities of the reflections of the first and second incident lights are measured simultaneously; the first and second discrete narrow spectral bands do not overlap; and/or a bandwidth of the first discrete narrow spectral band and is different than a bandwidth of the second discrete narrow spectral band.

According to the invention, an imaging system which comprises a processor configured to control execution of the steps of the method of claims <NUM> to <NUM> is provided. The system further comprises a light source configured to generate the first and second incident light; and a light sensor configured to measure the reflection of incident light.

In preferred embodiments of the claimed imaging system, the light source is a broadband light source or a light emitting diode; and/or the light sensor is a hyperspectral/multi-spectral camera.

Based on the foregoing deficiencies, the present disclosure is based in part on the recognition that 'fringe' images carry thickness information for both the lipid and muco-aqueous layers of the tear film. In contrast, it has been traditionally understood that color fringes are only affected by the lipid layer, and therefore the muco-aqueous layer has been ignored when interpreting the color fringes. In view of this recognition, the present disclosure describes a method for determining thickness for both lipid and muco-aqueous layers from 2D color/multi-spectral fringe images.

Initially, it is noted that 'fringe' images are those resulting from interference among reflected light rays from an incident light beam on an imaged object. Notably, when an incident light beam (having a wavelength λ) traveling through a medium comes into contact with a boundary to another layer, a portion of that light beam is reflected while another portion is transmitted through the barrier, and refracted. If the transmitted portion comes into contact with another medium barrier, it too is partially transmitted and reflected. By way of example illustrated in <FIG>, when an incident light beam <NUM> traveling through air contacts an air-oil interface <NUM>, it is partially reflected and partially transmitted. When the transmitted portion comes in contact with an oil-water interface <NUM>, it is reflected back toward, and transmitted through, the oil-air interface <NUM>. As a result, two light rays <NUM>, <NUM>, having traveled different path lengths, are transmitted back through the air away from the air-oil interface <NUM>.

When the path length difference between the reflected rays <NUM>, <NUM> is an odd multiple of λ/<NUM>, the beams are out of phase with each other and produce destructive interference; when the path length difference between the beams <NUM>, <NUM> is an even multiple of λ/<NUM>, the beams are in phase with each other and produce constructive interference. This interference can form a 'fringe' image, whereby regions of destructive interference produce a dark fringe and regions of constructive interference produce a bright fringe.

This observed interference of the reflected waves <NUM>, <NUM> is dependent on four factors: the layer thicknesses, the illumination/observation angle, the refraction index of the mediums/layers, and the wavelength of incident light. With prior knowledge of refraction index of the layers of the tear film, there are three corresponding interferometric methods for studying the tear film: <NUM>) thickness-dependent fringes, <NUM>) angle-dependent fringes, and <NUM>) wavelength-dependent fringes. Due to the higher tolerance for alignment error, interferometry based on wavelength-dependent fringes can be most suited for tear film thickness measurement.

More specifically, the transmission, reflection, and refraction for a light beam incident to the tear film is illustrated in with reference to <FIG>. The resulting reflected rays of light from the incident beam are identified as rays A-D. The intensity for each pixel of a fringe image of the tear film is related to the thickness of each layer as follows.

According to Fresnel's equations, reflectance (R) and transmittance (T) at each layer interface is:.

From the above transmittance and reflectance determinations, an intensity factor for each beam A-D resulting from the incident light beam is: <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT>.

Substituting the known indices of refraction results in the following relative values for each ray of reflected incident light A, B, C, and D: <MAT> <MAT> <MAT> <MAT>.

Further, the combined detected light wave of all reflected portions of the incident light (at wavelength λ and time t) can be written as: <MAT> where <MAT>, and <MAT>, where dlipid is the thickness of the lipid layer and dmucoaqueous is the thickness of the muco-aqueous layer, and where c is the speed of light.

According to one example based on Maxwell's equations, the reflectance intensity over time for each wavelength can thus be written as: <MAT> As shown above, A ≫ B∼D >> C. Therefore, the above intensity equation can be simplified as: <MAT> where α(λ)∼<NUM>-<NUM> and β(λ), γ(λ)∼<NUM>-<NUM>. With substitution, the intensity of reflected light for a given incident light of wavelength λ: <MAT> As noted above, Equation <NUM> is based on Maxwell's equations. However, the particular model may be derived differently, such that a different equation representing intensity is solved for the layer thicknesses.

Regardless of the equation used to represent intensity, the actual measured intensity may be a summation of intensities for each incident wavelength in a given band of light from a light source (e.g., a discrete narrow band as discussed in more detail below). In the example where the measurement is performed for a band having wavelengths between λ<NUM> and λ<NUM>, the measured intensity can be summarized as: <MAT> where I(λ) is an equation representing a measured intensity at a particular wavelength λ, Eoptics(λ) is the spectral response of the optical system used to pass the incident and reflected lights, and Esensor (X) is the spectral response of a light sensor that detects the reflected light-in other words, the imaging sensor and system characteristics. In other embodiments, summation over wavelengths within each band can be used to determine an intensity for each band (that may be used to solve for layer thicknesses), without necessarily accounting for the spectral responses of the optical system and/or the sensor.

With the above model (summarized by Equations <NUM> and <NUM>), the distances dlipid and dmucoaqueous of the lipid and muco-aqueous layers, respectively, can be determined for a detected intensity of reflected light, given a known wavelength of incident light and index of refraction. In this example with only two unknown variables, dlipid and dmucoaqueous, theoretically only two independent intensity measurements are required to solve for those variables. Of course, in practice, more measurements can be performed. Additionally, more layers can be solved for producing equations of a similar form to Equations <NUM> and <NUM>. The analysis to determine an optimal solution to the system of equations (a system of Equation <NUM> for each of a plurality of wavelengths of incident light) can be performed using, for example, least square fitting techniques, or a global optimization with pre-determined lookup table(s) (LUT). Using a least square fitting technique is dynamic and more dependent on initial conditions; but therefore prone to be trapped to local optimums. Using prior knowledge of the search range, lookup tables are more likely to produce a global optimal solution. Of course, a similarly simplified model or a more rigorous solution based on Maxwell's equations may also be used herein. And more sophisticated processing techniques, for example machine learning, can also be employed. For example, with a machine learning system, an algorithm can be trained to predict the thickness of the different layers using only Imeasure and without the knowledge of Equation <NUM>. The training of the system can be performed in a supervised learning setting where pairs of Imeasure and the true thicknesses are available. It is noted that depending on the design of the machine learning system, the pair of Imeasure and the true thickness can be obtained in multiple ways, including measurement based on fully resolved spectrometric reflectance, and/or measurement of physical tear film model(s), and/or numerical modeling/simulation.

The present disclosure is also based in part on the recognition that RGB images are under-sampled and averaged spectrometric measurements. Therefore, traditional RGB fringe images are not adequate for thickness measurement for various layers of the tear film. However, measurement contrast can be enhanced with thickness information better preserved with narrower spectral bandwidths than traditional RGB color. In other words, the present disclosure also relates the imaging with narrow spectral bands-measuring intensity I(λ) of Equation <NUM> and/or Imeasure(λ<NUM>,λ<NUM>) of Equation <NUM> for wavelengths of incident light in discrete narrow spectral bands (between λ<NUM> and λ<NUM>).

Conventionally, the muco-aqueous layer has only been able to be measured at a single spot with broadband white light source based on spectrometry technology. However, using a smaller number of spectral bands and a narrower spectral bandwidth than conventional RGB imaging can provide sufficient signal strength for 2D muco-aqueous layer thickness measurement. Further, narrow spectral bandwidths can help to enhance the imaging contrast, particularly for muco-aqueous layer measurement; although, if the bandwidth is too narrow, system tolerance may be adversely affected.

Such narrow spectral band imaging can be achieved by selecting an appropriate light source, and/or spectral filters to generate those narrow spectral bands. Relative to broadband imaging, the use of narrow spectral bands further enhances the contrast for muco-aqueous layer thickness measurement by mitigating the washout effect and preserving thickness information for the thickest layer in the multilayer structure. More particularly, the washout effect occurs when, with reference to Equation <NUM>, λ<NUM> and λ<NUM> are significantly different and thus Imeasure(λ<NUM>, λ<NUM>) is integrated over a large range. As a result, the uniqueness of each intensity measured at the individual wavelengths I(λ) (and caused by different layer thicknesses) is minimized (effectively "washed out") by the large integration range. This can ultimately cause different layer thicknesses to generate the same value for Imeasure(λ<NUM>, λ<NUM>). Practically, the minimum bandwidth is determined by measurement SNR, performance tolerance, and cost. And the maximum bandwidth is determined by the thickest layer in the structure and the system response characteristics, particularly the sensitivity/dynamic range of the sensors. According to the invention, the bandwidth for the discrete narrow spectral bands is between a few nanometers to a few tens of nanometers (at full width, half maximum).

It is also noted that the spectral bandwidth affects measurement accuracy differently for the lipid layer and muco-aqueous layer. Instead of a tradeoff bandwidth that potentially compromises the performance for all layers, the use of a plurality of narrow spectral bandwidths makes it possible to optimize a set of spectral bands with different bandwidths for a particular layer thickness measurement.

The spectral bands can be distributed across the entire spectral bandwidth in any fashion. For instance, they can be evenly distributed. In other embodiments, the spectral bands can be distributed to take advantage of the emission spectral bands of the light source or the quantum efficiency of a detector of the reflected light. In still other embodiments, the spectral bands can be distributed to provide maximum measurement sensitivity/accuracy based on a prior knowledge of the thickness ranges for different layers.

When combined with the above analysis of the measurement data (solving a system of equations for a plurality of measured intensities of reflected light), the measurement speed/efficiency can be significantly enhanced with a few spectral bands (depending on the number of layers to be measured), thereby overcoming the deficiencies of other spectrometer based methods with hundreds of spectral channels. For example, the improved measurement efficiency can help to increase measurement speed and/or reduce illumination power and improve user experience, which can further help to improve measurement performance. It is noted that such an approach does not depend on specific requirement for system/operating parameters (e.g., the center wavelength and the bandwidth for each spectral band); and these parameters may be set based on the statistical distribution of tear film thicknesses. Nevertheless, calibration of the system based on selected parameters may still be performed.

For thickness measurement of the tear film, the tear film can be modeled as two primary layers: the lipid layer and the muco-aqueous layer. Thus theoretically for a two layer thickness measurement, as few as two spectral bands are sufficient to derive the thickness for both layers. However, the number of spectral bands can be higher to improve measurement reliability and accuracy. Compared with the full spectrum spectrometry measurement, fewer spectral bands improves detection efficiency and the number of photons per spectral band can be much higher to achieve a better signal-to-noise ratio (SNR).

In view of these recognitions, briefly the system and method described herein operate according to the flow chart in <FIG>. First 2D fringe images of the tear film of the eye are generated <NUM> for each of a plurality of spectral bands (e.g., from discrete narrow spectral band spectrometry) of incident light on the eye. The resulting 2D images are analyzed <NUM> on a pixel-by-pixel basis to determine the intensity of the reflected light at each wavelength at that pixel. The thickness of each layer for a location on the eye/tear film corresponding to each pixel is then derived <NUM> from the intensity based on the above, or a similar, model, by substituting the determined intensities and wavelengths in Equation <NUM> and <NUM>, and solving the resulting system of equations for the different layer thicknesses.

With reference to <FIG>, a system, similar to an interferometric imaging system, for generating 2D fringe images is described. Therein, incident light is generated from a light source <NUM>. The incident light beam is directed to the tear film of a subject's eye <NUM>. Light rays reflected by the object are then detected by an imager or optical sensor <NUM> or like photodetector. The output of the photodetector is then supplied to a computer <NUM> or like processor (e.g., controller or other CPU) for further processing and analysis. The light source <NUM> and imager or optical sensor <NUM> are preferably synchronized. This synchronization may be coordinated directly between the source <NUM> and imager or optical sensor <NUM>, or facilitated by processing at the computer <NUM>.

As noted above, the incident light is generated, and the reflections from the object are detected, in a plurality of discrete narrow spectral bands. These discrete narrow spectral bands do not practically have a lower bandwidth limit and may be as great as hundreds of nanometers (at full width, half maximum) if any of the features of such bands described herein are achieved. Preferably, the spectral bands do not overlap, however, in some embodiments the bands may be partially overlapping. According to one non-limiting example, the discrete narrow spectral bands may be between <NUM> and <NUM>; and according to another non-limiting example may be about <NUM>. According to one example, illustrated in <FIG>, imaging may be conducted with five bands between about: <NUM>) <NUM>-<NUM>, <NUM>) <NUM>-<NUM>, <NUM>) <NUM>-<NUM>, <NUM>) <NUM>-<NUM>, and <NUM>) <NUM>-<NUM>. According to another example, each band is centered at <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>.

This incident light may be generated and detected according to any number of embodiments. For example, a first embodiment is illustrated in <FIG> and <FIG>. Therein, the light source <NUM> sequentially generates and detects light in each desired band. This can be accomplished, for example, by generating a broad bandwidth light at the light source <NUM> and sequentially applying different filters <NUM> (e.g., via a filter wheel) so that only the desired narrow bandwidth of light is provided to the eye at any given time. Alternatively, the light source <NUM> itself may generate light only in the desired narrow spectral band.

In other words, the eye is first illuminated <NUM> in a first of the narrow spectral bands and then an entire 2D image for the first band is generated <NUM>. Illumination <NUM> and acquisition <NUM> is then repeated for each of the narrow spectral bands. To update the band <NUM>, for example, the processor <NUM> may control which filter <NUM> is aligned with the light source <NUM> or control the light source <NUM> to output a different narrow spectral band. After all 2D images have been generated, thicknesses are determined <NUM> for locations of the eye corresponding to each pixel of the 2D images based on the above-described model.

Another embodiment based on a parallel acquisition of 2D images is illustrated in <FIG> and <FIG>. There, the light source <NUM> generates light <NUM> in a plurality of narrow spectral bands simultaneously. Accordingly, reflected light is detected by, for example, a multi-channel/multi-band 2D camera/sensor array <NUM>. Multi-spectrum optics and sensors, such as a prism-based multi-chip camera or a multi-layer sensor chip may be used to detect light in the multiple narrow spectral bands. With these, multiple spectral bands can be detected at the same time. Such detection can help reduce motion artifacts. After detection, 2D images are generated <NUM> for each of the detected narrow spectral bands. If fewer than all of the narrow spectral bands were generated by the light source <NUM>, the illumination <NUM> and 2D image generation <NUM> is repeated (by updating the light source <NUM> as described above and repeating illumination <NUM> and 2D image generation <NUM>) until there are 2D images for all desired narrow spectral bands. After all 2D images have been generated, thicknesses are determined <NUM> for locations of the eye corresponding to each pixel of the 2D images based on the above-described model.

In variations of the above embodiments, incident light is generated and reflected rays are detected (either by adjusting the narrow spectral band of incident light, or detecting multiple spectrums of light) for all desired narrow spectral bands at a single location of the eye. Imaging progresses location by location until data for all pixels of the 2D images has been acquired. All 2D images are then generated after illumination and detection has been completed. In other words, illumination and detection is repeated for each pixel location until the entire imaging region has been imaged and each 2D image acquired. Then, the thickness of each layer is determined as above.

In still another embodiment, the above sequential and parallel methods may be combined. For instance, illumination may be composed of light in multiple spectral bands at one moment and changed to different multi-spectral bands at the next moment, with the illumination synchronized accordingly with the multi-channel sensor/camera.

While system designs based on the above-described features perform efficiently with minimal waste of reflected light, it is possible to implement a less efficient system with a simpler design that may still meet safety criteria. For instance, in still another embodiment, the light source <NUM> may be a broadband (continuous or discrete) light source for illumination and the imager or optical sensor <NUM> may be a hyperspectral/multispectral imaging camera (where a cluster of pixels are coated with filters for different spectral band, similar to RBG color cameras using Bayer filter). While overall imaging efficiency may not be as optimal, the thickness measurement can still be performed, for example, based on the above model. Similarly, other hyperspectral/multi-spectral imaging technologies can be adopted to achieve thickness measurement, at the cost of overall system efficiency.

As suggested above, for any embodiment, the light source <NUM> may emit light of the different spectral bands by using multiple sub light sources, by sweeping/wavelength hopping, or by using a broadband light source combined with optical filters. It is estimated that the illumination power for reliable measurement is less than <NUM>µ W. The light source <NUM> can thus be any type (or combination of types) of light source capable of producing multiple spectral bands. For example, the light source <NUM> may be a broadband light source like a thermal source and a halogen lamp, or LED(s). In other embodiments, a synthesized white light source with spectral bands matching a color camera can be employed for measuring spectral bands in parallel. Additional synthetized white light sources (with different set of spectral bands) can also be employed to increase the effective number spectral bands.

For any embodiment, the camera/sensor photodetector <NUM> for detecting the reflected light may be selected based on the sensitivity and color imaging mechanism of the camera/sensor. For example, while traditional RGB cameras could be used, they commonly include Bayer filters, which limit the camera's efficiency and spatial displacement for pixels at different spectral bands. Alternatively prism based/multi-layer sensor multi-channel cameras offer better efficiency and resolution.

While <FIG> and <FIG> illustrate the incident light beam illumination source/optics separately from the imaging optics/sensor for detecting reflected light, it is understood that these could be integrated into a single device. In any event, the generation of the incident light and detection of the reflected light are synchronized as needed.

<FIG> and <FIG> illustrate a simulation and results according to the above-described method. More particularly, a simulated model of a three layer structure was generated with a refractive index of the top layer set to correspond to that of the human lipid layer and a refractive index of the middle layer set to correspond to that of the human muco-aqueous layer. <FIG> illustrates a thickness map of the top layer of the simulated model and <FIG> illustrates a thickness map of the middle layer of the simulated model. Although not representative of actual tear film layer thicknesses, the simulated thickness maps shown in <FIG> were generated with thickness ranges of a typical human tear film. Given the above, the simulated model in <FIG> facilitated testing of a range of thicknesses of an actual tear film and the visualization of results (shown in <FIG>).

Using discrete narrow spectral bands (having a <NUM> bandwidth and centered around <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>) using the above-described model, the simulated lipid layer thickness map of <FIG> and simulated muco-aqueous layer thickness map of <FIG> were reconstructed. These reconstructions are illustrated in <FIG>, respectively. As can be seen, the 2D reconstructed maps of <FIG> closely mirror the simulated maps of <FIG>. Therefore, it is understood that the above-described model can properly be used to determine layer thicknesses of the tear film by imaging the tear film with discrete narrow spectral bands.

According to another experimental test, a three-layer structure was formed of a ∼<NUM> silicon dioxide (SiO<NUM>) top layer, a ~<NUM> magnesium fluoride (MgF<NUM>) intermediate layer, and a > <NUM> BK7 borosilicate glass substrate layer. This structure was imaged and analyzed according to the above method using five discrete narrow spectral bands and a lookup table to determine the optimal solutions to the system of equations (of Equations <NUM> and <NUM>). While Equations <NUM> and <NUM> specifically reference the indices of refraction and depths of the lipid and muco-aqueous layers, it is understood that the corresponding variables represent the silicon dioxide and magnesium fluoride layers of the structure in this experiment.

In this test, imaging was performed with the spectral bands that were evenly distributed (e.g., as in the above example with <NUM> bands centered at <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>) and unevenly distributed (e.g., as illustrated in <FIG>) across the full spectrum of the imaging system's light source. Using collected data from imaging with evenly distributed narrow spectral bands to form a system of equations (of Equation <NUM>), the thickness of the SiO<NUM> layer was estimated to be <NUM> and the thickness of the MgF<NUM> layer was estimated to be <NUM>. Similarly, when imaging with discrete narrow spectral bands that were unevenly distributed, the thicknesses were estimated to be <NUM> and <NUM>, respectively. A conventional curve fitting technique estimated the layers to be <NUM> and <NUM>, respectively. Accordingly, the method and system described herein is capable of producing similarly accurate results to the conventional approach, but without the above-noted deficiencies.

While various features are presented above, it should be understood that the features may be used singly or in any combination thereof. Further, it should be understood that variations and modifications may occur to those skilled in the art to which the claimed examples pertain. Similarly, while the above disclosure primarily relates to imaging of the tear film of an eye, the disclosure may also be applied to imaging and determining layer thicknesses for any other multilayer structure.

Claim 1:
A method for measuring layer thicknesses of a multi-layer structure, wherein the multilayer structure is a tear film (<NUM>) of an eye, the method comprising:
generating (<NUM>, <NUM>, <NUM>) a first two-dimensional (2D) image of the structure by, for each pixel of the first 2D image, generating a first incident light from a light source (<NUM>), measuring an intensity of a reflection of the first incident light at a location of the structure corresponding to the pixel;
generating (<NUM>, <NUM>, <NUM>) a second 2D image of the structure by, for each pixel of the second 2D image, generating a second incident light from the light source (<NUM>), measuring an intensity of a reflection of the second incident light at a location of the structure corresponding to the pixel;
determining (<NUM>, <NUM>, <NUM>, <NUM>) using a computer (<NUM>) or like processor a thickness for at least one layer of the structure at each location of the structure based on the intensities of the measured reflections of corresponding pixels in the first and second 2D images, wherein the at least one determined layer thickness is a thickness of a lipid layer (<NUM>) and a muco-aqueous layer (<NUM>) of a tear film of an eye,
wherein the first incident light is generated in a first discrete narrow spectral band, the second incident light is generated in a second discrete narrow spectral band, and the first and second discrete narrow spectral bands do not fully overlap, wherein the first and second discrete narrow spectral bands have bandwidths which are between a few nanometers to a few tens of nanometers at full width half maximum, to mitigate fringe washout and preserve thickness information of the thickest layer of the structure, and
wherein the intensities of the reflection of the first and second incident light are measured by a light sensor (<NUM>).