Optical imaging performance testing system and method

Abstract

To test the imaging performance of an optical system, a test target is located in the object plane of the optical system and is illuminated to produce an image beam. One or more images of the test target are acquired from the image beam. Based on the acquired imaging data, edge spread functions at multiple locations within the test target are calculated. A model of a point spread function is constructed from the edge spread function. Based on the point spread function, multiple imaging performance values ​​corresponding to the multiple locations are calculated. The imaging performance values ​​are based on an enclosing rectangle energy, an enclosing circle energy, or a Strehl ratio.

Description

[0001] Related Applications

[0002] This application claims the benefit of U.S. Provisional Patent Application Serial No. 62 / 884,116, filed on August 7, 2019, entitled “OPTICAL IMAGING PERFORMANCE TEST SYSTEM AND METHOD,” under 35 U.S.C. §119(e), the contents of which are incorporated herein by reference in their entirety. Technical Field

[0003] The present invention generally relates to testing the imaging performance of optical imaging systems, such as may be used in microscopy to acquire imaging data from a sample being analyzed. Background Art

[0004] In optical imaging systems (including automated, high-throughput optical imaging systems used in microscopy), a key indicator of the quality of the imaging data obtained is the clarity of the image obtained. Many applications of microscopic imaging (for example, in DNA sequencing) rely on the ability to produce uniformly sharp images in the field of view of one or more channels or cameras despite imperfect focus and tilt of the sample. Optical alignment of the camera and other optical components of the optical imaging system with each other and with the imaged sample can significantly improve image clarity, thereby improving the quality of the imaging data generated. Therefore, it is desirable to be able to test and evaluate the imaging performance (i.e., quality) of the optical imaging system. For example, it is useful to determine whether the optical imaging system meets or exceeds a certain predetermined level of minimum quality that is considered acceptable for a given application of the optical imaging system. It is further useful to describe the overall imaging performance using a single precise number for convenient manufacturing and calculation of manufacturing performance indicators.

[0005] An image acquired by an optical imaging system can be characterized as a mixture of the true sample (or object) [S(x,y)] and the instrument response function (which is called the Point Spread Function [PSF(x,y)] in microscopy). Mathematically, the image I(x,y) is the sum of the convolution of the sample and the PSF with the noise N(x,y) at each pixel as follows:

[0006] I(x,y)=PSF(x,y)*S(x,y)+N(x,y) (1)

[0007] Evaluation of imaging performance may involve acquiring an image of a sample (which may be a test target, ie, an object provided for testing purposes), using the imaging data to measure and quantify the PSF, and comparing the results to a predetermined minimum passing standard.

[0008] It is desirable to describe the quality of a PSF using a single scalar number. Several metrics are commonly used, such as the full width at half maximum (FWHM) or Strehl ratio of the real-space PSF, the wavefront error or modulation transfer function (MTF) contrast at a single frequency, and the real-space quantities Encircled Energy or Ensquared Energy. The accuracy and precision of the estimates of the measurement of each quantity are closely related to the experimental methods used to calculate them. The pros and cons of each are related to the accuracy with which they are estimated, the ease of interpretation, and the ability to capture poor image resolution and poor image signal-to-noise ratio (SNR).

[0009] The classical formulation of equation (1) above does not reflect the fact that the PSF varies across the image field, which is equivalent to saying that the image may be sharp in the middle (high quality) and blurry at the edges (poor quality). An optical system that is nearly diffraction limited and optimized for weak source detection will have a compact PSF that contains density in an area of ​​approximately 5 x 5 pixels, in contrast to typically much wider images (e.g., 4096 x 3000 pixel images). A small PSF can vary greatly across the image, necessitating multiple independent measurements of the PSF across the field of view. In addition to changing field coordinates (x,y), the PSF is strongly affected by the focus of the image in the z direction, and high performance, high numerical aperture (NA) objectives experience rapid degradation of PSF performance with defocus, that is, they have a shallow depth of field. Diffraction effects place an upper limit on the depth of field, but poorly corrected or misaligned systems will experience even shallower depths of field. The practical limitations of this are significant, as systems of poor optical quality may require multiple refocusings to obtain a sharp image at every point in the field, resulting in poor system throughput and potential sample photodegradation.

[0010] Results from downstream processing of images by image analysis software are strongly influenced by image clarity. Blurred images of high contrast punctate objects experience significant signal-to-noise degradation and reduced resolution. For example, blurred images of DNA clusters reduce the ability to accurately measure nucleotide abundance, reduce the number of small distinct clusters that can be observed, and introduce crosstalk between adjacent clusters.

[0011] Image processing algorithms can partially mitigate blurry images by performing any of a number of explicit steps (using well-known deconvolution methods) or implicit steps (such as selective filtering of the data). This approach incurs computational expense if the PSF degradation needs to be estimated for each image or batch of images, and estimating the PSF degradation itself adds noise. Faster methods assume a constant sharpness PSF, but any difference between the actual PSF and the assumed PSF degrades the quality of the image processing result. Therefore, the PSF homogeneity of the input images affects the effectiveness of the image processing algorithm.

[0012] Existing methods of optical alignment are labor intensive and strive to obtain fast feedback describing the performance of the entire optical system, for example in the case of wavefront measurement, they may accurately and quickly describe a single point in the field of view, but neglect to inform the operator how their actions affect distant points in the field of view. The manufacturing labor costs associated with optical alignment can be reduced by using fast predictive feedback to direct operators. Therefore, fast global analysis of system performance (providing feedback with a latency on the order of 0-5 seconds) would be beneficial.

[0013] Furthermore, analysis of manufacturing yield (e.g. using the CpK process capability statistic) requires estimating the quality of each unit as a continuous variable, ideally in order to look at a single such variable to describe the entire unit build.

[0014] Therefore, there is a continuing need for improved methods for imaging performance testing of optical imaging systems. Summary of the invention

[0015] To fully or partially address the above-mentioned problems and / or other problems that may have been observed by those skilled in the art, the present disclosure provides methods, processes, systems, devices, instruments and / or apparatus, as described by examples in the implementations set forth below.

[0016] According to one embodiment, a method for testing the imaging performance of an optical system includes: positioning a test target on an object plane of the optical system; operating the optical system to illuminate the test target and generate an image beam; operating a focusing stage of the optical system to acquire a plurality of images of the test target corresponding to a plurality of defocus values; calculating a plurality of edge spread functions (Edge Spread Function) at a plurality of positions within the test target from each image; constructing a plurality of corresponding point spread function models from the edge spread functions; and calculating a plurality of imaging performance values ​​corresponding to the plurality of positions within an imaging volume extruded through a series of defocus positions defined by an image plane based on the point spread function model, wherein the imaging performance value is based on at least one of the following indicators: enclosing rectangle energy; enclosing circle energy; or Strehl ratio.

[0017] In an embodiment, illuminating the test target requires back-illuminating the test target with a numerical aperture (NA) greater than or equal to the numerical aperture (NA) of the optical system under test.

[0018] In an embodiment, the method comprises evaluating and describing the distribution and mapping of imaging performance values ​​over the imaging volume, or otherwise comprises calculating a condensed summary score based on such distribution and mapping.

[0019] According to another embodiment, an optical imaging performance testing system includes: a target holder configured to hold a test target; a light source configured to illuminate the test target; an imaging device configured to acquire an image of the test target; an objective lens located in an imaging light path between the test target and the imaging device, wherein the position of the objective lens can be adjusted along the imaging light path; and a controller including an electronic processor and a memory, and configured to control or execute at least the following steps or functions of a method for testing imaging performance: calculating the multiple edge spread functions, constructing the multiple point spread function models, and calculating the multiple imaging performance values.

[0020] In an embodiment, an optical imaging performance testing system includes a tube lens positioned in an imaging optical path between a test target and an imaging device.

[0021] In an embodiment, the optical imaging performance testing system includes other or additional mechanical devices or components configured to allow the angular orientation and position of the imaging devices to be changed relative to each other and relative to the objective lens.

[0022] In an embodiment, the controller is configured to control or perform the following steps or functions of a method for testing imaging performance: evaluating and describing the distribution and mapping of imaging performance values ​​on the imaging volume, and calculating a condensed summary score based on such distribution and mapping.

[0023] According to another embodiment, a non-transitory computer-readable medium includes instructions stored thereon that, when executed on a processor, control or perform at least the following steps or functions of a method for testing imaging performance: calculating a plurality of edge spread functions, constructing a point spread function model, and calculating a plurality of imaging performance values.

[0024] According to another embodiment, a system for testing imaging performance of an optical system includes a computer-readable storage medium.

[0025] Other devices, apparatus, systems, methods, features and advantages of the present invention will be or will become apparent to those skilled in the art upon examination of the following drawings and detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the present invention, and be protected by the following claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The present invention may be better understood with reference to the following drawings. The components in the drawings are not necessarily drawn to scale, but emphasis is placed on illustrating the principles of the present invention. In the drawings, the same reference numerals represent corresponding parts in different views.

[0027] Figure 1is a schematic diagram of an example of an optical imaging performance testing system according to an embodiment of the present disclosure.

[0028] Figure 2 is a schematic perspective cross-sectional view of an example of a target holder of an optical imaging performance test system according to an embodiment.

[0029] Figure 3A is a schematic plan view of an example of a test target of the optical imaging performance test system according to the embodiment.

[0030] Figure 3B yes Figure 3A Detailed view of a portion of the test target shown in .

[0031] Figure 4A is an example of a three-dimensional (3-D) model of a realistic, reasonably sharp point spread function or PSF (PSF with a true bounding rectangle energy value of 0.594) produced by implementing the systems and methods disclosed herein, where the intensity levels are color coded and different colors are represented by different levels of shading.

[0032] Figure 4B is with Figure 4A An example of a 3-D model of the same PSF, derived from the line spread function by line spread function (LSF x LSF) approximation.

[0033] Figure 4C is with Figure 4A Examples of related 3-D models of the same PSF, where the PSF model is fitted to edge spread function (ESF) data.

[0034] Figure 4D Yes Display Figure 4A The PSF model and Figure 4B The differences between the PSF models and the 3-D models.

[0035] Figure 4E Yes Display Figure 4A The PSF model and Figure 4C The differences between the PSF models and the 3-D models.

[0036] Figure 4F Yes Display Figure 4B The PSF model and Figure 4C The differences between the PSF models and the 3-D models.

[0037] Figure 5A is with Figure 4A Another example of a 3-D model of a similar true, relatively clean PSF with a true EE value of 0.693.

[0038] Figure 5Bis with Figure 5A Example of a correlated 3-D model of the same PSF, derived from the LSF x LSF approximation.

[0039] Figure 5C is with Figure 5A Examples of related 3-D models of the same PSF, where the PSF model is fitted to edge spread function (ESF) data.

[0040] Figure 5D Yes Display Figure 5A The PSF model and Figure 5B The differences between the PSF models and the 3-D models.

[0041] Figure 5E Yes Display Figure 5A The PSF model and Figure 5C The differences between the PSF models and the 3-D models.

[0042] Fig. 5F Yes Display Figure 5B The PSF model and Figure 5C The differences between the PSF models and the 3-D models.

[0043] Fig. 6A is an example of a 3-D model of a true, relatively blurred PSF (with a true EE value of 0.349), otherwise similar to Figure 4A .

[0044] Figure 6B is with Fig. 6A Example of a correlated 3-D model of the same PSF, derived from the LSF xLSF approximation.

[0045] Figure 6C is with Fig. 6A Examples of related 3-D models of the same PSF, where the PSF model is fitted to edge spread function (ESF) data.

[0046] Fig.6D Yes Display Fig. 6A The PSF model and Figure 6B The differences between the PSF models and the 3-D models.

[0047] Fig. 6E Yes Display Fig. 6A The PSF model and Figure 6C The differences between the PSF models and the 3-D models.

[0048] Fig. 6F Yes Display Figure 6B The PSF model and Figure 6C The differences between the PSF models and the 3-D models.

[0049] Fig. 7A is a plot of the accuracy of the LSF x LSF approximation for various PSF defocuses.

[0050] Figure 7B is a plot of the accuracy of the EE fit to the ESF data for various PSF defocuses.

[0051] Figure 7C is a plot of the EE estimation accuracy for PSF defocus.

[0052] Fig. 8A is from Figure 3A An example of an image acquired of one of the target features of the test target is shown.

[0053] Figure 8B is based on Fig. 8A Example of ESF calculated from an image of . The ESF has been cleaned using outlier rejection.

[0054] Fig. 9 Yes Figure 8B Example of the results of fitting the ESF to a model consisting of a linear combination of error functions and calculating the EE from the coefficients of the resulting model.

[0055] Fig.10 is an example of calculating the ESFs of four edges of target feature 313, fitting the ESFs to linear combinations of error functions, and calculating four independent EE values ​​from the corresponding fits.

[0056] Fig.11 is a flow chart illustrating an example of a method for testing imaging performance of an optical system according to an embodiment of the present disclosure.

[0057] Fig.12 is a flow chart illustrating the acquisition of a Z-stack of images according to an embodiment of the present disclosure.

[0058] Fig.13 is a flow chart illustrating processing of individual images according to an embodiment of the present disclosure.

[0059] Fig.14 An example of a set of EE performance graphs that can be generated according to the method according to an embodiment of the present disclosure is illustrated.

[0060] Fig.15 An example of a combined EE score and performance map that may be generated according to this method according to an embodiment of the present disclosure is illustrated.

[0061] Fig.16 The optical imaging performance test system according to the embodiment of the present disclosure (eg Figure 1Schematic diagram of a non-limiting example of a system controller (or controllers) that is part of or in communication with the system shown. DETAILED DESCRIPTION

[0062] The present disclosure provides a system and method for testing the imaging performance of an optical imaging system.

[0063] Figure 1 1 is a schematic diagram of an example of an optical imaging performance test system 100 according to an embodiment of the present disclosure. Generally, the structure and operation of the various components of the optical imaging performance test system 100 are known to those skilled in the art, and thus are only briefly described herein as necessary for understanding the disclosed subject matter. For illustrative purposes, Figure 1 This includes a Cartesian (XYZ) reference system whose origin is arbitrarily located relative to the drawing.

[0064] Typically, the test system 100 includes an optical system under test 104, a test target stage assembly 106, and a system controller 108 that is in electrical communication with appropriate components of the optical system 104 and the test target stage assembly 106 (e.g., for transmitting power, data, measurement signals, control signals, etc., as needed). The optical system 104 can be an actual optical system to be tested, such as a product intended for commercial availability. Alternatively, the optical system 104 can be a collection of components supported by or mounted to the test system 100 (e.g., on an optical bench of the test system 100) and arranged to simulate an actual optical system (e.g., the components are positioned and determined to be at distances and orientations from each other and aligned with each other according to an intended operational configuration).

[0065] In general, the optical system 104 can be any system (or instrument, device, etc.) configured to acquire an optical image of an object or sample to be analyzed. Such samples can be biological (e.g., spores, fungi, molds, bacteria, viruses, biological cells or intracellular components such as nucleic acids (DNA, RNA, etc.), biologically derived particles such as skin cells, debris, etc.) or non-biological. In general, the optical system 104 is configured to acquire optical images with a certain range of magnifications and can therefore be a type of microscope (or part thereof). The optical system 104 may or may not include a target holder 112 (e.g., a sample stage or a simulation of a sample stage) configured to hold a test target 116 (instead of a sample to be imaged in practice). In the case where the target holder 112 is separate, a mechanical reference 160 can generally be provided to allow the target holder 112 to be attached in a precisely positioned manner. The light source 120 is configured to illuminate the test target 116, and the light source 120 may or may not be part of the optical system 104 under test. One or more imaging devices 124 and 128 (e.g., first imaging device 124 and second imaging device 128 in the illustrated embodiment) (e.g., cameras) are configured to capture images of test target 116. Optical system 104 also includes other intermediate optical devices (optical components) as needed to define an illumination light path from light source 120 to target holder 112 and test target 116 (and through test target 116 in the illustrated embodiment), and an imaging light path from target holder 112 and test target 116 to imaging devices 124 and 128, as understood by those skilled in the art.

[0066] In particular, the optical system 104 generally includes an objective lens 132 located in the imaging optical path between the target holder 112 and the test target 116 and the imaging devices 124 and 128. The objective lens 132 is configured to magnify and focus the image to be captured by the imaging devices 124 and 128. In the optional case of an infinity-corrected microscope optics, the objective lens 132 performs this task in conjunction with tube lenses 152 and 156. In the illustrated embodiment, the objective lens 132 schematically depicts an objective lens assembly including an objective lens system mounted to an objective lens stage (or positioning device). The objective lens stage is configured to move (translate) the objective lens system along the Z axis. Therefore, the objective lens 132 is adjustable to focus or defocus at any desired depth (height) in the thickness of the test target 116, as required for the test purpose. Because the objective lens 132 can be focused at different heights, the adjustability enables the optical system 104 to generate a "z-stack" of images, as understood by those skilled in the art. Alternatively, objective 132 may be fixed in space and the sample may be moved in the z direction to change focus.Alternatively, objective 132 and sample may remain in place and tube lenses 152 and 156 may be moved relative to imaging devices 124 and 128.

[0067] In this example, the Z axis is considered to be the optical axis through the test target 116 (from Figure 1 The X-axis and Y-axis are located in a transverse plane orthogonal to the Z-axis. Therefore, the position of the test target 116 on the object plane fixed by the target holder 112 can be mapped using (X, Y) coordinates, and the depth (focus position) can be mapped to the Z coordinate.

[0068] In the illustrated embodiment, the optical system 104 has a top reading configuration, wherein the objective lens 132 is located above the target holder 112 and the test target 116. In alternative embodiments, the optical system 104 may have an inverted or bottom reading configuration, wherein the objective lens 132 is located below the target holder 112 and the test target 116. The optical system 104 may be configured to capture transmitted light or reflected light from the test target 116. In the illustrated embodiment, the optical system 104 is configured for transillumination, such that the illumination light path and the imaging light path are located on opposite sides of the target holder 112 and the test target 116. In alternative embodiments, the optical system 104 may be configured such that the illumination light path and the imaging light path are on the same side of the target holder 112 and the test target 116. For example, the objective lens 132 may be positioned in the illumination light path and the imaging light path, such as an epi-illumination or epi-fluorescence configuration as understood by those skilled in the art.

[0069] The optical system 104 may include a target fixture or assembly 136, of which the target holder 112 is a part. The target holder 112 is mounted, attached or otherwise mechanically referenced to a target stage (or positioning device) 140. A precise reference 160 may be used to match the optical system 104 with the test target stage assembly 106. The target stage 140 may be configured to be accurately moved in a known manner by electric or non-electric (and automatic and / or manual) actuation. The target stage 140 may be configured to translate along one or more axes and / or rotate around one or more axes. For example, the target stage 140 may be configured to move with five degrees of freedom (DOF), wherein the target stage 140 lacks freedom to move along the Z axis (lack of focusing movement). In an embodiment where the illumination light is directed from below the target holder 112 (as shown), the target holder 112 may include a window or aperture 188 to enable the illumination light to pass through, and the test target 116 may be mounted on such a window or aperture.

[0070] The light source 120 can be any incoherent light source suitable for optical microscopy. In contrast, a coherent source can produce ESFs that are incompatible with current analytical methods. Examples of incoherent light sources 120 include, but are not limited to, broadband light sources (e.g., halogen lamps, incandescent lamps, etc.), light emitting diodes (LEDs) or phosphor-type materials pumped by LED light, lasers, incandescent lamps, etc. In some embodiments, the light source 120 can include a plurality of light emitting units (e.g., LEDs) configured to emit light of different wavelengths, and a mechanism configured to enable the selection of light emitting units to be active at a particular time. In the illustrated embodiment, the light source 120 is integrated with the target holder 136 (and therefore moves with it) and is positioned at a fixed distance from the target holder 112 and the test target 116 along the Z axis. In other embodiments, the light source 120 can be separated from the target holder 136. In some embodiments, a condenser (not shown) can be positioned between the light source 120 and the target holder 112 and the test target 116 to gather the illumination light from the light source 120 to enhance the illumination of the test target 116, as understood by those skilled in the art. The angle of the illumination light, especially the numerical aperture (NA) of the illumination light and the uniformity of the radiation intensity will affect the accuracy of measuring the PSF. An ideal light source has a NA that exceeds the object space NA of the optical system by a small margin and exhibits uniform radiation intensity with respect to the angle.

[0071] The imaging devices 124 and 128 are multi-pixel (or pixelated) imaging devices in which the sensor is an array of pixels (e.g., based on charge coupled device (CCD) or active pixel sensor (APS) technology). Thus, the imaging devices 124 and 128 may be cameras of the type commonly used in microscopy and other imaging applications. In the illustrated embodiment, each of the imaging devices 124 and 128 schematically depicts an imaging device assembly including an imaging device unit (including the sensor, i.e., the pixel array) mounted to an imaging device stage (or positioning device). Each imaging device stage may be configured for six DOF motion, i.e., translation and rotation along all three (X, Y, and Z) axes.

[0072] The imaging devices 124 and 128 can be positioned at any nominal angle and distance relative to each other. In the illustrated embodiment, the first imaging device 124 is positioned coaxially with the objective lens 132 (along the Z axis), and the second imaging device 128 is positioned on an axis (on the X axis in the illustrated embodiment) that is angled (90 degrees in the illustrated embodiment) with the Z axis. The optical system 104 includes an image separation mirror 144 (e.g., a beam splitter or a dichroic mirror) located on an axis between the objective lens 132 and the first imaging device 124. The image separation mirror 144 is configured to separate an image beam propagating from the test target 116 into a first image beam portion propagating along a first separation imaging optical path from the image separation mirror 144 (via transmission through the image separation mirror 144) to the first imaging device 124, and a second image beam portion propagating along a second separation imaging optical path from the image separation mirror 144 (via reflection from the image separation mirror 144) to the second imaging device 128. Separating or dividing the image beam into the first image beam portion and the second image beam portion can be based on wavelength (ie, color) within the operating range of the electromagnetic spectrum. For example, the first image beam portion can be the blue portion of the image beam, and the second image beam portion can be the red portion of the image beam.

[0073] In the illustrated embodiment, the optical system 104 may include a filter assembly 148 (i.e., a wavelength selector) located in the imaging light path between the objective lens 132 and the imaging devices 124 and 128, or specifically between the objective lens 132 and the image separation mirror 144. In the illustrated embodiment, the filter assembly 148 may represent a plurality of filters mounted to a filter stage that is configured to selectively switch different filters into and out of the imaging light path by linear translation (e.g., filter slider) or rotation (e.g., filter wheel), such as Figure 1 As shown by the double-headed horizontal arrow in , the wavelength of the image beam portion to be received by the imaging devices 124 and 128 respectively can be selected.

[0074] The filter assembly 148 can be used to implement multi-channel imaging, each channel is associated with a different wavelength (i.e., color), and each imaging device 124 and 128 is used for one or more channels. As a non-exclusive example, the optical system 104 can be configured to acquire images in four channels (e.g., blue, green, red, and amber), wherein the first imaging device 124 is designated for two of the channels (e.g., blue and green), and the second imaging device 128 is designated for the other two channels (e.g., red and amber). The switching (positioning) of the filter assembly 148 can be coordinated with the operation of the imaging devices 124 and 128 and other components of the optical system 104 as needed to sequentially acquire images in the four channels. In a specific example, a four-channel configuration can be used for DNA sequencing, where different fluorescent labels have been associated with different nucleobases (adenine, cytosine, guanine, thymine) in the sample being analyzed.

[0075] Depending on the embodiment being tested, the optical system 104 may also include one or more other types of optical components in the illumination light path and / or the imaging light path. Examples include, but are not limited to, field lenses, relay lenses, beam expanders, beam collimators, apertures, slits, pinholes, confocal disks, etc. In the illustrated embodiment, the optical system 104 includes a first tube lens assembly 152 positioned in a first split imaging light path between the image splitting mirror 144 and the first imaging device 124, and a second tube lens assembly 156 positioned in a second split imaging light path between the image splitting mirror 144 and the second imaging device 128. In the illustrated embodiment, the first tube lens assembly 152 may represent a first tube lens mounted to a tube lens stage that is configured to adjust (translate) the relative positions of elements within the first tube lens along an axis of the first split imaging light path, such as Figure 1 The second tube lens assembly 156 may represent a second tube lens mounted to a tube lens stage configured to adjust (translate) the relative positions of the elements within the second tube lens along the axis of the second split imaging optical path, as shown in FIG. Figure 1 An adjustable tube lens can be used to implement infinity-corrected optics, as will be appreciated by those skilled in the art.

[0076] The optical system 104 may include a suitable reference datum configured to serve as a reference datum for defining the positions of various adjustable components (e.g., target holder 112 / test target 116, objective lens 132, etc.) of the optical system 104. In the illustrated embodiment, a fixed position structure of the optical system 104 (e.g., a surface of a housing 160 that houses the various components of the optical system 104) may serve as a reference datum.

[0077] Typically, the system controller (or controller) 108 is configured to control the operation of the various components of the optical system 104. This control includes monitoring and controlling (adjusting) the positions of the adjustable components of the optical system 104, and coordinating or synchronizing the operation of the adjustable components with other components such as the light source 120 and the imaging devices 124 and 128. The system controller 108 is also configured to process the imaging data output by the imaging devices 124 and 128 (e.g., data acquisition and signal analysis, including digitizing and recording / storing images, formatting images for display on a display device (such as a computer screen, etc.)). The system controller 108 is also configured to run performance tests on the optical system 104 according to the methods disclosed herein, including executing any algorithms associated with the methods. The system controller 108 is also configured to provide a user interface as needed to facilitate the implementation of the operation and performance tests of the optical system 104. For all of these functions, the system controller 108 Figure 11 is schematically depicted as including an electronic module 164 in communication with the optical system 104 and a computing device 168 in communication with the electronic module 164. The electronic module 164 and the computing device 168 will be understood to represent all hardware (microprocessors, memory, circuits, peripherals, etc.), firmware, and software components suitable for performing all of these functions, as will be understood by those skilled in the art.

[0078] In an embodiment, the electronic module 164 may represent a component dedicated to controlling the optical system 104 and running performance tests, while the computing device 168 may represent a more general computing device that is configurable or programmable to interface with the electronic module 164 to facilitate controlling the optical system 104 and running performance tests. The system controller 108 (electronic module 164 and / or computing device 168) may include a non-transitory computer-readable medium that includes non-transitory instructions for performing the methods disclosed herein. The system controller 108 (electronic module 164 and / or computing device 168) may include a main electronic processor that provides overall control, and one or more electronic processors configured for dedicated control operations or specific signal processing tasks. The system controller 108 (typically at least the computing device 168) may include one or more types of user interface devices, such as user input devices (e.g., keyboards, touch screens, mice, etc.), user output devices (e.g., display screens, printers, visual indicators or alarms, sound indicators or alarms, etc.), graphical user interfaces (GUIs) controlled by software, and devices for loading electronic processor-readable media (e.g., non-transitory logic instructions embodied in software, data, etc.). System controller 108 (and typically at least computing device 168) may include an operating system (e.g., Microsoft software).

[0079] It should be understood that Figure 1 1 is a high-level schematic diagram of the optical imaging performance test system 100 disclosed herein. As will be appreciated by those skilled in the art, other components such as additional structures, devices, and electronic devices may be included as required for actual implementation, depending on how the test system 100 is configured for a given application.

[0080] Figure 21 is a schematic perspective cross-sectional view of an example of a target holder 136 according to an embodiment. The target holder 112 may include a target support block 172, a clamping plate 176, and one or more fasteners 180. In the illustrated embodiment in which the light source 120 (an LED in the illustrated example) is integrated with the target holder 136, the target holder 136 may include a mounting member 184 for supporting and surrounding the light source 120 (and optionally serving as a heat sink). The mounting member 184 may be part of or attached to the target support block 172. The target support block 172 includes an annular shoulder for supporting the test target 116 and any suitable optical device (e.g., a window 188) disposed between the test target 116 and the light source 120. After the test target 116 is mounted to the target support block 172, a cover glass (or cover glass) 192 may be placed on the test target 116. As an example, the cover glass 192 may have a thickness of 500±5 μm. Clamp 176 (which may be configured as a spring) is then secured to target support block 172 using fastener 180 (eg, which may be a screw that engages a threaded hole of target support block 172 ) to secure test target 116 and cover glass 192 in a fixed position in target holder 136 .

[0081] Figure 3A 1 is a schematic plan view of a test target 116 according to an embodiment. The test target 116 may be or include a substrate of a planar geometric shape (e.g., plate-shaped) consisting of (or at least including an outer surface consisting of) a two-dimensional array (pattern) of a dark (light-absorbing) material 309 and bright (light-reflecting) target features 313 disposed on (or integrated with) the dark material 309. In this context, the terms "dark" and "bright" are relative to each other. That is, for implementations using reflected light illumination, the dark material is darker (more light-absorbing) than the target features 313, and the target features 313 are brighter (more light-reflecting) than the dark material 309. For implementations characterized by transmitted light illumination, the darker material is more light-absorbing, while the brighter material transmits light better (more light-transmitting). The target features 313 are preferably polygonal, thereby providing edges that show the contrast between the bright features 313 and the surrounding dark material 309. In particular, the target features 313 may be composed of straight lines (squares or rectangles). In an embodiment and as shown, target feature 313 is tilted (rotated) relative to the horizontal and vertical directions in the plane of test target 116 (the plane orthogonal to the optical axis), and in particular relative to the pixel arrays of imaging devices 124 and 128. Fig. 8A It is further shown in Fig. 8A is a detailed view of a portion of test target 116 implemented on the pixel arrays of imaging devices 124 and 128 .

[0082] In a non-exclusive example, dark material 309 is a fused quartz substrate (or a portion thereof), and target features 313 are a patterned chromium layer disposed on the substrate. The array of target features 313 is a 14 x 10 square array with 42 μm edges. Target features 313 are spaced (center to center) 92 μm apart on the long axis of the image and 94 μm apart on the short axis of the image. Target features 313 are tilted 100 mRad (about 5.73°) relative to the optical axis. The brightness of the image in dark field is 19%. The chromium is pointed at the objective lens.

[0083] The test system 100 may be used to perform a method of testing the imaging performance of the optical system 104. According to the method, the test target 116 is positioned at the object plane of the optical system 104 by securing the test target 116 in the target holder 112. Initial adjustments may be made to the positioning of various adjustable (on-stage) optical components (e.g., the target holder 112, the objective lens 132, the tube lenses 152 and 156, the imaging devices 124 and 128, etc.). In addition, the wavelength to be passed to the imaging devices 124 and 128 may be selected by adjusting the filter assembly 148. The light source 120 is then activated to generate illumination light to illuminate the test target 116, thereby causing the light emitted from the test target 116 to be imaged. The imaging light is focused into image beam portions by the objective lens 132 and the tube lenses 152 and 156 and is directed (separated by the image separation mirror 144) to the pixel arrays of the corresponding imaging devices 124 and 128 to acquire an image of the test target 116 (within the field of view of the objective lens 132) from the corresponding image beam portions. The imaging data is then transmitted from the imaging devices 124 and 128 to the system controller 108 for processing.

[0084] According to the method, the imaging data is used to calculate a plurality of edge spread functions at a plurality of locations within the test target 116. A model of the point spread function is then constructed from the calculated individual edge spread functions. From these point spread functions, a plurality of (estimated) values ​​of the enclosing rectangle energy EE are then calculated. Alternatively or additionally, the enclosing circle energy and / or Strehl ratio may be calculated.

[0085] Sharpness estimation (including bounding rectangle energy) from slanted edge objects

[0086] In an embodiment, the method entails fitting the experimental edge spread function (ESF) using the selected basis set so that the fit parameters can be used to create a model of the point spread function. From this model, various sharpness metrics can be derived.

[0087] Clarity Metric Definition

[0088] Enclosing rectangle energy

[0089] For a given PSF(x,y) centered at a point (x=0,y=0), the bounding rectangle energy is the portion of the PSF that falls within a box of area a centered at the same point:

[0090]

[0091] Encircling circle energy

[0092] For a given PSF (x, y) centered at a point (x=0, y=0), the bounding circle energy is the portion of the PSF that falls within a circle of radius r centered at the same point:

[0093]

[0094] Strehlby

[0095] For a given PSF, the Strehl ratio is the ratio of the maximum of the normalized PSF (here assumed to occur at (x=0; y=0)) to the maximum of a perfect, diffraction-limited PSF. In the case of a circular pupil, the diffraction-limited PSF is the Airy disk, and the Strehl ratio is:

[0096]

[0097] 0<Str.≤1 (5)

[0098] Line Spread Function (LSF) and Edge Spread Function (ESF)

[0099] One way to measure the PSF is to image an array of backlit holes as point sources, with the diameter of the holes significantly smaller than the Airy disk and the spacing between the holes large enough that the PSFs from adjacent point sources overlap to an insignificant degree. The challenges of this approach involve the precise fabrication of such tiny features against the background of a high OD target (especially when testing high NA microscopes) and the fact that the PSF decays as 1 / r in two dimensions. 2 Even though currently available sensors have a dynamic range of over 70 dB and 10,000 e -1 Even at full well depth, the PSF density a few pixels away from the PSF center may not be discernible, and the density in this area can quickly degrade the system signal-to-noise ratio (SNR). Alternative experimental protocols involve fluorescence illumination of dilute monodisperse coatings of quantum dots or individual fluorophores, which can generate reliable point sources but still fall victim to the same SNR constraints as well-hole objectives.

[0100] A common solution is to use an extended edge target instead of a pinhole, the so-called slanted edge series method is often used to generate modulation transfer function (MTF) measurements. Such a target exploits line symmetry to view a one-dimensional projection of the point spread function, called the line spread function (LSF):

[0101]

[0102]

[0103] Since the LSF decays as 1 / r, analysis of the LSF better captures the spread density of the PSF a few pixels from the center. Instead of using a pinhole to measure the PSF, the LSF is measured using the edge, and the resulting experimental curve is called the edge spread function (ESF). The edge can be aligned or tilted with the axis of the imaging system. First, consider an edge aligned with the y-axis, which is described by the transfer function E(x,y):

[0104]

[0105] This image is the 2-D convolution of the PSF with E(x,y) or equivalently the 1-D convolution of the LSF(x) with E(x):

[0106]

[0107]

[0108] Conceptually, it is straightforward to find LSF(x) as the derivative of the observed ESF(x) according to the following relationship:

[0109]

[0110] However, in practice numerical differentiation of noisy data may produce undesirable noise levels. In contrast, according to aspects of the present disclosure, Equation 12 is directly fitted as follows.

[0111] Approximation of the PSF using a separable function

[0112] Although measuring the LSF is more robust than measuring the PSF, the ultimate goal is to know the PSF. It is formally impossible to reconstruct the complete 2-D PSF from two 1-D LSFs recorded in orthogonal directions. Instead, according to one aspect of the present disclosure, the method (e.g., an algorithm that can be embodied in software executed by a control device) calculates an approximation of the PSF as follows:

[0113]

[0114] c = ∫PSF(x, y)dxdy (15)

[0115] This assumption is intolerable in two cases: (1) a very accurate well-corrected PSF model is required, e.g. looking at the location of the zero in the Airy disk; and (2) the PSF under evaluation is far from perfect (>1 / 4 wave RMS wavefront error) and contains substantial asymmetries that are not even about the y or x axis, e.g. the coma experienced at diagonal corners of the field, where |y| ≈ |x|.

[0116] The finite accuracy of the LSF x LSF approximation is Fig. 7A (Accuracy of the LSF x LSF approximation for various PSF defocuses). For the analysis, a collection of several known PSFs were projected into the horizontal and vertical LSFs and the approximate PSFs were recomputed from their outer products using Equation 14. The enclosing rectangle energy (EE4) across a 2 x 2 pixel region of the original and approximate PSFs was calculated. The original PSFs were real-world PSFs obtained from experimental wavefront error measurements of several field points of a high NA microscope system. For each of the nine wavefront error measurements, 31 PSFs were generated by adding a defocus term before integrating across the pupil to obtain the image space PSF. For the sharper PSFs, the LSF x LSF approximation consistently underestimates the EE of the actual PSF by about 0:05, but does so in a manner that is accurate enough that an empirical correction using polynomial fitting or other interpolation methods can be performed to restore accuracy. For the blurrier PSFs, the average correspondence between the LSF x LSF approximation and the true PSF is better, but the accuracy is below the mean with EE < 0.1. Despite the poor relative accuracy of the blurred PSFs, the blurred PSFs are clearly identified as such and are not confused with the sharp PSFs.

[0117] Overall, the approximation is accurate enough to enable a meaningful comparison of the system under test to the PSF resolution specification.

[0118] Before describing the ESF fitting process in detail, it is helpful to describe the form of the pixelated input data and touch upon practical issues associated with image registration.

[0119] Slanted edge target

[0120] Images of slanted edge targets are the only data source for PSF fitting for this method, where the edges are arranged in an array of squares, each with four edges. The use of slanted edge ESF targets is well established. Considering the ESF of a single edge, a region of data centered on the edge can be extracted and continued for an arbitrary number of pixels on either side of the edge, where the length is long enough to capture the entire ESF for a reasonably blurred PSF and also provide a good estimate of the baseline intensity level for both fully dark and fully bright portions of the image. For slanted edge targets, multiple ESFs can be combined, for example, if the edge is mostly vertical but has a 1 / a radian slant, where a has a value of about 10, then a collection of adjacent rows of the image can be collected and pooled so that each edge contains a unique copy of the ESF, shifted 1 / a pixels relative to adjacent rows. The resulting ESFs can then be viewed together and shifted in x by (row height) / a to align the midpoint of each ESF transition.

[0121] This dataset samples the ESF at intervals of 1 / a pixels, but does not inherently increase resolution, as each observation at a particular pixel is still an average over the pixel's active area. The pixel's active area and geometry, other than the pitch, may not be known for the particular sensor employed. The pixel's active area is assumed to be 100% fill factor. Therefore, the image around the observed vertically tilted ESF will be modeled as follows:

[0122]

[0123] In current practice, 64 rows of images spanning 32 pixels (16 pixels on either side of the edge) for slanted edge objects with a = 10 are pooled together for analysis. Outlier rejection eliminates a quarter of the rows based on the distance from the median of all rows together, which is calculated as l 2 Norm.

[0124] Image Registration

[0125] Coarse Image Registration

[0126] Edges are identified by first computing the single-frequency Fourier transform of the projection of the center of the image in the vertical and horizontal directions. The phase of the Fourier transform is used to estimate the overall displacement of the image by using the known dimensions of the target, assuming a nominal magnification.

[0127] The image is divided into adjacent tiles, and the centroid of each tile in x and the centroid of each tile in y are calculated along with the mean of each tile. The approximate position of each square of the image is known enough from the coarse shift of the FFT, and the centroid of each square is then calculated based on the pre-computed centroid values, providing an early estimate of the position of each square.

[0128] The global set of tile (x;y) coordinates is fit to linear models of rotation, translation, and magnification to generate a revised estimate of the tile position based on the global model.

[0129] Fine image registration

[0130] Each square is precisely located by identifying the center of the four edges and averaging the position information. The fit to the vertical edges is calculated by computing the numerical derivative along each row and cubing the result to identify the edge, then using the product as a weight for the centroid calculation to estimate the position of the edge along the row coordinates. The set of edge coordinates and row numbers are fit to a linear model of slope and offset using a robust statistical median method. This technique is repeated for all four edges, swapping rows and columns for horizontal edges. The set of edge offsets is used to estimate the center of the square, and the median of the slope is used to calculate the rotation angle of the square for the slanted edge calculation.

[0131] The fine registration fits of the squares are then fit again to a global model of overall image translation, rotation and magnification. The results are used to compute the relative orientation of the two cameras in a multi-camera system, specifically the relative image translation Δx; Δy and rotation θ. The magnification parameters are used to compute the overall magnification for each image. Distortion can be computed by modifying the fit function to include a specific distortion model or by jointly analyzing the fit residuals of a model specifying a specific distortion model, e.g. without distortion.

[0132] Fitting the PSF model to the ESF data

[0133] According to embodiments of the present disclosure, several methods may be utilized to acquire ESF data and create a PSF consistent with the LSF x LSF approximation (Eq. 14), as described below.

[0134] Direct outer product

[0135] LSF x (x) and LSF y (y) can be calculated directly from the numerical derivatives of the vertical and horizontal ESF data (Equation 13), and the outer product can be taken. The resulting matrix will oversample the PSF by a factor a, where the edges are tilted by 1 / a radians, and the image can be downsampled by integration to produce a pixelated version of the PSF.

[0136] However, in another embodiment, this method is not used for several reasons. First, it does not provide much data averaging and is sensitive to noise in the numerical derivatives, which is typically high. Second, this is an estimate of the pixelated PSF, while design specifications and testing often refer to a non-pixelated PSF as an output of common optical modeling software.

[0137] Interpolate ESF

[0138] The noise of the numerical derivatives can be reduced by fitting the ESF to N smooth continuous functions with known derivatives, where N is less than the number of total data points in the experimental ESF.

[0139]

[0140]

[0141] This helps with noise, but like existing methods it does not fit directly to the non-pixelated PSF.

[0142] Fitting the ESF to the PSF model

[0143] An improvement in fitting the ESF to a smooth function is to first assume a specific model for the PSF, denoted as PSF a , and then use this model to derive an analytical expression for the ESF. The key is that the PSF model is a separable function in terms of x and y. Using the inverse width b of the logarithmic interval i to choose the Gaussian basis set, but in principle other basis sets can be used.

[0144]

[0145]

[0146]

[0147]

[0148] Where (x, y) is the field coordinate of the point on the object plane of the test target 116, N g is the number of Gaussians used to form the basis set for modeling the PSF, a i is a set of coefficients that weigh the contribution of each Gaussian i in the model of Equation 20, and b i Proportional to the inverse square of the width of each Gaussian.

[0149] The non-pixelated ESF can then be directly calculated from the coefficients:

[0150]

[0151]

[0152]

[0153] where erf(x) is the Gaussian error function, i.e. the definite integral of every Gaussian, and is usually available in numerical libraries.

[0154] The pixelated ESF can be calculated by numerically integrating the non-pixelated ESF:

[0155]

[0156]

[0157] Note that the fitting function used to fit the LSF is symmetric about x0, so that the LSF x (x-x0)=LSF x (x0-x). This symmetry is not strictly necessary, but is used to reduce the fitting space and increase the equivalence of the rising ESF to the falling ESF (i.e., the ESF from the right side of the box to the ESF from the left side of the box). The upper and lower fitted regions of the ESF are statistically very different because the image from the high gain sensor is a shot noise limited counting process and the variance in the lower, darker part of the ESF data is lower than the variance at the top of the ESF data in an absolute sense. If a weighted linear least squares fit to the ESF is performed using the signal level (in photoelectrons) of each ESF data point as a weight equivalent to its variance, this will result in different fits to the left and right ESFs for the PSF with an LSF that is asymmetric about the origin.

[0158] Alternative algorithms may utilize different basis sets in Equation 20, which ignore the explicit symmetry used here. Such basis sets may be functions commonly used for interpolation, such as B-spline functions, polynomial functions, or combinations thereof, such as the Gaussian function employed in Equation 20, as well as odd-power polynomials or cosine functions of varying frequencies to allow fitting of asymmetric LSFs. Simultaneous linear least squares fits of rising and falling ESFs (e.g., the left and right edges of a block) may be used to fit asymmetric LSFs to asymmetric basis functions using shot noise-derived weights.

[0159] Numerical fitting

[0160] In practice, the image data is fitted using a linear least squares method. The QR decomposition is used as part of the known DGELS LAPACK (Linear Algebra Package) routine, as implemented in the INTEL MKL Math kernel library. The image data Img is fitted using the design matrix D to obtain the fitting coefficients Ai. There are several pre-computed versions of D, using nine different offsets x0 and each row of data will use the row of D corresponding to the x0 value that is closest to the x0 value of the data.

[0161] Img=DX (29)

[0162] dim[D]=(ND,N g +1) = (32, 7) (30)

[0163]

[0164] f H =4 (32)

[0165] s=0.4 (33)

[0166] The fitting coefficient X[0] represents the baseline intensity of the image due to background noise and leakage of the test target. If the fitted data is the result of ESF x (x), then the coefficients are X[1]; X[2]; :::; X[N g ] represents the coefficient a in Equation 20 x；1 , a x；1 ,:::,a x；Ng If the fitted data is the result of ESF y (y) horizontal edge, then the coefficients X[1], X[2]; :::; X[N g ] represents a in Equation 20 y，1 ; a y，1 ;:::;a y，Ng .

[0167] Reconstruct the PSF model and calculate the sharpness

[0168] The PSF model can be fitted with the coefficients a obtained in the previous section i Reconstruction. Several different models represent different levels of knowledge. The most complete model combines the vertical LSF and the horizontal LSF using Equation 20 and the bounding rectangle energy can be directly calculated from a simple product of the sum of the coefficients.

[0169]

[0170] The accuracy of this method is discussed below.

[0171] The bounding circle energy can also be calculated numerically by evaluating equations 3 and 20. The Strehl ratio takes the simple form:

[0172]

[0173] Alternative Simple PSF Model

[0174] Although the PSF is essentially a 2-D concept, it is useful to express the EE of the PSF, which is determined assuming that the vertical and horizontal LSFs are the same. These uses include finding the astigmatism of an image by comparing the focus position that maximizes the EE estimated from the horizontal ESF with the focus position that maximizes the EE estimated from the vertical ESF. For this purpose, a very limited number of fitting coefficients a are used. i A simple PSF model of .

[0175]

[0176]

[0177]

[0178] This form of data feedback is the primary method of performing system alignment and has proven to be very robust in operation.

[0179] The model of Equation 37 is chosen to be separable with respect to x and y so that a 2-D reconstruction of the PSF can be obtained from ESF data that only contains information about the one-dimensional LSF. Therefore, the model may not represent the complex PSF caused by highly aberrated or defocused systems, but is sufficient to describe the PSF of a well-compensated and in-focus system. The ESF can be further modified to reflect the degradation of the PSF through the contribution of the detector MTF, which is achieved in the worst case by averaging the exact ESF of Equation 38 over the entire length of each pixel.

[0180] Data fitting (e.g., based on linear least squares regression) is performed by fitting Equation 38 to the experimental data to obtain the coefficients a i The PSF model is obtained by using a i The EE value is constructed by evaluating Equation 37. The EE value is then calculated analytically by integrating Equation 37 over the block area to obtain Equation 39.

[0181] Algorithm accuracy

[0182] The accuracy of the entire algorithm is evaluated using synthetic images with known PSFs, using the same real-world PSFs used to evaluate the accuracy of the LSF x LSF approximation. The PSF is then convolved with an image of an infinitely sharp test target. The convolution represents an ideal, noise-free image with a grid of 10 x 14 diagonal squares. These squares provide test diversity because they cannot be superimposed on each other because the placement of each square relative to the camera pixel grid varies at the sub-pixel level depending on where the center of the square is located. To understand the effect of shot noise on the data, the image is first scaled to match the intensity in the actual image of the target, and a baseline is added to add the effect of system background intensity and test target bleed-through. The scaled image is expressed in units of photoelectrons. A shot noise sampled image is created from this scaled image by sampling the intensity of each pixel from a distribution calculated specifically for each pixel. The shot noise Poisson distribution is approximated by a normal distribution with a mean corresponding to the mean of the scaled image and a variance also equal to the mean of the image. The shot noise is sampled using a 2D pixel distribution per pixel. 16-1 image counts a scaling factor of 10,550 photoelectrons, the image is ultimately scaled to 12-bit depth as the high bit of a 16-bit TIFF.

[0183] After each image is created, it is run through the full analysis pipeline to estimate the bounding rectangle energy of each square using Equation 35. In addition, the PSF is modeled based on the fitting coefficients a x；i and a y；i Created to allow for direct comparison. Figures 4A-4F The result shows a reasonably clean PSF with a true EE of 0.594. The 3-D plot of the PSF depicts the true PSF ( Figure 4A ), PSF approximated from LSF x LSF ( Figure 4B ), and finally the PSF model recovered from analyzing the synthetic image and fitting the ESF data ( Figure 4C ). The differences between the three PSFs are as follows Figures 4D-4F , the same vertical scale is used. As expected, the PSF model is very similar to the LSF x LSF approximation. Note that the density of the true PSF at the bottom drops off more rapidly in the front right of the plot than in the front left of the plot. The LSF x LSF model preserves this property and the PSF model from the ESF data also faithfully recovers it. The LSF x LSF approximation leads to an underestimate of the peak and an overestimate of the horizontal rows and vertical columns emanating from the peak. This leads to an underestimate of the true EE and is unavoidable given the approximation. This results in the typical “+” shaped PSF model when viewing the PSF from above. Nonetheless, the faithful reconstruction of the LSF x LSF model and the overestimation of the EE based solely on the fit coefficients a x；i and a y；i Accurate calculation of EE indicates that the ESF fitting process operates as expected.

[0184] Fit two additional PSFs in the same way, as Figures 5A-5F 6A-6F, which depict the sharp and blurred PSFs, respectively. Note that, as predicted, the LSF x LSF model does not adequately capture the asymmetry of the blurred PSF.

[0185] use Fig. 7A The synthetic image analysis was repeated for nine different experimental PSFs calculated over a range of thirty-one different focus positions using the same PSF used in . Figure 7BA scatter plot of the EE estimated using Equation 35 relative to the EE for a known starting PSF is shown. Each point of the scatter plot represents an image consisting of 10x14 squares, each sampled independently with the expectation that when presented with multiple squares that differ in sub-pixel arrangement, they will produce the same estimate of EE as well as the noise generated by shot noise and algorithmic errors (if any). The error bars represent the standard deviation of all 140 measurements. The points show natural clustering at different locations, which is due to the PSF generation creating a series of similar PSFs around the best focus of each PSF.

[0186] The EE is underestimated by the algorithm, which is to be expected given the core LSF x LSF approximation. The EE estimates should be expected to match the LSF x LSF approximation exactly, but in this case they are slightly elevated. While this does make the overall estimate more accurate, it also provides an opportunity to improve the performance of the numerical methods for ESF fitting and EE estimation.

[0187] Figure 7B The accuracy of the EE estimates is quite acceptable for instrument testing purposes and demonstrates that the basic approach adopted by the method disclosed in this paper is capable of generating usable data and a useful testbed.

[0188] The accuracy of the algorithm

[0189] according to Figure 7B Judging by the relatively tight standard deviation of the EE estimate in , the accuracy of the algorithm is acceptable. However, using feedback for optical alignment places an additional level of demands beyond just tight standard deviation. When performing an optimization, typically very small perturbations are made to the system, and it is important that the feedback signal does not jump back and forth as a function of the adjustment variable. Figure 7C is a plot of EE versus focus position, illustrating that the algorithm provides a very smooth and predictable response curve to defocus perturbations. While the distance between the red and black lines (or the circular and triangular data points, respectively) suggests that EE is underestimated, it is underestimated systematically and consistently, and the inaccuracy of the method does not jump out of control as the defocus position changes from point to point. Therefore, this curve is suitable for higher-level interpretation and fitting, such as to determine the defocus position where EE is maximized.

[0190] As noted above, it is possible to approximate the LSF using alternative basis sets, including basis sets that allow fitting of asymmetric LSFs. If such methods are used, they may produce results that are more Figure 7B However, it is necessary to verify that Figure 7C The response curve of the form is smooth enough for optimization use. An unproven concern is that using a large number of fitting parameters will compromise smoothness. However, this hypothesis remains untested, and a higher accuracy model may be different from what is expected and actually be smoother or smoother.

[0191] EE score

[0192] The enclosing rectangle energy (EE) value can be used as an indicator of the imaging performance of the optical system 104 under test. The user can make initial settings or adjustments to the optical components of the system (e.g., alignment between components and between components and the test target 116). The user can then run a test of the optical system 104 according to the methods described herein. Based on the test results, the user can determine whether further adjustments should be made to improve the results (e.g., further optimize alignment and focus). After making any further adjustments, the user can run the test again. Iterations of testing and adjustments can continue until the results are deemed acceptable.

[0193] If all field points in the image exceed a predetermined minimum threshold EE value (e.g., EE>0.51), it can be determined that the optical system 104 has successfully passed the imaging performance test. However, evaluating imaging performance based solely on whether all EE values ​​pass may be considered disadvantageous in some cases. This approach may be problematic in cases where the optical system 104 exhibits some dark spots but is otherwise a clear (high image quality) system. In addition, since optical performance is continuously evaluated as the alignment process progresses, the discontinuity created by using a threshold makes the threshold method difficult to use as an optimization process where a continuous metric is preferred (e.g., alignment laser power, or alignment minimum root mean square (RMS) wavefront error), and the threshold effect may be detrimental to manufacturing yield calculations.

[0194] To overcome the above potential disadvantages, in a further embodiment of the disclosed method, an EE score ES is calculated based on the EE value. The EE score describes the entire field with a single continuous variable that needs to exceed (or reach or exceed) a predetermined minimum threshold to pass the imaging performance test. For example, the EE score can be weighted so that ES ≥ 100 is a passing score. The EE score has two components: <ee>is the spatial average of all EE values ​​observed in all field points over a specified depth of field; and the overlap integral EO measures the consistency of EE between different imaging channels (e.g., four channels corresponding to the four colors into which the image is filtered, where in the illustrated embodiment, two imaging devices 124 and 128 are used in conjunction with an adjustable filter assembly 148 to each acquire images in two of the four channels). EO is rewarded if the sharp and blurred regions coincide in field position (x, y) and focus position (z). EO is penalized if the four channels are inconsistent, for example, if the focus of the two imaging devices 124 and 128 relative to each other is not good enough, or if the blue channel is sharp in the upper left corner and blurred in the lower right corner while the red channel is the opposite, etc. The EE score and its components are calculated according to equations 41-44 below.

[0195]

[0196]

[0197]

[0198] where x and y again represent arrays of size N x ×N y , c is the color (e.g., in a four-channel embodiment, colors 1, 2, 3, and 4 might correspond to blue, green, red, and amber), z is the focal position, and nm is nanometers.

[0199] Although the EE score considers contributions from all channels, it is also useful to consider a single channel at a time and only consider the homogeneity of field position and focus position to have similar scores. c This is an example where the heterogeneity of a single channel is captured in Equation 46, similar to the mean squared error.

[0200]

[0201]

[0202] If the integral in Equation 46 is zero, then the penalty represented by the heterogeneity EH will be zero.

[0203] Fit EE relative to Z

[0204] The integrals in equations 42, 43, and 46 are intended to characterize the focused imaging performance and can be calculated using a simple sum of experimental data. Alternatively, the integral can be calculated by first fitting the experimental pairs of EE values ​​and their corresponding z positions to a Gaussian distribution (Equation 49). The integral is then calculated as an analytical solution to the definite integral of the Gaussian fit function according to equations 49-55 below.

[0205] EE(z)=aexp(-(z-z0) 2 / w 2 ) (49)

[0206]

[0207]

[0208]

[0209]

[0210] Where a is the peak EE value obtained at the focus, w is the Gaussian width and z0 is the z value corresponding to the peak EE value. This is particularly useful because it allows relatively coarse z steps to be taken (thus reducing data acquisition) while still accurately capturing z0 to within a small fraction of the z step. Equation 55 can be used to calculate the integral in Equation 42 above.

[0211] For EO calculations, the overlap integral between different channels in Equation 44 can also be calculated using analytical integration of the Gaussian fit of the experimental data, as shown in Equations 56-63:

[0212]

[0213]

[0214]

[0215]

[0216] where w1 and w2 are the widths of the Gaussians corresponding to channels 1 and 2. The product of the two Gaussians is itself p is the center and the width is w p The third Gaussian of . Equations 56-63 can be used to calculate equation 47 above.

[0217] The penalty indicates how much performance is lost for various optical defects.

[0218] In addition to estimating the current EE score of the optical system 104, the method may entail communicating to the user how much potential score is lost due to one or more artifacts or optical defects (e.g., aberrations). In this way, the method can inform the user which specific defects have the most negative impact on imaging performance. To this end, the method can calculate one or more penalties associated with one or more corresponding artifacts. The penalty is the difference between the current EE score and the score that would be obtained after taking into account the defect. The method may include displaying information corresponding to the penalty on an area of ​​the image displayed to the user. When only considering <ee>When the contribution to the EE score is greater (with no overlapping EO score), the penalty calculation should always result in a greater <ee>The penalty is expressed as the difference between the actual score and the artificially increased score and should always be a negative number. The penalty calculation may sometimes result in a positive number when accounting for overlapping score EOs.

[0219] One example of a penalty is a penalty from imaging device (e.g., camera) Z-shift. In calculating the penalty, the method may assume that both imaging devices 124 and 128 can be optimally focused relative to each other, but does not assume that the axial chromatic aberration within imaging devices 124 and 128 is zero. The method calculates the ideal focus position of the first imaging device 124 as the average of two sets of wavelength-filtered imaging data acquired by the first imaging device 124 (e.g., the average of blue and green data). The method also calculates the ideal focus position of the second imaging device 128 as the average of two sets of wavelength-filtered imaging data acquired by the second imaging device 128 (e.g., the average of red and amber data).

[0220] Another example of a penalty is a penalty from tilt of an imaging device (e.g., a camera). For each channel, the method constructs a focal plane as the average of two colors (e.g., blue and green in channel 1), fits it to the plane using a linear least squares method to find the tips and tilted parts, which are then removed from the focal plane while preserving the focus difference for each imaging device 124 and 128.

[0221] Another example of a penalty is the penalty from astigmatism. The EE scores for the horizontal LSF and vertical LSF are calculated independently, making the focal plane tilt and height in each direction optimal. These maps are then averaged together to create the final estimate.

[0222] Another example of a penalty is the penalty from axial chromatic aberration. This penalty is calculated twice, once for each channel. For channel 1, the best focus for both colors (e.g., blue and green) is found, the difference is divided, and each channel is offset by that amount. Due to imperfect camera-to-camera focus differences, the final focus position may still not be ideal.

[0223] Another example of a penalty is the penalty from field flatness. This penalty is calculated twice, once for each channel. For channel 1, the best focus planes of the two color exposures (e.g. blue and green exposures) are averaged together and the best fit plane is subtracted from the focus position of the (e.g. blue and green) data, while channel 2 is processed normally. This penalty can have conflicting interpretations. For example, if both channels are equally non-flat, they will both be penalized. <ee>EO, but overlapping EO will enhance it in a productive way. Flattening the channels will eliminate both effects. <ee>effect will dominate, but this is not guaranteed.

[0224] Fig. 8A is an example of an image acquired from one of the object features 313 of the test object 116 . Figure 8B is from Fig. 8A Example of ESF calculation for an image of . Figure 8B , image coordinate zero corresponds to one of the edges of imaged target feature 313. Figure 8B Some of the data points in are outliers. As shown, one or more of these outliers may correspond to dark artifacts (e.g., dirt or debris from manufacturing) on ​​the target feature 313. The method may include rejecting (e.g., ignoring or removing) outliers in the ESF data.

[0225] Fig. 9 Yes Figure 8B Example of the result of fitting the ESF to the error function and calculating the EE from the ESF.

[0226] Fig.10 is an example of calculating the ESFs of the four edges of the target feature 313, fitting the ESFs to the error function, and calculating four independent EE values ​​from the corresponding ESFs. Nominally, the left and right edges should result in the same measurement (EE left =EE right ), the upper and lower edges should result in the same measurement (EE up =EE down ). However, the left and right edges are not necessarily measured the same as the top and bottom edges.

[0227] In an alternative embodiment, the enclosing circle energy may be used instead of the enclosing rectangle energy as the imaging performance indicator. In a further alternative embodiment, the Strehl ratio may be used instead of the enclosing rectangle energy as the imaging performance indicator.

[0228] Fig.11 1100 is a flowchart illustrating an example of a method for testing the imaging performance of an optical system (such as the optical system 104 described herein) according to an embodiment of the present disclosure. As shown, the method is initiated by moving the Z stage of the objective lens to an approximate focus position. The user then initiates a Z-stack acquisition process by which multiple images of a test target are acquired at different focus positions. The hardware of the optical system then performs a Z-stack acquisition, Fig.12 An embodiment thereof is further described in . The Z-stack range is then compared to the focus performance during the Z-stack acquisition. The Z focus position is then updated based on the Z-stack results. It is then determined whether focus is found in the middle third of the Z-stack range. If so, the method determines that the currently acquired data is available, performs global calculations on the Z-stack, and notifies the user of the new data generated.

[0229] On the other hand, if it is determined that focus has not been found in the middle third of the Z-stack range, then an imaging performance value (e.g., a bounding rectangle energy estimate as described herein) is calculated from the currently acquired data, but the user is warned that the data is incomplete or unreliable because focus could not be found in the middle of the range. The Z-stack acquisition process can then be repeated and a more informed guess is made about the focus position obtained from that z-stack.

[0230] Fig.12 1200 is a flowchart illustrating the acquisition of a Z-stack of images according to an embodiment of the present disclosure. When a user initiates a Z-stack acquisition ( Fig.11 ), a series of Z positions are calculated around the initial focus guess. For each field of view FOV i, in the set of FOVs, the Z stage is commanded to move to the defocused position corresponding to FOV i. The actual position of the Z stage is then queried and recorded. Images from all imaging devices are then acquired simultaneously. High-speed processing of each image is then performed individually, an embodiment of which is described in Fig.13 The image data is then pooled. Multiple PSF models are then constructed, one for each ESF, by fitting the ESF to a basis set of Gaussian error functions, as described herein.

[0231] Fig.13 1300 is a flowchart illustrating high-speed processing of an image itself according to an embodiment of the present disclosure. The image is downsampled to representations of intensity, horizontal centroid, and vertical centroid. A rough global alignment is then performed using the downsampled image. Each block (or other type of target feature of the test target) is located using the centroid data. Multiple block locations are then fit to a global model of translation, rotation, and magnification. ESFs are then extracted from each edge of each block, and the edge angles and centers are calculated. Outliers are then removed from the ESF data. Multiple PSF models are then constructed by independently fitting each ESF to a basis set of Gaussian error functions, as described herein. Imaging performance values ​​(e.g., bounding rectangle energy estimates as described herein) are then calculated for each ESF. The data can be averaged (EE horizontal =(EE left =EE right ) / 2,EE vertical =(EE up =EE down ) / 2). Fig.13 The process shown can be repeated for multiple images, and the resulting image data can be pooled ( Fig.12 ) and further processed.

[0232] In an embodiment, one or more of flowcharts 1100, 1200, and 1300 may represent an optical imaging performance test system or a portion of an optical imaging performance test system configured to perform (e.g., control or execute) the above-described and Fig.11 , 12 and 13. For this purpose, a controller (e.g., Figure 1 The controller 108 shown in FIG. 1 ) (including a processor, memory, and other components as understood by those skilled in the art) controls the execution of the steps or functions, for example by controlling the components of the test system involved in executing the steps or functions. As understood by those skilled in the art, this may involve receiving and processing user input and / or machine executable instructions.

[0233] Fig.14 An example of a set of EE performance graphs that can be generated according to the method and can be displayed to a user on a display screen of a computing device (e.g., computing device 168) is illustrated. The columns of the graphs correspond to different colors, in this example, blue, green, amber, and red from left to right. In the top row (row 1), each graph shows the average EE for each point in the field of view (FOV). In the next row (row 2), each graph shows the best EE found at any defocus value for each point in the FOV. In the next row (row 3), each graph shows the surface of best focus determined by the EE. In the next row (row 4), each graph shows the astigmatism of horizontal and vertical imaging data determined by the EE. In the bottom row (row 5), each graph shows the depth of field as the z-range where the EE exceeds the minimum value.

[0234] Fig.15 An example of a combined EE score and performance graph that can be generated according to the method and can be displayed to a user on a display screen of a computing device (e.g., computing device 168) is illustrated. The top graph (1) is a global EE graph that shows the available EE across all channels and defocus positions. Below the top graph (1), a set of graphs (2) shows the contribution of various optical defects, such as those described above. Thus, these graphs (2) inform the user how much better the imaging performance will be if the user resolves the relevant defects. In this example, the first row of the set of graphs (2) shows the global score in different colors, in this example, blue, green, amber, and red from left to right. The second row of the set of graphs (2) shows, from left to right, the penalty for overlap, imaging device Z displacement on EE, the penalty for axial chromatic aberration in channel 1 on EE, and the penalty for axial chromatic aberration in channel 2 on EE. The third row of the set of graphs (2) shows, from left to right, the penalty for astigmatism on EE, the penalty for imaging device tilt on EE, the penalty for field flatness in channel 1 on EE, and the penalty for field flatness in channel 2 on EE.

[0235] Fig.16 1 is a schematic diagram of a non-limiting example of a system controller (or controller) 1600 that may be part of or in communication with an optical imaging performance test system according to an embodiment of the present disclosure. For example, the system controller 1600 may correspond to the system controller described above and as Figure 1 The test system 100 is shown with a system controller 108 .

[0236] In the illustrated embodiment, the system controller 1600 includes a processor 1602 (typically based on an electronic device), which can represent a main electronic processor that provides overall control, and one or more electronic processors (e.g., a graphics processing unit or GPU, a digital signal processor or DSP, an application-specific integrated circuit or ASIC, a field programmable gate array or FPGA, etc.) configured for dedicated control operations or specific signal processing tasks. The system controller 1600 also includes one or more memories 1604 (volatile and / or non-volatile) for storing data and / or software. The system controller 1600 may also include one or more device drivers 1606 for controlling one or more types of user interface devices and providing interfaces between the user interface devices and the components of the system controller 1600 that communicate with the user interface devices. Such user interface devices may include user input devices 1608 (e.g., keyboards, keypads, touch screens, mice, joysticks, trackballs, etc.) and user output devices 1610 (e.g., display screens, printers, visual indicators or alarms, audible indicators or alarms, etc.). In various embodiments, the system controller 1600 may be considered to include, or at least be in communication with, one or more of the user input devices 1608 and / or the user output devices 1610. The system controller 1600 may also include one or more types of computer programs or software 1612 contained in memory and / or on one or more types of computer-readable media 1614. The computer programs or software may include non-transitory instructions (e.g., logic instructions) for controlling or executing various operations of the test system 100. The computer programs or software may include application software and system software. The system software may include an operating system (e.g., Microsoft Windows) for controlling and managing various functions of the system controller 1600, including the interaction between the hardware and the application software. In particular, the operating system may provide a graphical user interface (GUI) that may be displayed via user output device 1610 and with which a user may interact using user input device 1608 .

[0237] The system controller 1600 may also include one or more data acquisition / signal conditioning components (DAQ) 1616 (eg, embodied in hardware, firmware, and / or software) for receiving and processing signals from the optical system under test (eg, as described above and as described above). Figure 1 DAQ 1616 may also be configured to transmit control signals to the optical system to control the movement / positioning of adjustable optical components, such as those described herein. DAQ 1616 may correspond to the above-described and Figure 1 All or part of the electronic module 164 shown.

[0238] The system controller 1700 may also include a data analyzer (or module) 1618 that is configured to process the signals output from the optical system and generate data therefrom, including imaging performance metrics, scores, graphs, etc., as described throughout this disclosure. Thus, the data analyzer 1618 may be configured to implement (control or execute) all or part of any of the methods disclosed herein. For these purposes, the data analyzer 1618 may be embodied in software and / or electronic devices (hardware and / or firmware) as understood by those skilled in the art. The data analyzer 1618 may correspond to the above-described and Figure 1 All or part of the electronic module 164 and / or computing device 168 as shown.

[0239] It should be understood that Fig.16 1 is a high-level schematic diagram of an example of a system controller 1600 consistent with the present disclosure. Other components may be included as desired for the actual implementation, such as additional structures, devices, electronic devices, and computer-related or electronic processor-related components. It will also be understood that the system controller 1600 is not limited to the system controller 1600 of FIG. 1 . Fig.16 1600 are schematically represented as functional blocks intended to represent structures (e.g., circuits, mechanisms, hardware, firmware, software, etc.) that may be provided. The various functional blocks and any signal links between them have been arbitrarily positioned for purposes of illustration only and are not limiting in any way. Those skilled in the art will appreciate that, in practice, the functionality of the system controller 1600 may be implemented in a variety of ways, not necessarily in a single manner. Fig.16 The exact manner shown in and described here by way of example is achieved.

[0240] Exemplary Embodiments

[0241] Exemplary embodiments provided in accordance with the presently disclosed subject matter include, but are not limited to, the following:

[0242] 1. A method for testing the imaging performance of an optical system, the method comprising: positioning a test target on an object plane of the optical system; operating the optical system to illuminate the test target and generate an image beam; operating a focusing stage of the optical system to acquire a plurality of images of the test target corresponding to a plurality of defocus values ​​from the image beam; calculating a plurality of edge spread functions at a plurality of positions within the test target from each image; constructing a plurality of corresponding point spread function models from the edge spread functions; and calculating a plurality of imaging performance values ​​corresponding to the plurality of positions based on the point spread function models, wherein the imaging performance values ​​are based on an indicator selected from the group consisting of: enclosing rectangle energy; enclosing circle energy; and Strehl ratio.

[0243] 2. The method of embodiment 1, wherein the plurality of positions comprises a plurality of field coordinates (x, y) in the object plane within the test target.

[0244] 3. A method as described in any of the preceding embodiments, wherein the multiple positions include multiple focus positions (z) along an optical axis passing through the test target, and operating the optical system includes: acquiring multiple images of the test target at different focus positions (z).

[0245] 4. The method of any of the preceding embodiments, comprising: generating one or more graphs of imaging performance based on the combination of the imaging performance values.

[0246] 5. The method of Example 4, comprising: comparing two or more of the figures to provide a measure of the relative alignment of the focal plane of each imaging device or channel with respect to the object plane.

[0247] 6. A method as described in Example 4 or 5, wherein the one or more figures correspond to different imaging channels, and the different imaging channels correspond to different imaging devices of the optical system operated to acquire images, or different wavelengths of the acquired images, or both different imaging devices and different colors.

[0248] 7. The method of Example 6, comprising: comparing two or more of the figures to provide a measure of the relative alignment of each imaging device or channel with respect to each other; and both of the above.

[0249] 8. The method as described in any one of Examples 4-7 includes: after generating the one or more images, adjusting the position of one or more optical components of the optical system or replacing the one or more optical components based on information provided by the one or more images.

[0250] 9. A method as described in Example 8, wherein the one or more optical components are selected from the group consisting of: one or more imaging devices; an objective lens of the optical system; one or more tube lenses of the optical system; one or more reflectors or dichroic mirrors; and a combination of two or more of the foregoing.

[0251] 10. A method as described in Example 8 or 9, wherein the one or more images are one or more initial images, and the method further includes: after adjusting or replacing the one or more optical components, obtaining a new image of the test target, calculating multiple new imaging performance values, and generating one or more new images of the imaging performance.

[0252] 11. The method of embodiment 10, comprising: comparing the one or more new images with the one or more initial images to determine positional adjustments to be made to the one or more optical components to optimize imaging performance.

[0253] 12. The method of any one of embodiments 8-11, wherein the adjusting or replacing finds a pair of optimal conjugate image planes in the optical system;

[0254] 13. A method as described in any of Examples 8-12, wherein the adjustment or replacement improves a property selected from the group consisting of: focus matching of each imaging device; tilt of the imaging device; flattening of field curvature; reduction of astigmatism; reduction of wavelength-dependent focus shift; and a combination of two or more of the above.

[0255] 14. The method of any one of the preceding embodiments, comprising: calculating one or more global scores of imaging performance based on the combination of the imaging performance values.

[0256] 15. The method as described in Example 14 includes: modifying the one or more global scores to penalize or reward the heterogeneity of the imaging performance values ​​within the test target at a series of field coordinates (x, y) in the object plane, or the heterogeneity of the imaging performance values ​​at a series of focal positions (z) in the object plane, or both.

[0257] 16. The method of embodiment 14 or 15, comprising modifying the one or more global scores to penalize or reward the similarity of different imaging channels as a function of the field coordinates (x, y) in the object plane within the test target or the focal position (z) of the object plane, or both, wherein the different imaging channels correspond to different imaging devices of the optical system operated to acquire images, or different wavelengths of the acquired images, or both different imaging devices and different colors.

[0258] 17. The method as described in any of the preceding embodiments comprises: after calculating the imaging performance value, adjusting the position of one or more optical components of the optical system based on information provided by the imaging performance value.

[0259] 18. A method as described in Example 17, wherein the imaging performance value is an initial imaging performance value, and the method further includes acquiring a new image of the test target after adjusting the one or more optical components and calculating a plurality of new imaging performance values.

[0260] 19. The method of embodiment 18, comprising: comparing the new imaging performance value with the initial imaging performance value to determine a position adjustment to be made to the one or more optical components to optimize the imaging performance.

[0261] 20. The method of any preceding embodiment, wherein positioning the test target comprises aligning the target relative to a fiducial shared by one or more optical components of the optical system.

[0262] 21. A method as described in any of the preceding embodiments, wherein operating the optical system includes: utilizing an objective lens in the image beam, and the method further includes: adjusting the position of the objective lens along the axis of the image beam to obtain multiple images of the test target at different focal positions (z).

[0263] 22. The method of embodiment 21, wherein the objective has a configuration selected from the group consisting of: the objective is configured for infinite conjugate microscopy; and the objective is configured for finite conjugate microscopy.

[0264] 23. The method of any preceding embodiment, wherein operating the optical system comprises operating two or more imaging devices to acquire corresponding images of the test target.

[0265] 24. The method of embodiment 23, wherein the two or more imaging devices acquire corresponding images at two or more different wavelengths.

[0266] 25. The method of embodiment 24, comprising: splitting the image beam propagating from the test target into two or more image beam portions, and transmitting the two or more image beam portions to the two or more imaging devices, respectively.

[0267] 26. The method of any preceding embodiment, wherein operating the optical system comprises operating an optical filter assembly to filter the image beam at a selected wavelength.

[0268] 27. A method as described in any of the preceding embodiments, wherein operating the optical system includes: utilizing a tube lens in the image beam, and the method further includes: adjusting the relative position of one or more lenses or lens groups within the tube lens to obtain multiple images of the test target at different positions of the tube lens.

[0269] 28. The method of any preceding embodiment, wherein the test target comprises a dark material and an array of light features disposed on the dark material.

[0270] 29. The method of embodiment 28, wherein the bright features are polygonal.

[0271] 30. The method of embodiment 29, wherein the bright features are tilted such that the edges of the bright features are oriented at an angle relative to a pixel array of the optical imaging system that captures the image.

[0272] 31. An optical imaging performance testing system, comprising: a target holder configured to hold a test target; a light source configured to illuminate the test target; an imaging device configured to acquire an image of the test target; an objective lens located in an imaging light path between the test target and the imaging device, wherein the position of at least one of the objective lens or the target holder can be adjusted along the imaging light path; and a controller comprising an electronic processor and a memory, and configured to control the following steps of a method as in any of the foregoing embodiments: calculating multiple edge spread functions, constructing multiple point spread function models, and calculating multiple imaging performance values.

[0273] 32. The system of embodiment 31, wherein the objective has a configuration selected from the group consisting of: the objective is configured for infinite conjugate microscopy; and the objective is configured for finite conjugate microscopy.

[0274] 33. A system as described in Example 31 or 32, wherein the imaging device includes multiple imaging devices, and also includes an image separation mirror, which is configured to split the imaging light path into multiple imaging light paths, and the multiple image light paths respectively point to corresponding imaging devices.

[0275] 34. The system of any one of embodiments 31-33, comprising a filter assembly configured to select a wavelength of an image beam in the imaging light path for propagation to the imaging device.

[0276] 35. The system of any one of embodiments 31-34, comprising a tube lens in the imaging light path, wherein the relative position of one or more lenses or lens groups within the tube lens is adjustable.

[0277] 36. The system of any one of embodiments 31-35, comprising the test target, wherein the test target comprises a dark material and an array of light features disposed on the dark material.

[0278] 37. The system of embodiment 36, wherein the bright features are polygonal.

[0279] 38. The system of embodiment 37, wherein the bright features are tilted such that edges of the bright features are oriented at an angle relative to a pixel array of the optical imaging system that captures an image.

[0280] 39. A non-transitory computer-readable medium comprising instructions stored thereon, which, when executed on a processor, perform the following steps of the method as in any of the preceding embodiments: calculating multiple edge spread functions, constructing multiple point spread function models, and calculating multiple imaging performance values.

[0281] 40. An optical system imaging performance testing system, comprising the computer-readable storage medium of embodiment 39.

[0282] It should be understood that one or more of the processes, sub-processes, and process steps described herein may be performed by hardware, firmware, software, or a combination of two or more of the foregoing on one or more electronic or digitally controlled devices. The software may reside on a suitable electronic processing component or system (e.g., Figure 1 The software memory may include an ordered list of executable instructions for implementing logical functions (i.e., "logic" that may be implemented in digital form (e.g., digital circuits or source code) or analog form (e.g., analog sources such as analog electrical, sound, or video signals)). The instructions may be executed within a processing module that includes, for example, one or more microprocessors, general purpose processors, combinations of processors, digital signal processors (DSPs), field programmable gate arrays (FPGAs), or application specific integrated circuits (ASICs). In addition, the schematics depict a logical partitioning of functions with physical (hardware and / or software) implementations that are not limited by the physical layout of the architecture or functionality. The examples of the systems described herein may be implemented in various configurations and operated as hardware / software components in a single hardware / software unit or in separate hardware / software units.

[0283] The executable instructions may be implemented as a computer program product having instructions stored therein, which, when executed by a processing module (e.g., Figure 1 When executed by the controller 108 or 1600 in 1 or 16, the electronic system is directed to execute the instruction. The computer program product can be selectively embodied in any non-transient computer-readable storage medium for use by or in combination with an instruction execution system, device or equipment, such as a system based on an electronic computer, a system containing a processor, or other systems that can selectively obtain instructions from and execute instructions from an instruction execution system, device or equipment. In the context of the present disclosure, a computer-readable storage medium is any non-transient device that can store a program used by or in combination with an instruction execution system, device or equipment. A non-transient computer-readable storage medium can be, for example, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, device or device. A non-exhaustive list of more specific examples of non-transitory computer-readable media includes: an electrical connection having one or more wires (electronic); a portable computer floppy disk (magnetic); a random access memory (electronic); a read-only memory (electronic); an erasable programmable read-only memory, such as flash memory (electronic); optical disk storage, such as CD-ROM, CD-R, CD-RW (optical); and digital versatile disk storage, i.e., DVD (optical). Note that the non-transitory computer-readable storage medium may even be paper or other suitable medium on which the program is printed, as the program may be captured electronically, such as by optical scanning of the paper or other medium, and then compiled, interpreted, or otherwise processed in a suitable manner if necessary, and then stored in computer memory or machine storage.

[0284] It should also be understood that the term "in signal communication" as used herein refers to two or more systems, devices, components, modules or submodules being able to communicate with each other through signals propagating on a certain type of signal path. The signal can be a communication, power, data or energy signal that can transmit information, power or energy from a first system, device, component, module or submodule along a signal path between a first and a second system, device, component, module or submodule to a second system, device, component, module or submodule. The signal path can include physical, electrical, magnetic, electromagnetic, electrochemical, optical, wired or wireless connections. The signal path can also include additional systems, devices, components, modules or submodules between the first and second systems, devices, components, modules or submodules.

[0285] More generally, terms such as "communication" and "in communication" (e.g., a first component "communicates" or is "in communication" with a second component) are used herein to indicate a structural, functional, mechanical, electrical, signal, optical, magnetic, electromagnetic, ionic, or fluid relationship between two or more components or elements. Thus, the fact that one component is referred to as being in communication with a second component is not intended to exclude the possibility that additional components may be present between and / or operably associated or engaged with the first and second components.

[0286] It should be understood that various aspects or details of the present invention may be changed without departing from the scope of the present invention. Furthermore, the foregoing description is for the purpose of illustration only and not for the purpose of limitation - the present invention is defined by the claims.< / ee> < / ee> < / ee> < / ee> < / ee>

Claims

1. A method for testing the imaging performance of an optical system, the method comprising: positioning a test target on an object plane of the optical system; operating the optical system to illuminate the test target and generate an image beam; operating a focusing stage of the optical system to acquire a plurality of images of the test target corresponding to a plurality of defocus values ​​from the image beam by one or more imaging devices; calculating from each image a plurality of edge spread functions at a plurality of locations within the test object; constructing corresponding multiple point spread function models by independently fitting each of the edge spread functions to a basis set of Gaussian error functions; calculating a plurality of imaging performance values ​​corresponding to the plurality of locations based on the plurality of point spread function models, wherein the imaging performance values ​​are based on an indicator selected from the group consisting of: bounding rectangle energy; bounding circle energy; and Strehl ratio; generating one or more graphs of imaging performance based on the combination of the imaging performance values; and comparing two or more of the figures to provide a measure of the relative alignment of the focal plane of each imaging device or channel with respect to the object plane, The plurality of positions comprises a plurality of focus positions z along an optical axis passing through the test object, and operating the optical system comprises acquiring a plurality of images of the test object at different focus positions z. 2 . The method of claim 1 , wherein the plurality of positions further comprises a plurality of field coordinates (x, y) in the object plane within the test target.

3. The method of claim 1 , wherein the one or more graphs correspond to different imaging channels, and the different imaging channels correspond to different imaging devices of the optical system operated to acquire images, or different wavelengths of the acquired images, or both different imaging devices and different colors.

4. The method of claim 3, comprising: comparing two or more of the figures to provide a measure of the relative alignment of each imaging device or channel with respect to each other; As well as both of the above.

5. The method of claim 1, comprising: After generating the one or more maps, the position of one or more optical components of the optical system is adjusted or the one or more optical components are replaced based on information provided by the one or more maps.

6. The method of claim 5, wherein the one or more optical components are selected from the group consisting of: one or more imaging devices; an objective lens of the optical system; one or more tube lenses of the optical system; one or more reflective mirrors or dichroic mirrors; and a combination of two or more of the foregoing.

7. The method of claim 5, wherein the one or more graphs are one or more initial graphs, and the method further comprises: After the one or more optical components are adjusted or replaced, a new image of the test target is acquired, a plurality of new imaging performance values ​​are calculated, and one or more new graphs of imaging performance are generated.

8. The method of claim 7, comprising: The one or more new maps are compared to the one or more initial maps to determine positional adjustments to be made to the one or more optical components to optimize imaging performance.

9. The method of claim 5, comprising at least one of the following: (a) wherein the adjusting or replacing finds a pair of optimal conjugate image planes in the optical system; (b) wherein the adjusting or replacing improves a property selected from the group consisting of: focus matching of the imaging devices; tilting of the imaging devices; flattening of field curvature; reduction of astigmatism; reduction of wavelength-dependent focus shift; and a combination of two or more of the foregoing.

10. The method of claim 1, comprising: One or more global scores for imaging performance are calculated based on the combination of the imaging performance values.

11. The method of claim 10, comprising at least one of the following: (a) modifying the one or more global scores to penalize or reward heterogeneity of the imaging performance values ​​over a range of field coordinates (x, y) in the object plane within the test target, or heterogeneity of the imaging performance values ​​over a range of focus positions z in the object plane, or both; (b) modifying the one or more global scores to penalize or reward similarity of different imaging channels as a function of field coordinates (x, y) in the object plane within the test target or focal position z of the object plane, or both, wherein the different imaging channels correspond to different imaging devices of the optical system operated to acquire images, or different wavelengths of acquired images, or both different imaging devices and different colors.

12. The method of claim 1, comprising: After calculating the imaging performance value, the position of one or more optical components of the optical system is adjusted based on information provided by the imaging performance value.

13. The method of claim 12, wherein the imaging performance value is an initial imaging performance value, and the method further comprises: After adjusting the one or more optical components, a new image of the test target is acquired and a plurality of new imaging performance values ​​are calculated.

14. The method of claim 13, comprising: The new imaging performance value is compared to the initial imaging performance value to determine positional adjustments to be made to the one or more optical components to optimize imaging performance.

15. The method of claim 1, comprising at least one of the following: (a) wherein locating the test target comprises: aligning the target relative to a fiducial shared with one or more optical components of the optical system; (b) wherein operating the optical system comprises: utilizing an objective lens in the image beam, and the method further comprises: adjusting a position of the objective lens along an axis of the image beam to acquire a plurality of images of the test target at different focus positions z; (c) wherein operating the optical system comprises: utilizing an objective lens in the image beam, and the method further comprises: adjusting a position of the objective lens along an axis of the image beam to acquire a plurality of images of the test target at different focal positions z, wherein the objective lens has a configuration selected from the group consisting of: the objective lens is configured for infinite conjugate microscopy; and the objective lens is configured for finite conjugate microscopy; (d) wherein operating the optical system comprises: operating two or more imaging devices to acquire respective images of the test target; (e) wherein operating the optical system comprises: operating two or more imaging devices to acquire corresponding images of the test target, wherein the two or more imaging devices acquire corresponding images at two or more different wavelengths; (f) wherein operating the optical system comprises: operating two or more imaging devices to acquire corresponding images of the test target, wherein the two or more imaging devices acquire corresponding images at two or more different wavelengths, and the method further comprises: separating an image beam propagated from the test target into two or more image beam portions, and transmitting the two or more image beam portions to the two or more imaging devices, respectively; (g) wherein operating the optical system comprises: operating a filter assembly to filter the image beam at a selected wavelength; (h) wherein operating the optical system comprises: utilizing a tube lens in the image beam, and the method further comprises: adjusting the relative position of one or more lenses or lens groups within the tube lens to obtain a plurality of images of the test object at different positions of the tube lens; (i) wherein the test target comprises: a dark material and an array of bright features disposed on the dark material; (j) wherein the test target comprises: a dark material and an array of bright features disposed on the dark material, and wherein the bright features are polygonal; (k) wherein the test target comprises: a dark material and an array of light features disposed on the dark material, wherein the light features are polygonal and the light features are tilted such that edges of the light features are oriented at an angle relative to a pixel array of an acquired image of the optical imaging system.

16. An optical imaging performance testing system, comprising: a target holder configured to hold a test target; a light source configured to illuminate the test target; an imaging device configured to acquire an image of the test target; an objective lens located in an imaging light path between the test target and the imaging device, wherein a position of at least one of the objective lens or the target holder is adjustable along the imaging light path; and a controller, comprising an electronic processor and a memory, and configured to control the steps of the method of claim 1: calculating the plurality of edge spread functions at a plurality of locations within the test target, constructing the plurality of point spread function models by independently fitting each of the edge spread functions to a basis set of Gaussian error functions, calculating the plurality of imaging performance values, generating one or more graphs of imaging performance based on a combination of the imaging performance values, and comparing two or more of the graphs to provide a measure of the relative alignment of the focal plane of each imaging device or channel with respect to the object plane, Wherein the plurality of positions comprises a plurality of focal positions along an optical axis passing through the test object.

17. The system of claim 16, comprising at least one of the following: (a) wherein the objective has a configuration selected from the group consisting of: the objective is configured for infinite conjugate microscopy; and the objective is configured for finite conjugate microscopy; (b) wherein the imaging device comprises a plurality of imaging devices, and the system further comprises an image separation mirror configured to separate the imaging light path into a plurality of imaging light paths, wherein the plurality of imaging light paths are directed to corresponding imaging devices respectively; (c) a filter assembly configured to select a wavelength of an image beam in the imaging light path for propagation to the imaging device; (d) a tube lens located in the imaging light path, wherein the relative position of one or more lenses or lens groups within the tube lens is adjustable; (e) the test target, wherein the test target comprises a dark material and an array of light features disposed on the dark material; (f) the test target, wherein the test target comprises a dark material and an array of light features disposed on the dark material, and wherein the light features are polygonal; (g) The test target, wherein the test target comprises a dark material and an array of light features disposed on the dark material, the light features being polygonal, and wherein the light features are tilted such that edges of the light features are oriented at an angle relative to a pixel array of an acquired image of the optical imaging system.

18. A non-transitory computer readable medium comprising instructions stored thereon, which, when executed on a processor, perform the steps of the method of claim 1: calculating the plurality of edge spread functions at a plurality of locations within the test target, constructing the plurality of point spread function models by independently fitting each of the edge spread functions to a basis set of Gaussian error functions, calculating the plurality of imaging performance values, generating one or more graphs of imaging performance based on combinations of the imaging performance values, and comparing two or more of the graphs to provide a measure of the relative alignment of the focal plane of each imaging device or channel with respect to the object plane, Wherein the plurality of positions comprises a plurality of focal positions along an optical axis passing through the test object.

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