Optical imaging performance test system and method

Abstract

An improved approach for imaging performance testing of optical imaging systems is provided. In one embodiment, a test target is placed in an object plane of an optical system and illuminated to generate an image beam. One or more images of the test target are acquired from the image beam. Edge spread functions are calculated from the acquired image data at multiple locations within the test target, and a model of the point spread function is constructed from the edge spread functions. Multiple imaging performance values ​​corresponding to the multiple locations are calculated based on the point spread functions. The imaging performance values ​​are based on ensquared energy, encircled energy, or Strehl ratio.

Description

[Technical Field]

[0001] Overall, this invention relates to testing the imaging performance of an optical imaging system that can be used in microscopy to obtain imaging data from a sample under analysis. [Background technology]

[0002] In optical imaging systems, including automated high-throughput optical imaging systems used with microscopes, a key metric for the quality of the resulting imaging data is the clarity of the images.

[0003] Many applications of microscopy imaging (e.g., in DNA sequencing) rely on the ability to produce uniformly sharp images across one or more channels or the entire field of view of a camera, regardless of imperfections in the focus and tilt of the specimen.

[0004] The optical alignment of the camera and other optical components of an optical imaging system, as well as with respect to the specimen being imaged, significantly contributes to image clarity and, consequently, the quality of the resulting imaging data.

[0005] Therefore, it is desirable to be able to test and evaluate the imaging performance (i.e., quality) of an optical imaging system. For example, it is useful to determine whether an optical imaging system meets or exceeds a predetermined minimum level of quality considered acceptable for a given application of the optical imaging system. In calculating appropriate manufacturing and manufacturing performance metrics, it is even more useful to describe the overall imaging performance using a single, exact numerical value.

[0006] Images acquired by an optical imaging system can be characterized as a mixture of the actual specimen (or object) [S(x,y)] and an instrument response function called the Point Spread Function [PSF(x,y)] in a microscope. Mathematically, the image I(x,y) is the sum of the convolution of the specimen and the PSF, and the noise N(x,y) of each pixel, as follows:

[0007]

number

[0008] The evaluation of imaging performance includes acquiring images of the specimen (which may be a test target, i.e., the object provided for the test purpose), measuring and quantifying the PSF using the imaging data, and comparing the results against a predetermined minimum acceptance criterion.

[0009] It is desirable to describe the quality of the PSF using a single scalar number. Several metrics are commonly used, including the full width at half maximum (FWHM) or Strehl ratio of the real-space PSF, wavefront error or modulation transfer function (MTF) contrast at a single frequency, and the real-space quantity encircled energy or ensquared energy. The accuracy and precision of the estimation of each measurement is strongly related to the experimental methods used to compute them. The advantages and disadvantages of each relate to the accuracy of the estimation, the ease of interpretation, and their ability to capture both insufficient image resolution to noise ratio (SNR) and insufficient image signal to noise ratio (SNR).

[0010] The classic equation (1) above does not reflect the fact that the PSF varies across the entire field of the image, which is equivalent to saying that an image can be sharp (high quality) in the center and blurry (low quality) at the edges. Optical systems that are close to the diffraction limit and optimized for detecting weak light sources have a compact PSF with density contained in an area of ​​about 5x5 pixels, in contrast to images that are typically much wider, such as 4096x3000 pixels.

[0011] Because a small PSF can vary significantly across the entire image, multiple independent measurements of the PSF across the entire field of view are necessary. In addition to switching field coordinates (x,y), the PSF is strongly affected by the image focus in the z direction, and with high-performance, high numerical aperture (NA) objective lenses, PSF performance degrades rapidly when the focus is blurred, which means the objective lens has a shallow depth of field. Diffraction effects set an upper limit on the depth of field, but in systems with insufficient correction or alignment, the depth of field becomes even shallower. This practical limitation is significant because systems with low optical quality may need to focus several times to obtain a sharp image at all points in the field, reducing system throughput and potentially causing photodegradation of the sample.

[0012] Image downstream processing by image analysis software can produce results that are strongly influenced by image sharpness. Blurred images of high-contrast point objects fundamentally reduce the signal-to-noise ratio and decrease resolution. For example, blurred images of DNA clusters reduce the ability to perform accurate photometry of nucleotide quantities, decrease the number of small, distinct clusters that can be observed, and introduce crosstalk between adjacent clusters.

[0013] Image processing algorithms can partially reduce blurry images by performing either several explicit steps using well-known deconvolution techniques or implicit steps such as selective filtering of data. In such an approach, computational costs occur when it is necessary to estimate the degree of PSF degradation for each image or batch of images, and the estimation of PSF degradation itself can add noise.

[0014] Faster approaches assume that the sharpness of the PSF is constant, but due to the difference between the actual PSF and the assumed PSF, the quality of the image processing results may deteriorate. Therefore, the uniformity of the PSF across the input image affects the effectiveness of the image processing algorithm.

Summary of the Invention

Problems to be Solved by the Invention

[0015] Existing optical alignment methods are labor-intensive and struggle to obtain quick feedback that describes the overall performance of the optical system. For example, in the case of wavefront measurement, it may neglect to accurately and quickly describe a single point within the field of view and inform the operator how the effects of those measurements impact distant locations within the field.

[0016] By using fast predictive feedback directly to the operator, the manufacturing labor costs associated with optical alignment can be reduced. Therefore, a fast global analysis of system performance will serve to provide feedback with a delay of about 0 to 5 seconds.

[0017] Furthermore, in the analysis of manufacturing yield (e.g., using CpK process capability statistics), it is necessary to estimate the quality of each unit as a continuous variable, and it is ideal to describe the build of the entire unit by looking at such a single variable.

[0018] Therefore, there is a continuing need for improved approaches to testing the imaging performance of optical imaging systems.

[0019] To address all or part of the aforementioned problems and / or other problems that a person skilled in the art may observe, the present invention provides methods, processes, systems, apparatus, equipment, and / or devices, as described below as examples of embodiments. [Means for solving the problem]

[0020] According to one embodiment of the present invention, a method for testing the imaging performance of an optical system includes: positioning a test target on the object plane of the optical system; operating the optical system to illuminate the test target and generate an image beam; operating the focusing stage of the optical system to acquire multiple images of the test target corresponding to multiple defocus values; calculating multiple edge spread functions at multiple locations within the test target from each image; constructing multiple point spread function models from each edge spread function; and calculating multiple imaging performance values ​​corresponding to multiple locations in an imaging volume defined by the image plane extruded through a range of defocus locations, based on the point spread functions, wherein the imaging performance values ​​are based on at least one metric of ensquared energy, encircled energy, or Strehr ratio.

[0021] In one embodiment, illuminating the test target involves back-illuminating the test target with a numerical aperture (NA) greater than or equal to that of the optical system under test.

[0022] In one embodiment, the method includes evaluating and describing the distribution and mapping of imaging performance values ​​across the entire imaging volume, and further including calculating a summary score based on such distribution and mapping.

[0023] According to another embodiment, the optical imaging performance test system comprises 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 positioned in the imaging optical path between the test target and the imaging device, wherein the position of the objective lens is adjustable along the imaging optical path; and a controller having an electronic processor and memory, configured to control or perform at least the steps or functions of a method for testing imaging performance: calculating multiple edge spread functions; constructing a point spread function model; and calculating multiple imaging performance values.

[0024] In one embodiment, the optical imaging performance test system includes a tube lens positioned in the imaging optical path between the test target and the imaging device.

[0025] In one embodiment, the optical imaging performance test system includes other or additional mechanical devices or components configured to allow the angular direction and position of the imaging devices to be changed relative to each other and to the objective lens.

[0026] In one embodiment, the controller is configured to control or perform the steps or functions of a method for testing imaging performance, including evaluating and describing the distribution and mapping of imaging performance values ​​across an imaging volume, and calculating a summary score for such distribution and mapping.

[0027] According to another embodiment, a non-temporary computer-readable medium, when executed on a processor, includes stored instructions that control or execute at least the steps or functions of a method for testing imaging performance, including calculating multiple edge spread functions, constructing a model of a point spread function, and calculating multiple imaging performance values.

[0028] According to another embodiment, a system for testing the imaging performance of an optical system includes a computer-readable storage medium.

[0029] Other devices, apparatus, systems, methods, features, and advantages of the present invention will be apparent to those skilled in the art by examination of the following drawings and detailed description. All such additional systems, methods, features, and advantages are included in this description, are within the scope of the embodiments, and are intended to be protected by the appended claims.

[0030] The present invention can be better understood by referring to the following figures. The components in the drawings are not necessarily to scale, but rather are emphasized to illustrate the principles of the present invention. In the drawings, the same reference numerals indicate corresponding parts in different drawings. [Brief explanation of the drawing]

[0031] [Figure 1] This is a schematic diagram of an example of an optical imaging performance test system according to one embodiment of the present disclosure. [Figure 2] This is a schematic perspective cross-sectional view of an example of a target fixing device for an optical imaging performance testing system according to one embodiment. [Figure 3A] This is a schematic plan view of an example of a test target for an optical imaging performance test system according to one embodiment. [Figure 3B] This is a detailed view of the section containing the test target shown in Figure 3A. [Figure 4A]An example of a three-dimensional (3D) model of a true moderately sharp point spread function or PSF (PSF with a true ensquared energy value of 0.594) generated by implementing the systems and methods disclosed herein, where the intensity levels are color-coded, and different colors represent different levels of shading. [Figure 4B] This is an example of a 3D model of the same PSF related to Figure 4A, derived from the line spread function using the line spread function (LSF × LSF) approximation. [Figure 4C] This is an example of a 3D model of the same PSF related to Figure 4A, where the PSF model is fitted to edge spread function (ESF) data. [Figure 4D] This is a 3D model showing the difference between the PSF model in Figure 4A and the PSF model in Figure 4B. [Figure 4E] This is a 3D model showing the difference between the PSF model in Figure 4A and the PSF model in Figure 4C. [Figure 4F] This is a 3D model showing the difference between the PSF model in Figure 4B and the PSF model in Figure 4C. [Figure 5A] Here is another example of a 3D model similar to Figure 4A, with a truly relatively sharp PSF (true EE value of 0.693). [Figure 5B] This is an example of a 3D model of the same PSF related to Figure 5A, derived from the LSF×LSF approximation. [Figure 5C] This is an example of a 3D model of the same PSF related to Figure 5A, where the PSF model is fitted to the edge spread function (ESF) data. [Figure 5D] This is a 3D model showing the difference between the PSF model in Figure 5A and the PSF model in Figure 5B. [Figure 5E] This is a 3D model showing the difference between the PSF model in Figure 5A and the PSF model in Figure 5C. [Figure 5F] This is a 3D model showing the difference between the PSF model in Figure 5B and the PSF model in Figure 5C. [Figure 6A] This is an example of a 3D model similar to Figure 4A, but with a true, relatively blurry PSF (true EE value of 0.349). [Figure 6B] This is an example of a 3D model of the same PSF related to Figure 6A, derived from the LSF×LSF approximation. [Figure 6C] This is an example of a 3D model of the same PSF related to Figure 6A, where the PSF model is fitted to the edge spread function (ESF) data. [Figure 6D] This is a 3D model showing the difference between the PSF model in Figure 6A and the PSF model in Figure 6B. [Figure 6E] This is a 3D model showing the difference between the PSF model in Figure 6A and the PSF model in Figure 6C. [Figure 6F] This is a 3D model showing the difference between the PSF model in Figure 6B and the PSF model in Figure 6C. [Figure 7A] This is a plot of the accuracy of LSF × LSF approximation for various PSFs based on focusing. [Figure 7B] This plots the accuracy of EE fitting of various PSFs to ESF data by focusing. [Figure 7C] This is a plot of the accuracy of EE estimation for a given PSF by focusing. [Figure 8A] This is an example of an image obtained from one of the target characteristics of the test target shown in Figure 3A. [Figure 8B] This is an example of an ESF calculated from the image in Figure 8A. The ESF has been refined using outlier removal. [Figure 9] Figure 8B shows an example of fitting the ESF to a model consisting of a linear combination of error functions, and then calculating the EE from the coefficients of the resulting model. [Figure 10] This example calculates the ESF for four edges of target characteristic 313, fits the ESF to a linear combination of error functions, and calculates four independent EE values ​​from the corresponding fits. [Figure 11] This flowchart illustrates an example of a method for testing the imaging performance of an optical system according to one embodiment of the present disclosure. [Figure 12] This is a flowchart illustrating the acquisition of an image Z-stack according to one embodiment of the present disclosure. [Figure 13] This is a flowchart illustrating the processing of individual images according to one embodiment of the present disclosure. [Figure 14] An example of a set of EE performance maps that can be generated according to one embodiment of the present disclosure is shown. [Figure 15] An example of a combined EE score and performance map that can be generated according to one embodiment of the present disclosure is shown. [Figure 16] Figure 1 is a schematic diagram of a non-limiting example of a system controller (or controller) that is part of or capable of communicating with an optical imaging performance test system according to embodiments of the present disclosure, such as the system shown here. [Modes for carrying out the invention]

[0032] This disclosure provides a system and method for testing the imaging performance of an optical imaging system.

[0033] Figure 1 is a schematic diagram showing an example of an optical imaging performance test system 100 according to one embodiment of the present disclosure. The structure and operation of the various components of the optical imaging performance test system 100 as a whole are known to those skilled in the art and are therefore described herein only briefly where necessary to understand the subject matter disclosed. For illustrative purposes, Figure 1 includes a Cartesian (XYZ) coordinate system, the origin of which is arbitrarily located relative to the figure in which it is depicted.

[0034] Overall, the test system 100 includes the optical system 104 under test, the test target stage assembly 106, and a system controller 108 for electrical communication (for transmitting, if necessary, power, data, measurement signals, control signals, etc.) with appropriate components of the optical system 104 and the test target stage assembly 106.

[0035] The optical system 104 may be an actual optical system being tested, such as a product intended for commercial availability. Alternatively, the optical system 104 may be a collection of components arranged to emulate an actual optical system, supported by or mounted on the test system 100 (e.g., on the optical bench of the test system 100) (for example, with the components positioned and determined relative to each other in terms of distance and orientation and aligned with each other according to the configuration of the desired operation).

[0036] Overall, the optical system 104 may be any system (or instrument, device, etc.) configured to acquire an optical image of the object or sample to be analyzed. Such a sample may be biological (e.g., spores, fungi, molds, bacteria, viruses, biological cells or intracellular components such as nucleic acids (DNA, RNA, etc.), skin cells, organic deposits, etc.) or non-biological.

[0037] Typically, the optical system 104 is configured to acquire optical images at a range of magnifications and may therefore be a type of microscope (or part thereof).

[0038] The optical system 104 may or may not include a target holder 112 (e.g., a sample stage, or an emulation of a sample stage) configured to hold a test target 116 (instead of the sample actually being imaged). If the target holder 112 is separate, a mechanical datum 160 may be provided to allow the mounting of the target holder 112 in a manner that is precisely positioned overall.

[0039] 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, such as cameras (for example, a first imaging device 124 and a second imaging device 128 in the illustrated embodiment), are configured to image the test target 116. The optical system 104 further includes other intermediate optical systems (optical components) where necessary to define the illumination optical path from the light source 120 to the target holder 112 and the test target 116 (and through the test target 116 in the illustrated embodiment) and the imaging optical path from the target holder 112 and the test target 116 to the imaging devices 124 and 128, as will be understood by those skilled in the art.

[0040] In particular, the optical system 104 typically includes an objective lens 132 positioned 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 images captured by the imaging devices 124 and 128. In the optional case of an infinity-corrected microscope optical system, the objective lens 132 performs this task in combination with tube lenses 152 and 156.

[0041] In the illustrated embodiment, the objective lens 132 schematically represents an objective lens assembly including an objective lens system mounted on an objective lens stage (or positioning device). The objective lens stage is configured to move (translate) the objective lens system along the Z-axis. Thus, the objective lens 132 can be adjusted to focus on or defocus at any desired depth (height) of the thickness of the test target 116, as required for the test purpose. Since the objective lens 132 can focus at different heights, the adjustability allows the optical system 104 to generate a "z-stack" of images, as will be understood by those skilled in the art. Alternatively, to change the focus, the objective lens 132 can be fixed in space and the specimen can be moved along the z-direction. Alternatively, the objective lens 132 and the specimen can be held in place and the tube lenses 152 and 156 can be moved relative to the imaging devices 124 and 128.

[0042] In this embodiment, the Z-axis is defined as the optical axis passing through the test target 116 (perpendicular to the viewpoint in Figure 1), along which the illumination and imaging optical paths are located, and along which the objective lens 132 can adjust focus and defocus. Therefore, the X and Y axes are in a cross-section perpendicular to the Z-axis. Thus, the location on the object plane where the test target 116 is fixed by the target holder 112 can be mapped using (X,Y) coordinates, and the depth (focal position) can be mapped to the Z coordinate.

[0043] In the embodiment shown in Figure 1, the optical system 104 has a top-lead configuration in which the objective lens 132 is positioned above the target holder 112 and the test target 116. In an alternative embodiment, the optical system 104 may have an inverted or bottom-lead configuration in which the objective lens 132 is positioned below the target holder 112 and the test target 116.

[0044] The optical system 104 can be configured to capture transmitted or reflected light from the test target 116. In the illustrated embodiment, the optical system 104 is configured for transmitted illumination such that the illumination path and imaging path are on opposite sides of the target holder 112 and the test target 116. In an alternative embodiment, the optical system 104 can be configured such that the illumination path and imaging path are on the same side of the target holder 112 and the test target 116. For example, the objective lens 132 can be positioned in both the illumination path and the imaging path, such as in an epi-illumination or epi-fluorescence configuration, as will be understood by those skilled in the art.

[0045] The optical system 104 may include a target fixture or assembly 136, of which the target holder 112 is part. The target holder 112 is mounted, attached to, or otherwise mechanically referenced by a target stage (or positioning device) 140. The optical system 104 may be mated to the test target stage assembly 106 using a precise datum 160. The target stage 140 can be configured to move precisely in known ways via motorized or non-motorized (and automatic and / or manual) actuation. The target stage 140 can be configured to translate along one or more axes and / or rotate about those axes. For example, the target stage 140 may be configured for movement with 5 degrees of freedom (DOF), in which case the target stage 140 lacks the freedom to move along the Z axis (lacks focusing movement).

[0046] In embodiments in which illumination light is directed from below the target holder 112 (as shown in the figure), the target holder 112 may include a window or opening 188 to allow the illumination light to pass through, and the test target 116 can be mounted in such a window or opening.

[0047] The light source 120 may be any incoherent light source suitable for optical microscopy. In contrast, coherent light sources 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 phosphorus-type materials optically excited by LEDs, lasers, incandescent lamps, etc. In some embodiments, the light source 120 may include a plurality of light-emitting units (e.g., LEDs) configured to emit light at different wavelengths, and a mechanism configured to enable the selection of light-emitting units to be activated at a given time.

[0048] In the illustrated embodiment, the light source 120 is integrated with the target fixture 136 (and thus movable with it) and 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 may be separate from the target fixture 136.

[0049] In some embodiments, as will be understood by those skilled in the art, a capacitor (not shown) can be placed between the light source 120 and the target holder 112 and the test target 116 to concentrate the illumination light from the light source 120 and enhance the illumination of the test target 116. The angle of the illumination ray, in particular the numerical aperture (NA) of the illumination ray and the uniformity of the radiant intensity, affect the accuracy of the measured Point Spread Function (PSF). The NA of an ideal light source is slightly higher than the objective NA of the optical system and exhibits uniform radiant intensity across the aforementioned angles.

[0050] The imaging devices 124 and 128 are multi-pixel (or pixelated) imaging devices in which the sensor is a pixel array (for example, 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.

[0051] In the illustrated embodiments, imaging devices 124 and 128 respectively schematically represent imaging device assemblies including imaging device units (sensors, i.e., pixel arrays) mounted on an imaging device stage (or positioning device). Each imaging device stage can be configured for six DOF movements, i.e., both translation along all three (X, Y, and Z) axes and rotation around those axes.

[0052] The imaging devices 124 and 128 can be positioned at any nominal angle and distance from each other. In the illustrated embodiment, the first imaging device 124 is positioned on the axis (along the Z-axis) together with the objective lens 132, and the second imaging device 128 is positioned on an axis angled with respect to the Z-axis (on the X-axis in the illustrated embodiment) (90 degrees in the illustrated embodiment).

[0053] The optical system 104 includes an image separation mirror 144 (e.g., a beam splitter or dichroic mirror) positioned on the axis between the objective lens 132 and the first imaging device 124. The image separation mirror 144 is configured to split the image beam propagating from the test target 116 into a first image beam portion that propagates along a first splitting 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 that propagates along a second splitting imaging optical path from the image separation mirror 144 (via reflection from the image separation mirror 144) to the second imaging device 128. The separation or splitting of the image beam into the first and second image beam portions may be based on wavelengths (i.e., colors) within the electromagnetic spectral range of operation. For example, the first image beam portion may be the blue portion of the image beam, and the second image beam portion may be the red portion.

[0054] The optical system 104 may include a filter assembly 148 (i.e., a wavelength selector) positioned in the imaging optical path between the objective lens 132 and the imaging devices 124 and 128, or specifically, in the illustrated embodiment, between the objective lens 132 and the image separation mirror 144.

[0055] In the illustrated embodiment, the filter assembly 148 may represent a plurality of filters mounted on a filter stage configured to selectively switch different filters into and out of the imaging optical path by linear translation (e.g., filter slide) or rotation (e.g., filter wheel), as indicated by the bidirectional horizontal arrows in Figure 1, thereby enabling selection of wavelengths passed through the image beam portions received by imaging devices 124 and 128, respectively.

[0056] The filter assembly 148 can be used to perform multi-channel imaging, where 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.

[0057] 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), with the first imaging device 124 designated for two channels (e.g., blue and green) and the second imaging device 128 designated for the other two channels (e.g., red and amber). Switching (positioning) of the filter assembly 148 can be coordinated with the operation of imaging devices 124 and 128 and other components of the optical system 104 as needed to sequentially acquire images in four channels. In a particular example, the four-channel configuration is useful in DNA sequencing where different fluorescent labels are associated with different nucleic acid bases (adenine, cytosine, guanine, thymine) in the sample being analyzed.

[0058] Depending on the embodiment under test, the optical system 104 may further include one or more other types of optical components in the illumination and / or imaging optical paths. Examples include, but are not limited to, field lenses, relay lenses, beam expanders, beam collimators, apertures, slits, pinholes, confocal disks, and the like.

[0059] In the illustrated embodiment, the optical system 104 includes a first tube lens assembly 152 positioned in a first split imaging optical path between an image separation mirror 144 and a first imaging device 124, and a second tube lens assembly 156 positioned in a second split imaging optical path between an image separation mirror 144 and a second imaging device 128.

[0060] In the illustrated embodiment, the first tube lens assembly 152 may represent a first tube lens mounted on a tube lens stage configured to adjust (translate) the relative positions of elements within the first tube lens along the axis of the first segmented imaging optical path, as indicated by the bidirectional vertical arrows in Figure 1. The second tube lens assembly 156 may represent a second tube lens mounted on a tube lens stage configured to adjust (translate) the relative positions of elements within the second tube lens along the axis of the second segmented imaging optical path, as indicated by the bidirectional horizontal arrows in Figure 1. As will be understood by those skilled in the art, an infinity-corrected optical system can be realized using adjustable tube lenses.

[0061] The optical system 104 may include appropriate reference datums configured to function as reference datums for defining the positions of various adjustable components of the optical system 104, such as the target holder 112 / test target 116, the objective lens 132, etc.

[0062] In the illustrated embodiment, a fixed position structure of the optical system 104, such as the surface of the enclosure 160 in which various components of the optical system 104 are housed, may function as a reference datum.

[0063] Overall, the system controller (or controller) 108 is configured to control the operation of 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 imaging devices 124 and 128.

[0064] The system controller 108 is further configured to process the imaging data output by the imaging devices 124 and 128 (including data acquisition and signal analysis, such as digitizing and recording / storing images, and formatting images for display on a display device such as a computer screen).

[0065] The system controller 108 is further configured to perform a performance test of the optical system 104 in accordance with a method disclosed herein, which includes executing any algorithm associated with that method.

[0066] The system controller 108 is further configured to provide a user interface as needed to facilitate embodiments of operational and performance testing of the optical system 104. For all such functions, the system controller 108 is schematically shown in Figure 1 as including an electronic module 164 that communicates with the optical system 104 and a computing device 168 that communicates with the electronic module 164. The electronic module 164 and the computing device 168 are understood to represent all the hardware (microprocessor, memory, electrical circuits, peripherals, etc.), firmware, and software components appropriate for performing all such functions, as will be understood by those skilled in the art.

[0067] In one embodiment, the electronics module 164 may represent components dedicated to controlling the optical system 104 and performing performance tests, while the computing device 168 may mean a more general-purpose computing device that can be configured or programmed to interface with the electronics module 164 to facilitate controlling the optical system 104 and performing performance tests.

[0068] The system controller 108 (electronic module 164 and / or computing device 168) may include a non-temporary computer-readable medium containing non-temporary 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.

[0069] The system controller 108 (typically at least computing device 168) may include one or more user input devices (e.g., keypad, touchscreen, mouse, etc.), user output devices (e.g., display screen, printer, visible indicator or alert, audible indicator or alert, etc.), a software-controlled graphical user interface (GUI), and a device for loading media readable by an electronic processor (e.g., non-temporary logical instructions embodied in software, data, etc.). The system controller 108 (typically at least computing device 168) may also include an operating system (e.g., Microsoft Windows® software) for controlling and managing various functions of the system controller 108.

[0070] It will be understood that Figure 1 is a high-level schematic diagram of the optical imaging performance test system 100 disclosed herein. As will be understood by those skilled in the art, other components such as additional structures, devices, and electronics may be included as necessary in the actual embodiment, depending on how the test system 100 is configured for a given application.

[0071] Figure 2 is a schematic perspective view showing an example of a target fixture 136 according to one embodiment. The target holder 112 includes a target support block 172, a clamp plate 176, and one or more fasteners 180. In the illustrated embodiment in which a light source 120 (an LED in the illustrated example) is integrated with the target fixture 136, the target fixture 136 may include a mounting member 184 for supporting and enclosing the light source 120 (and optionally functioning as a heat sink for the light source 120). The mounting member 184 may be part of the target support block 172 or attached thereto. The target support block 172 includes an annular shoulder for supporting the test target 116 and any suitable optical system (e.g., a window 188) provided between the test target 116 and the light source 120.

[0072] After the test target 116 is mounted on the target support block 172, a cover glass (or cover slip) 192 may be placed on the test target 116. For example, the cover glass 192 may have a thickness of 500 ± 5 μm. Next, the clamp plate 176 (which can be configured as a spring) is fixed to the target support block 172 using a fastener 180 (which may be a screw that engages with a screw hole in the target support block 172, for example) to fix the test target 116 and the cover glass 192 in place within the target fixture 136.

[0073] Figure 3A is a schematic plan view of a test target 116 according to one embodiment. The test target 116 may be a planar geometric substrate (e.g., plate-shaped) comprising (or including at least an outer surface composed of) a dark (light-absorbing) material 309 and a two-dimensional array (pattern) of bright (light-reflecting) target characteristics 313 arranged on (or integrated with) the dark material 309.

[0074] In this specification, the terms “dark” and “bright” are related to each other. That is, in embodiments using reflected light illumination, a dark material is darker (absorbs more light) than target property 313, and target property 313 is brighter (reflects more light) than dark material 309. In embodiments featuring transmitted light illumination, a dark material absorbs more light, and a bright material transmits light better (transmits more light).

[0075] The target feature 313 is preferably polygonal and therefore provides an edge that shows contrast between the light feature 313 and the surrounding dark material 309. In particular, the target feature 313 may be linear (square or rectangular).

[0076] In one embodiment, as shown in the figure, the target characteristics 313 are tilted (rotated) in the plane of the test target 116 (a plane perpendicular to the optical axis) with respect to the horizontal and vertical directions, particularly with respect to the pixel arrays of the imaging devices 124 and 128. This is further shown in Figure 8A, which is a detail view of a section of the test target 116 realized on either the pixel array of the imaging devices 124 and 128.

[0077] In a non-exclusive example, the dark material 309 is a fused silica substrate (or part thereof), and the target feature 313 is a patterned chromium layer placed on the substrate. The array of target features 313 is a 14 × 10 square array with 42 μm edges. The target features 313 are spaced 92 μm apart (center to center) on the long axis of the image and 94 μm apart on the short axis of the image. The target features 313 are tilted at 100 mRad (approximately 5.73°) with respect to the optical axis. The image is 19% brighter than the darkfield. The chromium is pointed towards the objective lens.

[0078] The test system 100 can be used to perform a method for testing the imaging performance of the optical system 104. According to this method, the test target 116 is positioned in the object plane of the optical system 104 by fixing the test target 116 to the target holder 112. Initial adjustments may be made to the positioning of various adjustable (stepwise) 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.). Furthermore, the wavelengths passed to the imaging devices 124 and 128 can be selected by adjusting the filter assembly 148.

[0079] Next, the light source 120 is activated to generate illumination light to light up the test target 116, resulting in imaging light emitted from the test target 116. The imaging light is focused onto the pixel arrays of the respective imaging devices 124 and 128 as image beam portions (divided by the image separation mirror 144) by the objective lens 132 and the tube lenses 152 and 156, acquiring an image of the test target 116 (within the field of view of the objective lens 132) from each image beam portion. The imaging data is then transferred from the imaging devices 124 and 128 to the system controller 108 for processing.

[0080] This method utilizes imaging data to calculate multiple edge spread functions at multiple locations within the test target 116. Next, a point spread function model is constructed from each of the calculated edge spread functions. Then, multiple (estimated) values ​​of the ensquared energy EE are calculated from these point spread functions. Alternatively, the encircled energy and / or Strehr ratio can be calculated.

[0081] Estimation of sharpness (including ensquared energy) from a sloping edge target. In one embodiment, this method involves fitting an experimental edge spread function (ESF) using a basis set selected so that fit parameters are available to generate a model of the point spread function. From this model, various measures of sharpness can be derived.

[0082] Definition of the Criteria for Measuring Clarity Ensquared energy For a given PSF(x,y) centered at the point (x=0,y=0), the ensquared energy is the ratio of PSFs that fall within a square of area a centered at the same point, as follows:

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[0084] Encircled Energy For a specific PSF(x,y) centered at the point (x=0,y=0), the encircled energy is the proportion of PSFs that fall within a circle of radius r centered at the same point, as follows:

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[0086] Strehr ratio For a given PSF, the Strehr ratio is the ratio of the maximum normalized PSF (assuming it occurs at (x=0;y=0)) to the maximum perfect diffraction-limited PSF. For a round pupil, the diffraction-limited PSF is the Airy disk, and its Strehr ratio is as follows:

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[0089] Line Spread Function (LSF) and Edge Spread Function (ESF) One method for measuring PSF is to image a backlit array of holes as a point source, where the hole diameters are significantly smaller than those of an Airy disk, and the spacing between holes is large enough that the PSFs from adjacent point sources overlap to an imperceptible degree. The problems with such an approach are the accurate fabrication of such small properties against a high OD target background (especially when testing with a high NA microscope) and the fact that the PSF is 1 / r 2 This is accompanied by the fact that it attenuates in two dimensions, and the normal signal quickly becomes noisy.

[0090] Dynamic range exceeds 70 dB and well depth is 10,000 e -1 Even with currently available sensors exceeding [a certain value], it may be impossible to detect PSF density several pixels away from the center of the PSF, and the density in that region can rapidly degrade the system's signal-to-noise ratio (SNR). Alternative experimental protocols involve fluorescence illumination of a thin monodisperse coating of quantum dots or single fluorophores, which can generate a reliable point light source, but still suffer from the same SNR constraints as hole targets.

[0091] A common solution is to use an extended edge target instead of a pinhole, which is part of the so-called slanted edge family of methods commonly used to generate modulation transfer function (MTF) measurements. Such a target leverages line symmetry to examine a one-dimensional projection of a point spread function known as a line spread function (LSF), as follows:

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[0094] Because LSF attenuates at a rate of 1 / r, LSF analysis can better capture the expanded density of PSF several pixels away from the center. Instead of measuring PSF using a pinhole, LSF is measured using an edge, and the resulting experimental profile is known as the edge spread function (ESF). The edge can be aligned with the axis of the imaging system or tilted.

[0095] First, consider an edge aligned with the y-axis and described by the following transfer function E(x,y).

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[0097] This image represents a two-dimensional convolution of PSF and E(x,y), or equivalently, a one-dimensional convolution of LSF(x) and E(x).

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[0102] Conceptually, it is straightforward to find LSF(x) as the derivative of the observed ESF(x) according to the following relationship.

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[0104] However, in practice, numerically differentiating noisy data results in undesirable levels of noise. Instead, according to one aspect of this disclosure, equation 12 is directly fitted as described below.

[0105] Approximation of PSF using separable functions While measuring the LSF rather than the PSF is more stable, the ultimate goal is to know about the PSF. It is formally impossible to reconstruct a complete two-dimensional PSF from two one-dimensional LSFs recorded along orthogonal directions. Instead, according to one aspect of this disclosure, this method (e.g., an algorithm that can be embodied in software performed by a control device) calculates an approximation of the PSF as follows:

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[0108] This assumption is unacceptable in two cases: (1) when a very accurate model of the appropriately corrected PSF is required, for example, to examine the location of nulls in an Airy disk; and (2) when the PSF under evaluation is far from perfect (>1 / 4 wave RMS wavefront error) and contains substantial asymmetry with respect to the y-axis or x-axis, such as coma aberration occurring at the diagonal corners of the field where |y|≒|x| exists.

[0109] The finite accuracy of the LSF×LSF approximation is evaluated in Figure 7A (accuracy of the LSF×LSF approximation for various PSFs due to focusing). For the analysis, several known PSF clusters were projected onto horizontal and vertical LSFs, and the approximate PSF was recalculated from their cross product using Equation 14.

[0110] Ensquared energy (EE4) was calculated for the original and approximated PSFs across a 2x2 pixel region. The original PSF was assumed to be a real-world PSF obtained from experimental wavefront error measurements at 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 entire pupil to obtain the image-space PSF. For sharper PSFs, the LSF×LSF approximation consistently underestimates the EE of the actual PSF by approximately 0:05, but such an underestimation is accurate and sufficient to recover accuracy by performing empirical corrections using polynomial fitting or other interpolation methods. For blurred PSFs, the average correspondence between the LSF×LSF approximation and the true PSF is good, but the accuracy is lower than average for EE<0.1. The relative accuracy of blurred PSFs is lower, but blurred PSFs are thus clearly distinguishable and not confused with sharp PSFs.

[0111] Overall, the approximation is accurate enough to allow for a meaningful comparison between the system under test and the PSF clarity specifications.

[0112] Before going into detail about the ESF fit process, it is helpful to describe the format of the pixelated input data and touch upon practical concerns related to image registration.

[0113] Beveled edge target The images of the slanted edge target are the sole data source for PSF fitting in this method, with the edges arranged in a square array, each with four edges. The use of slanted edge ESF targets is well established. Given an ESF for a single edge, a data region can be extracted that is centered on the edge and extends to any number of pixels on either side of the edge, its length being sufficient to image the entire ESF of a moderately desaturated PSF, and also providing a good estimate of the baseline intensity levels of the completely dark and completely bright parts of the image. With the slanted edge target, multiple ESFs can be combined; for example, if the edge is almost vertical but has a slope of 1 / a radians (where a is approximately a value of 10), a group of adjacent rows of the image can be collected and pooled such that each edge contains a unique copy of the ESF shifted by 1 / a pixels relative to the adjacent row. The resulting ESFs can then be displayed together and shifted by x by (row height) / a to align the midpoint of each ESF transition.

[0114] This dataset samples ESF at intervals of 1 / a pixels, but does not increase resolution in itself, as each observation at a particular pixel is still an average of the entire active area of ​​the pixel. For the specific sensor used, the active area and geometry of pixels other than the pitch may not be known. The active area of ​​a pixel is assumed to be 100% fill factor. Therefore, the observed image around a vertically tilted ESF is modeled as follows:

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[0116] In current practice, 64 rows of images spanning 32 pixels (16 pixels on each side of the edge) of a sloped edge target with a=10 were pooled together for analysis. Outlier removal was performed using l 2The distance was calculated as the norm, and a quarter of the rows were removed based on the distance from the median of all rows.

[0117] Image registration Coarse image registration Edges were initially identified by calculating the single-frequency Fourier transform of the projection of the image's center in both the vertical and horizontal directions. The phase of that Fourier transform was then used to estimate the overall image shift by assuming nominal magnification and using the known dimensions of the target.

[0118] The image was divided into adjacent tiles, and the centroid of each tile was calculated along with the mean value of each tile, along with the x and y coordinates of each tile. The approximate location of each square in the image was sufficiently determined from the coarse shift of the FFT, and then the centroid of each square was calculated from the pre-calculated centroid values ​​to provide an early estimate of the location of each square.

[0119] A global set of square (x;y) coordinates was fitted to a linear model of rotation, translation, and scaling, generating modified estimates of the square's location based on the global model.

[0120] Detailed image registration Each square was precisely positioned by identifying the centers of its four edges and averaging the location information. Fitting to vertical edges was performed by numerically differentiating along each row, cubing the results to identify edges, and then using the product of these derivatives as weights for centroid calculations to estimate where the edges are located along the row coordinates. The sets of edge coordinates and row numbers were fitted to a linear model of gradient and offset using a robust median statistical approach. This method was repeated for all four edges, swapping rows and columns for horizontal edges. The sets of edge offsets were used to estimate the center of the squares, and the median gradient was used to calculate the rotation angle of the squares used in the calculation of sloping edges.

[0121] Next, the finely registered square fit values ​​were refitted to a global model of translation, rotation, and scaling of the entire image. This result was applied to calculate the relative orientation of two cameras in a multi-camera system, specifically the relative image translation Δx;Δy and rotation θ. The scaling parameter was used to calculate the overall scaling of each image. Distortion can be calculated by modifying the fit function to include a specific distortion model, or by collectively analyzing the residuals of the fit to a model specifying a particular distortion model (e.g., no distortion).

[0122] Fitting PSF models to ESF data According to embodiments of this disclosure, ESF data can be obtained using several methods, and a PSF that matches the LSF × LSF approximation (Equation 14) can be created as follows.

[0123] Cross product (direct product) 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 their cross product can be taken. The resulting matrix can be used to oversample the PSF by coefficient a when the edges are tilted by 1 / a radians, and downsample the image by integration to generate a pixelated version of the PSF.

[0124] However, in other embodiments, this approach is not used for several reasons. Firstly, it does not provide much averaging of the data and is sensitive to high numerical derivative noise overall. Secondly, this is an estimate of the pixelated PSF, and in design specifications and testing, the unpixelated PSF is often referred to as the output of general optical modeling software.

[0125] ESF interpolation The noise in numerical differentiation can be reduced by fitting the ESF to N smooth continuous functions using known derivatives, where N is less than the total number of data points in the experimental ESF.

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[0129] This is useful for noise reduction, but unlike the previous method, it doesn't directly fit unpixelated PSFs.

[0130] Fitting the ESF to the PSF model Improvements in fitting ESF to smooth functions include first PSF a The approach involves assuming a specific model of the PSF represented by and then using that model to derive the analytical formula for the ESF. The key to this is that the PSF model is a function separable in x and y. The Gaussian basis system has an inverse width b of the logarithmic interval. i While it is selected using [this method], in principle other basis systems can also be used.

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[0135] In the mathematical formula, (x, y) are the field coordinates of a point on the object plane within the test target 116, and N g is the number of Gaussian distributions used to include the basis system utilized for modeling the PSF, and a i is a set of coefficients that weight the contribution of each Gaussian distribution i in the model of Equation 20, and b i is proportional to the inverse square of the width of each Gaussian distribution.

[0136] The non-pixelated ESF can be directly calculated from the perspective of coefficients.

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[0140] In the mathematical formula, erf(x) is the Gaussian error function, that is, the definite integral of each Gaussian distribution, and is generally available in numerical libraries.

[0141] The pixelated ESF can be calculated by numerical integration of the non-pixelated ESF as follows.

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[0144] The fitting function used to fit LSF is LSF x (x-x0)=LSF x The ESF is symmetric with respect to x0 such that (x0-x). This symmetry is not strictly necessary, but was used to reduce the fit space and enhance the equivalence of the ESFs from the contrast between the rising and falling ESFs, i.e., the contrast between the right side of the square and the left side of the square. Images from high-gain sensors are a counting process with limited shot noise, and in absolute terms, the variance of the dark areas at the bottom of the ESF data is smaller than the variance of the ESF data at the top, so the upper and lower fitted regions of the ESF are statistically quite different. If weighted linear least squares of the ESF is performed using the signal level (units of photoelectrons) of each ESF data point as a weight corresponding to its variance, this results in different fits to the left and right ESFs for a PSF with an LSF that is asymmetric with respect to the origin.

[0145] Alternative algorithms can utilize different basis sets in Equation 20 that ignore the obvious symmetries used here. Such basis sets can be functions commonly used for interpolation, such as B-splines, polynomials, or combinations of Gaussian distributions used in Equation 20 with polynomials of odd powers or sinusoidal functions of various frequencies, enabling the fitting of asymmetric LSFs. In simultaneous linear least squares for ascending and descending ESFs (e.g., left and right edges of a square), asymmetric LSFs can be fitted to asymmetric basis functions using weights derived from shot noise.

[0146] Numerical fitting In practice, the image data is fitted using linear least squares. QR decomposition is used as part of the well-known DGELS LAPACK (linear algebra package) routine implemented in the Intel MKL Math kernel library. The image data Img is fitted using the design matrix D to obtain the fit coefficient Ai. Several pre-calculated versions of D exist using nine different offsets x0, and every row of the data uses the row of D corresponding to the x0 value closest to the x0 value of the data.

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[0152] The fit coefficient X[0] represents the baseline intensity of the image due to background noise and bleed-through of the test target. If the fitted data is a vertical edge that becomes ESFx(x), then the coefficients X[1];X[2];:::;X[Ng] are the coefficients ax;1,ax; in equation 20 2 ,:::,ax;Ng is shown. The fitted data is E If it is a horizontal edge that is SFy(y), then the coefficients X[1];X[2];:::;X[Ng] are the coefficients ay;1,ay; in equation 20 2 ,:::,ay; Indicates Ng.

[0153] PSF model reconstruction and sharpness calculation The PSF model uses the fit coefficient a obtained in the previous section. i It can be reconstructed from this. Several different models demonstrate different levels of knowledge. The most complete model combines vertical LSF and horizontal LSF using Equation 20, and the ensquared energy can be calculated directly from the simple product of the sum of the coefficients.

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[0156] The accuracy of this method will be explained below.

[0157] The encircled energy can also be calculated numerically by evaluating equations 3 and 20. The Strehr ratio takes a simple form, as follows:

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[0159] Alternative Simple PSF Model While PSF is fundamentally a two-dimensional concept, it has been useful to represent the EE of the PSF determined by assuming that both the vertical LSF and horizontal LSF are identical. Such applications include searching for astigmatism in an image by comparing the focal position that maximizes the contrast between the EE estimated from the horizontal ESF and the EE estimated from the vertical ESF. For this purpose, a simple PSF model is used with a fit coefficient a i It is used in a very limited number of applications.

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[0164] This form of data feedback is the primary method used to perform system alignment and has proven to be highly stable in operation.

[0165] The model in Equation 37 was chosen to be separable with respect to x and y so that a two-dimensional reconstruction of the PSF can be obtained from ESF data containing only information about the one-dimensional LSF. Therefore, this model may not be able to represent the complex PSF resulting from highly aberrated or out-of-focus systems, but it is sufficient to describe the PSF of well-compensated, in-focus systems. The ESF can be further modified to reflect the degradation of the PSF due to the contribution of the detector MTF, which in the worst case is achieved by averaging the exact ESF in Equation 38 over the entire length of each pixel.

[0166] For example, data fitting based on linear least squares regression is performed by fitting equation 38 to the experimental data to obtain the coefficient ai. The PSF model is constructed by evaluating equation 37 using the value of ai obtained from the fit. Next, the EE value is calculated analytically by integrating equation 37 over a square region to obtain the equation 39 To obtain.

[0167] Algorithm accuracy The overall accuracy of the algorithm was evaluated using a composite image with known PSFs, using the same real-world PSFs that were used to evaluate the accuracy of the LSF×LSF approximation. Next, the PSFs were convolved with an image of an infinitely sharp test target. The convolution presents an ideal, noise-free image with a 10×14 slanted square grid. The squares offer various tests in that the placement of each square relative to the camera's pixel grid differs at the sub-pixel level depending on where the center of the square lies, making it impossible to superimpose them on one another. To understand the effect of shot noise on the data, the image was first scaled to match the intensity of the actual image of the target, and a baseline was added to account for the system's background intensity and the bleed-through effect of the test target. The scaled image was expressed in units of photoelectrons. A shot-noise sampled image was created from this scaled image by sampling the intensity of each pixel from a specially calculated distribution for each pixel. The Poisson distribution of the shot noise was approximated as a normal distribution with a mean corresponding to the mean of the scaled image and a variance equal to the mean of the image. The image was ultimately 2 16 By using a scaling factor of 10,550 photoelectrons per -1 image count, the image was scaled to a depth of 12 bits as the upper bits of a 16-bit TIFF.

[0168] After creating each image, the complete analysis pipeline was run to estimate the ensquared energy of each square using Equation 35. Furthermore, the PSF model was fitted with the fitting coefficient ax;i and a y;i This was created to allow for direct comparison. Figures 4A–4F show the results of a moderately sharp PSF with a true EE of 0.594. The 3D plots of the PSFs show the true PSF (Figure 4A), the PSF derived from the LSF×LSF approximation (Figure 4B), and finally the PSF model reconstructed from the analysis of the composite image and fitting of the ESF data (Figure 4C). The differences between the three PSFs are shown in Figures 4D–4F using the same vertical scale. As expected, the PSF model is very similar to the LSF×LSF approximation. Note that the density of the true PSF in the base decreases faster in the front right of the plot than in the front left of the plot. This property is preserved by the LSF×LSF model, and the PSF model from the ESF data is also faithfully recovered. The LSF×LSF approximation causes an underestimation of the peaks and an overestimation of the horizontal and vertical rows and columns arising from the peaks. This results in an underestimation of the true EE, making it an unavoidable approximation. When viewed from above, the PSF has a distinctive "+" shape. Nevertheless, it is a faithful reconstruction of the LSF×LSF model and the fit coefficient a x;i and a y;i The accurate calculation of the EE from only suggests that the ESF fitting procedure is working as expected.

[0169] Two additional PSFs were fitted in the same way, and the sharp and blurry PSFs are shown in Figures 5A-5F and 6A-6F, respectively. As expected, the asymmetry of the blurry PSF is not properly captured by the LSF×LSF model.

[0170] The composite image analysis was repeated for nine different experimental PSFs calculated across a range of 31 different focal positions, using the same PSFs used in Figure 7A. Figure 7B shows a scatter plot comparing the EE estimated using Equation 35 with the EE of a known starting PSF. Each point in the scatter plot represents an image consisting of 10 × 14 squares, each square sampled individually with the expectation that those squares will produce the same estimate of EE, along with noise arising from both shot noise and algorithmic errors (if any) that are exposed when multiple squares with different subpixel arrangements are displayed. Error bars show the standard deviation of all 140 measurements. The points show natural clustering at various locations resulting from the generation of PSFs, creating a series of similar PSFs around the optimal focal point of each PSF.

[0171] The EE is underestimated by the algorithm, which is expected to be a core LSF×LSF approximation. The EE estimate is expected to exactly match the LSF×LSF approximation, but in this case it is slightly higher. This will result in a more accurate overall estimate, but also presents an opportunity to improve the performance of the numerical methods for ESF fitting and EE estimation.

[0172] The accuracy of the EE estimation in Figure 7B is very acceptable for instrumental testing purposes, demonstrating that the basic approach employed by the method disclosed herein can generate usable data and a useful testing platform.

[0173] Algorithm accuracy Judging from the relatively strict standard deviation of the EE estimation in Figure 7B, the accuracy of the algorithm is acceptable. However, using feedback for optical alignment presents an additional level of requirement that goes just beyond a strict standard deviation. Typically, when performing optimization with small perturbations in the system, it is important that the feedback signal does not jump around as a function of the adjustment variable. Figure 7C is a plot of the contrast between EE and focal position, showing that the algorithm provides a very smooth and predictable response curve to the perturbation of the focus blur. The distance between the red and black lines (or the circular and triangular data points, respectively) indicates that the EE is underestimated, but it is an underestimation that is systematic and consistent, and the inaccuracy of the method does not jump around significantly even as the focus blur position changes point by point. Therefore, this curve is suitable for a higher level of interpretation and fitting, such as to determine the focus blur position where the EE is maximized.

[0174] As mentioned above, alternative basis sets can be used to approximate the LSF, including a basis set that enables fitting of an asymmetric LSF. Using such methods may yield higher accuracy than that shown in Figure 7B. However, it is necessary to verify that the response curve in the form of Figure 7C is smooth enough for use in optimization. An unproven concern is that using more fitting parameters will impair smoothness. However, this assumption remains untested, and higher-accuracy models may, contrary to expectations, actually be just as smooth or even smoother.

[0175] EE score The ensquared energy (EE) value can be used as a metric for measuring the imaging performance of the optical system 104 under test. The user may perform initial setup or adjustments to the optical components of the system (e.g., alignment between components and between components and the test target 116). The user may then perform a test of the optical system 104 according to the method described herein. Based on the test results, the user may determine whether further adjustments are needed to improve the results (e.g., further optimization of alignment and focus). After further adjustments, the user may perform the test again. The repetition of testing and adjustments may be continued until the results are considered acceptable.

[0176] The optical system 104 can be determined to have passed the imaging performance test if all field points in the image exceed a predetermined minimum threshold EE value (e.g., EE > 0.51). However, evaluating imaging performance solely on whether all EE values ​​pass may be considered unfavorable in some situations. This approach may not be acceptable if the optical system 104 is otherwise a sharp (high-quality) system, although it may exhibit some blurry spots. Furthermore, because optical performance is continuously evaluated during the alignment procedure, the discontinuity arising from the use of thresholding can make it problematic to use the thresholding method as an optimization process where continuous measurement is preferred (e.g., alignment against laser output or alignment against least-mean-square (RMS) wavefront error), and the thresholding effect may not be useful in calculating manufacturing yield.

[0177] To overcome the aforementioned potential disadvantages, further embodiments of the method disclosed herein use an EE score called E S This is calculated based on the EE value. The EE score describes the entire field using a single continuous variable that must exceed (or meet, or exceed) a predetermined minimum threshold in order to pass the imaging performance test. For example, the EE score is E SYou may weight the scores so that a score of ≥100 is considered a passing score. The EE score has the following two elements: <ee>The overlap integral EO is the spatial average of all EE values ​​observed at all field points over a specified depth of field, and measures the consistency of EE across different imaging channels (e.g., four channels corresponding to four colors from which the image is filtered, where in the illustrated embodiment, two imaging devices 124 and 128 are used in combination with an adjustable filter assembly 148 to acquire images on two of the four channels). The EO is rewarded if sharp and blurred areas coincide at both the field position (x,y) and the focal position (z). The EO is penalized if the four channels are inconsistent, for example, if the two imaging devices 124 and 128 are not sufficiently in focus with respect to each other, or if the blue channel is sharp in the upper left corner and blurred in the lower right corner while the red channel is on the opposite side. The EE score and its components are calculated according to the following equations 41-44.

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[0182] In the formula, x and y are of size N. x ×N y The field points are arranged in a grid, where c is the color (for example, in a 4-channel embodiment, colors 1, 2, 3, and 4 may correspond to blue, green, red, and amber), z is the focal position, and nm is in nanometers.

[0183] The EE score considers contributions from all channels, but it is also useful to have a similar score that considers only one channel at a time, and only considers uniformity with respect to field position and focus position. Channel-specific EE score EC c This is an example where the heterogeneity of a single channel is captured by Equation 46, similar to the mean squared variance.

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[0188] If the integral of equation 46 is zero, the penalty represented by the heterogeneity EH becomes zero.

[0189] EE vs Z fitting The integrations of equations 42, 43, and 46 are aimed at characterizing the imaging performance with respect to the focus and can be calculated using the simple sum of experimental data. Alternatively, the integration can also be calculated by first fitting the experimental pairs of EE values and their corresponding z locations to a Gaussian profile (Equation 49). Next, the integration is calculated as the analytical solution of the definite integral of the Gaussian fitting functions for each of the following equations 49 - 55.

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[0197] In the formula, a is the peak EE value obtained for the focus, w is the Gaussian width, and z0 is the z value corresponding to the peak EE value. This is particularly useful in that it allows for taking relatively coarse z steps (thus reducing data acquisition) while accurately capturing z0 within less than one number of z step sizes. Formula 55 may be used to calculate the integral of the above-mentioned formula 42.

[0198] In the case of the EO operation, the overlapping integral between different channels in formula 44 can also be calculated using the analytical integral of the Gaussian fit of experimental data, as shown in formulas 56 to 63.

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[0207] In the formula, W1 and W2 are the widths of the Gaussian distributions corresponding to channels 1 and 2. The product of the two Gaussian distributions is the width W P Z0 P This is a third Gaussian distribution centered at [this point]. You can also use equations 56-63 to calculate equation 47 above.

[0208] The penalty indicates how much performance was lost due to various optical defects.

[0209] In addition to estimating the current EE score for the optical system 104, this method may also involve informing 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 may inform the user which particular defects are having the most adverse impact on imaging performance. For this purpose, the method may calculate one or more penalties associated with each of one or more artifacts. The penalty is the difference between the current EE score and the score that would be obtained if the defect were explained. This method may also include displaying information corresponding to the penalties across the entire field of the image displayed to the user. <ee>When considering only the contribution of (no overlapping score EO), the penalty calculation is always larger. <ee>It should be a quantity. The penalty is communicated as the difference between the actual score and the artificially increased score, and this must always be a negative number. When overlapping score EO is considered, the penalty calculation may be a positive number.

[0210] One example of a penalty is a penalty due to the Z-shift of an imaging device (e.g., a camera). When calculating this penalty, this method may assume that both imaging devices 124 and 128 can be optimally focused on each other, but it does not assume that there is zero axial chromatic aberration in imaging devices 124 and 128. This method calculates the ideal focal position for the first imaging device 124 as the average of two wavelength-filtered imaging data acquired by the first imaging device 124 (e.g., the average of blue and green data). This method also calculates the ideal focal position for the second imaging device 128 as the average of two wavelength-filtered imaging data acquired by the second imaging device 128 (e.g., the average of red and amber data).

[0211] Another example of a penalty is a penalty due to the tilt of the imaging device (e.g., camera). For each channel, this method constructs the focal plane as the average of both colors (e.g., blue and green in channel 1), fits it to the plane using linear least squares, and finds the tip and tilt portions that are removed from the focal point while maintaining the focal difference of each imaging device 124 and 128.

[0212] Another example of a penalty is the penalty for astigmatism. The EE scores for horizontal LSF and vertical LSF are calculated separately, allowing for optimization in the direction of tilt and height of the focal plane. The maps are then averaged to create the final estimate.

[0213] Another example of a penalty is the penalty for axial chromatic aberration. This penalty is calculated twice, once for each channel. For channel 1, the optimal focus for each of the two colors (e.g., blue and green) is found, the difference is divided, and each channel is offset by that amount. The resulting focus position may still not be ideal because the difference in focus between cameras is imperfect.

[0214] Another example of a penalty is the penalty for field flatness. This penalty is calculated twice, once for each channel. For channel 1, the optimal focal planes of the two color exposures (e.g., blue and green exposures) are averaged, and the optimal fit plane is subtracted from the focal position of the data (e.g., blue and green), while channel 2 is processed normally. This penalty can be interpreted inconsistently. For example, if both channels are equally non-flat, then both <ee>Problems arise from this perspective, but overlapping EOs are strengthened in a productive way. Flattening the channels eliminates both effects. Typically, <ee>While the influence of [this factor] is dominant, this is not guaranteed.

[0215] Figure 8A is an example image acquired from one of the target characteristics 313 of the test target 116. Figure 8B is an example ESF calculated from the image in Figure 8A. In Figure 8B, image coordinate zero corresponds to one of the edges of the imaged target characteristic 313. Some of the data points in Figure 8B are outliers. As shown, one or more of these outliers may correspond to dark artifacts on the target characteristic 313 (e.g., dirt or debris from manufacturing). This method may include removing outliers from the ESF data (e.g., ignoring or deleting them).

[0216] Figure 9 shows an example of the result obtained by fitting the ESF from Figure 8B to an error function and calculating the EE from the ESF.

[0217] Figure 10 shows an example of calculating the ESF for the four edges of the target characteristic 313, fitting the ESF to an error function, and calculating four independent EE values ​​from the corresponding ESFs. Nominally, the left edge and the right edge will have the same measurement (EE). left =EE right ), the upper edge and lower edge will have the same measurement (EE up =EE down However, the measurements of the left and right edges are not necessarily the same as the measurements of the top and bottom edges.

[0218] In alternative embodiments, encircled energy may be used as the imaging performance metric instead of ensquared energy. In further alternative embodiments, the Strehr ratio may be used as the imaging performance metric instead of ensquared energy.

[0219] Figure 11 is a flowchart 1100 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 one embodiment of the present disclosure. As shown, the method is initiated by moving the Z-stage of the objective lens to an approximate focal position. Next, the user initiates the Z-stack acquisition process, which acquires multiple images of the test target at different focal positions. Next, the optical system hardware performs the Z-stack acquisition, an embodiment of which is shown in Figure 12. Next, the Z-stack range is compared with the focal performance during Z-stack acquisition. Next, the Z-focal position is updated based on the Z-stack results. Next, it is determined whether the focal point is in the middle third of the Z-stack range. If "yes", the method determines that the currently acquired data is usable, performs a global calculation on the Z-stack, and the newly generated data is notified to the user.

[0220] On the other hand, if it is determined that the central third of the Z-stack range is out of focus, imaging performance values ​​(e.g., estimates of the ensquared energy as described herein) are calculated from the currently acquired data, but the user is warned that the data is incomplete or unreliable because a focus could not be found in the center of the range. The Z-stack acquisition process may then be repeated using inferences based on more information about the focal position obtained from this Z-stack.

[0221] Figure 12 is a flowchart 1200 illustrating the acquisition of an image Z-stack according to one embodiment of the present disclosure. After the user starts Z-stack acquisition (Figure 11), the range of Z positions around the initial focus estimation is calculated. Each field of view within the set of FOVs i Regarding the Z stage, the FOV i The system is instructed to move to the corresponding defocus position. Next, the actual position of the Z-stage is queried and recorded. Then, images from all imaging devices are acquired simultaneously. Next, high-speed processing of each image itself is performed, an embodiment of which is further shown in Figure 13. Next, the image data is pooled. Then, multiple PSF models, one for each ESF, are constructed by fitting the ESFs to a basis set of Gaussian error functions, as described herein.

[0222] Figure 13 is a flowchart 1300 illustrating high-speed processing of the image itself according to one embodiment of the present disclosure. The image is downsampled to representations of intensity, horizontal centroid, and vertical centroid. Next, a coarse global registration is performed using the downsampled image. Each square (or other type of target characteristic of the test target) is positioned using centroid data. Next, the positions of multiple squares are fitted into a global model of translation, rotation, and scaling. Next, ESFs are extracted from each edge of each square, and the angle and center of the edge are calculated. Next, outliers are removed from the ESF data. Next, multiple PSF models are constructed by independently fitting each ESF to a basis set of Gaussian error functions, as described herein. Next, for each ESF, an imaging performance value (e.g., an estimate of the ensquared energy as described herein) is calculated. The data can be averaged (EE horizontal =( EE left =EE right ) / 2, EE vertical =( EE up =EE down ) / 2). The process shown in Figure 13 may be repeated for multiple images, and the resulting image data can be pooled (Figure 12) and further processed.

[0223] In one embodiment, one or more of the flowcharts 1100, 1200, and 1300 may show an optical imaging performance test system or a part of an optical imaging performance test system configured to perform (e.g., control or execute) the steps or functions described above and shown in one or more of the figures 11, 12, and 13. For this purpose, a controller (e.g., controller 108 shown in Figure 1) including a processor, memory, and other components as understood by those skilled in the art may be provided to control the performance of the steps or functions, for example, by controlling the components of the test system involved in performing the steps or functions. This may be involved in receiving and processing user input and / or machine-executable instructions, as understood by those skilled in the art.

[0224] Figure 14 shows an example of a set of EE performance maps that may be generated according to the method and can be displayed to a user on the display screen of a computing device (e.g., computing device 168). The columns of the maps correspond to different colors, which in this embodiment are blue, green, amber, and red from left to right. In the top row (row 1), each map displays the average EE for each point in the field of view (FOV). In the next row (row 2), each map displays the best EE found in terms of focus blur value for each point in the FOV. In the next row (row 3), each map displays the optimal focus surface as determined by the EE. In the next row (row 4), each map displays the astigmatism of the comparison between horizontal and vertical imaging data as determined by the EE. In the bottom row (row 5), each map displays the depth of field as the z range over which the EE exceeds the minimum value.

[0225] Figure 15 shows an example of combined EE scores and performance maps that may be generated according to the method and can be displayed to the user on the display screen of a computing device (e.g., computing device 168). The top map (1) is a global EE map showing the available EE for all channels and out-of-focus positions. Below the top map (1), group of maps (2) shows the contributions of various optical defects as described above. Thus, these maps (2) inform the user how much imaging performance will improve if the defects in question are addressed. In this embodiment, the first row of group map (2) shows the global scores for different colors, in this embodiment from left to right: blue, green, amber, and red. The second row of group map (2) shows, from left to right, overlap, penalty to EE due to imaging device Z-shift, penalty to EE due to on-axial chromatic aberration of channel 1, and penalty to EE due to on-axial chromatic aberration of channel 2. The third row of group (2) shows, from left to right, the penalty to EE due to astigmatism, the penalty to EE due to tilt of the imaging device, the penalty to EE due to the flatness of the field in channel 1, and the penalty to EE due to the flatness of the field in channel 2.

[0226] Figure 16 is a schematic diagram of a non-limiting example of a system controller (or controller) 1600 that is part of an optical imaging performance test system or can communicate with an optical imaging performance test system, according to one embodiment of the present disclosure. For example, the system controller 1600 may correspond to the system controller 108 of the test system 100 described above and shown in Figure 1.

[0227] In the illustrated embodiment, the system controller 1600 includes a processor 1602 (typically electronically based) representing a primary electronic processor that provides overall control, and one or more electronic processors (e.g., graphics processing unit or GPU, digital signal processor or DSP, application-specific integrated circuit or ASIC, 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 an interface between the user interface devices and 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., keyboard, keypad, touchscreen, mouse, joystick, trackball, etc.) and user output devices 1610 (e.g., display screen, printer, visual indicator or alert, audible indicator or alert, etc.).

[0228] In various embodiments, the system controller 1600 may be considered to include, or at least communicate with, one or more user input devices 1608 and / or 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 one or more types of computer-readable media 1614. The computer programs or software may include non-temporary instructions (e.g., logical instructions) for controlling or performing 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., a Microsoft Windows® operating system) for controlling and managing various functions of the system controller 1600, including interactions between hardware and application software. In particular, the operating system may provide a graphical user interface (GUI) that is viewable via the user output device 1610 and that the user can interact with using the user input device 1608.

[0229] The system controller 1600 may also include one or more data acquisition / signal conditioning components (DAQs) 1616 (which can be embodied in hardware, firmware, and / or software) for receiving and processing signals (e.g., imaging data) output by the optical system 104 described above and shown in Figure 1, including a data format for presentation in graphical form via a GUI. The DAQs 1616 can also be configured to transmit control signals to the optical system to control the movement / positioning of adjustable optical components as described herein. The DAQs 1616 may correspond to all or part of the electronics module 164 described above and shown in Figure 1.

[0230] System Controller 1600 This may further include a data analyzer (or module) 1618 configured to process signals output from an optical system and generate data therefrom, including imaging performance metrics, scores, maps, etc., as described throughout this disclosure. Thus, the data analyzer 1618 can be configured to implement (control or perform) all or part of any of the methods disclosed herein. For these purposes, the data analyzer 1618 can be embodied in software and / or electronics (hardware and / or firmware) as understood by those skilled in the art. The data analyzer 1618 may correspond to all or part of the electronics module 164 and / or computing device 168 described above and shown in Figure 1.

[0231] Figure 16 will be understood to be a high-level schematic diagram of an example of a system controller 1600 consistent with the present disclosure. Other components, such as additional structures, devices, electronics, and computer-related or electronic processor-related components, may be included as required by the actual embodiment. It will also be understood that the system controller 1600 is schematically represented in Figure 16 as a functional block intended to show the structures it may have (e.g., circuits, mechanisms, hardware, firmware, software, etc.). Various functional blocks and signal links between them are arbitrarily positioned for illustrative purposes only and are not limiting in any way. Those skilled in the art will understand that the functions of the system controller 1600 can be performed in various ways, not necessarily in the way shown in Figure 16 and described by example herein.

[0232] Exemplary Embodiments Exemplary embodiments provided herein in accordance with the subject matter disclosed herein include, but are not limited to, the following:

[0233] 1. A method for testing the imaging performance of an optical system, comprising: positioning a test target on the object plane of the optical system; operating the optical system to illuminate the test target and generate an image beam; operating the focusing stage of the optical system to acquire multiple images of the test target from the image beam corresponding to multiple defocus values; calculating multiple edge spread functions at multiple locations within the test target from each image; constructing multiple point spread functions from each edge spread function; and calculating multiple imaging performance values ​​corresponding to multiple locations based on the point spread functions, wherein the imaging performance values ​​are based on a metric selected from the group consisting of ensquared energy, encircled energy, and Strehr ratio.

[0234] 2. The method according to Embodiment 1, wherein the multiple locations include multiple field coordinates (x,y) in the object plane within the test target.

[0235] 3. The method according to Embodiment 1 or 2, wherein the multiple locations include multiple focal positions (z) along the optical axis passing through the test target, and operating the optical system includes acquiring multiple images of the test target at different focal positions (z).

[0236] 4. A method according to any one of embodiments 1 to 3, comprising generating one or more maps of imaging performance based on a combination of imaging performance values.

[0237] 5. The method according to Embodiment 4, comprising comparing two or more maps to provide a measure of the relative alignment of the focal planes of each imaging device or channel with respect to the object plane.

[0238] 6. The method according to Embodiment 4 or 5, wherein one or more maps correspond to different imaging channels, and the different imaging channels correspond to different imaging devices in an optical system that operates to acquire an image, or different wavelengths of the image to be acquired, or both different imaging devices and different colors.

[0239] 7. The method of Embodiment 6, comprising comparing two or more maps to provide a measure of the relative alignment of each imaging device or channel relative to each other, and both of the foregoing.

[0240] 8. The method according to any one of embodiments 4 to 7, comprising, after generating one or more maps, adjusting the position of one or more optical components of an optical system, or replacing one or more optical components based on information provided by one or more maps.

[0241] 9. The method according to Embodiment 8, wherein one or more optical components are selected from the group consisting of one or more imaging devices, an objective lens of an optical system, one or more tube lenses of an optical system, one or more mirrors or dichroic mirrors, and two or more combinations of the above.

[0242] 10. The method according to Embodiment 8 or 9, further comprising: one or more maps being one or more initial maps; obtaining a new image of the test target after adjusting or replacing one or more optical components; calculating a plurality of new imaging performance values; and generating one or more new maps of imaging performance.

[0243] 11. The method according to Embodiment 10, comprising comparing one or more new maps with one or more initial maps to determine positional adjustments to be made to one or more optical components to optimize imaging performance.

[0244] 12. The method according to any one of embodiments 8 to 11, wherein an optimal pair of conjugate image planes is found in the optical system by adjusting or replacing them.

[0245] 13. The method according to any one of embodiments 8 to 12, wherein adjusting or replacing improves an attribute selected from the group consisting of: focus matching of the imaging device, tilt of the imaging device, flattening of field curvature, reduction of astigmatism, reduction of wavelength-dependent focus shift, and two or more combinations thereof.

[0246] 14. The method according to any one of embodiments 1 to 13, comprising calculating one or more global scores for imaging performance based on a combination of imaging performance values.

[0247] 15. The method according to Embodiment 14, comprising modifying one or more global scores to penalize or reward non-uniformity of imaging performance values ​​over a range of field coordinates (x,y) in the object plane within the test target, or through a range of focal positions (z) in the object plane, or both of the foregoing.

[0248] 16. The method according to Embodiment 14 or 15, comprising modifying 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 as a function of the focal position (z) of the object plane, or both thereof, wherein the different imaging channels correspond to different imaging devices in the optical system that operate to acquire an image, or to different wavelengths of the acquired image, or to both different imaging devices and different colors.

[0249] 17. The method according to any one of embodiments 1 to 16, comprising calculating imaging performance values ​​and then adjusting the position of one or more optical components of an optical system based on the information provided by the imaging performance values.

[0250] 18. The method according to Embodiment 17, further comprising: the imaging performance value being an initial imaging performance value; acquiring a new image of the test target after adjusting one or more optical components; and calculating a plurality of new imaging performance values.

[0251] 19. The method according to Embodiment 18, comprising comparing a new imaging performance value with an initial imaging performance value to determine positional adjustments to be made to one or more optical components in order to optimize imaging performance.

[0252] 20. The method according to any one of embodiments 1 to 20, wherein positioning the test target includes aligning the target with a datum shared with one or more optical components of an optical system.

[0253] 21. The method according to any one of Embodiments 1 to 20, wherein operating the optical system includes utilizing an objective lens in an image beam, and further includes adjusting the position of the objective lens along the axis of the image beam to acquire multiple images of a test target at different focal positions (z).

[0254] 22. The method according to Embodiment 21, wherein the objective lens has a configuration selected from the group consisting of the objective lens being configured for an infinite conjugate microscope and the objective lens being configured for a finite conjugate microscope.

[0255] 23. The method according to any one of Embodiments 1 to 22, wherein operating the optical system includes operating two or more imaging devices to acquire images of each of the test targets.

[0256] 24. The method according to Embodiment 23, wherein two or more imaging devices acquire images at two or more different wavelengths.

[0257] 25. The method according to Embodiment 24, comprising dividing an image beam propagating from a test target into two or more image beam portions, and transmitting the two or more image beam portions to two or more imaging devices, respectively.

[0258] 26. The method according to any one of Embodiments 1 to 25, wherein operating the optical system includes operating a filter assembly to filter the image beam at a selected wavelength.

[0259] 27. The method according to any one of Embodiments 1 to 26, wherein operating the optical system includes utilizing a tube lens in an image beam, and further includes adjusting the relative positions of one or more lenses or lens groups within the tube lens to acquire multiple images of a test target at different positions on the tube lens.

[0260] 28. The method according to any one of Embodiments 1 to 27, wherein the test target comprises a dark material and an array of bright properties placed on the dark material.

[0261] 29. The method according to embodiment 28, wherein the bright characteristic is polygonal.

[0262] 30. The method according to Embodiment 29, wherein the bright feature is tilted such that the edges of the bright feature are oriented at a certain angle with respect to the pixel array of the optical imaging system that acquires the image.

[0263] 31. An optical imaging performance test 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 positioned in the imaging optical path between the test target and the imaging device, wherein at least one position of the objective lens or the target holder is adjustable along the imaging optical path; and a controller having an electronic processor and memory, configured to control the steps of the method according to any one of the prior embodiments: calculating a plurality of edge spread functions; constructing a plurality of point spread function models; and calculating a plurality of imaging performance values.

[0264] 32. The system according to Embodiment 31, wherein the objective lens has a configuration selected from the group consisting of the objective lens being configured for an infinite conjugate microscope and the objective lens being configured for a finite conjugate microscope.

[0265] 33. The system according to embodiment 31 or 32, wherein the imaging device comprises a plurality of imaging devices, and the system further comprises an image separation mirror configured to divide an imaging optical path into a plurality of imaging optical paths, each directed toward an imaging device.

[0266] 34. The system according to any one of embodiments 31 to 33, comprising a filter assembly configured to select the wavelength of an image beam in an imaging optical path for propagation to an imaging device.

[0267] 35. The system according to any one of embodiments 31 to 34, comprising a tube lens positioned in the imaging optical path, wherein the relative positions of one or more lenses or lens groups within the tube lens are adjustable.

[0268] 36. The system according to any one of embodiments 31 to 35, comprising a test target, the test target comprising a dark material and an array of bright properties placed on the dark material.

[0269] 37. The bright characteristic is polygonal, as described in Embodiment 36.

[0270] 38. The system according to Embodiment 37, wherein the bright feature is tilted such that the edges of the bright feature are oriented at a certain angle with respect to the pixel array of the optical imaging system that acquires the image.

[0271] 39. A non-temporary computer-readable medium containing stored instructions that, when executed on a processor, perform the steps of: calculating multiple edge spread functions; constructing multiple point spread function models; and calculating multiple imaging performance values, according to the method of any one of the preceding embodiments.

[0272] 40. A system for testing the imaging performance of an optical system, comprising a computer-readable storage medium as described in Embodiment 39.

[0273] It is understood that one or more of the processes, subprocesses, and process steps described herein can be executed on one or more electronic or digitally controlled devices by hardware, firmware, software, or a combination of two or more of the foregoing. The software may reside in software memory (not shown) in a suitable electronic processing component or system, such as controller 108 or 1600 schematically shown in Figure 1 or 16. The software memory may contain an ordered list of executable instructions for implementing logic functions (i.e., “logic” which can be implemented in digital form, such as digital circuits or source code, or in analog form, such as analog sources, such as analog electrical, sound, or video signals).

[0274] Instructions can be executed within a processing module, 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). Furthermore, schematic diagrams illustrate logical divisions of functions having physical (hardware and / or software) embodiments that are not limited by the architecture or physical layout of the function. The examples of systems described herein may be implemented in various configurations and may operate as hardware / software components in a single hardware / software unit or in separate hardware / software units.

[0275] An executable instruction can be implemented as a computer program product containing stored instructions that, when executed by a processing module of an electronic system (e.g., controller 108 or 1600 in Figure 1 or 16), instructs the electronic system to execute the instruction. The computer program product can be selectively embodied in any non-temporary computer-readable storage medium for use in conjunction with, for example, an instruction execution system, apparatus, or device such as an electronic computer-based system or a processor-based system, or by another system capable of selectively fetching and executing instructions from an instruction execution system, apparatus, or device, or in any other system capable of doing so.

[0276] In this specification, a computer-readable storage medium is any non-temporary means capable of storing a program for use by or in connection with an instruction execution system, apparatus, or device. A non-temporary computer-readable storage medium may optionally be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. A non-exclusive list of more specific examples of non-temporary computer-readable media includes electrical connections having one or more wires (electronic), portable computer diskettes (magnetic), random access memory (electronic), read-only memory (electronic), erasable programmable read-only memory such as flash memory (electronic), compact disk memory such as CD-ROM, CD-R, CD-RW (optical), and digital multipurpose disk memory, i.e., DVD (optical). Since a program can be electronically captured, for example, via optical scanning of paper or other media, then compiled, read, and processed in an appropriate manner as needed, and stored in computer memory or machine memory, the non-temporary computer-readable storage medium may be paper on which the program is printed or another suitable medium.

[0277] As used herein, the term “in signaling communications” will also be understood to mean that two or more systems, devices, components, modules, or submodules can communicate with each other via signals propagating through some type of signaling path. These signals may be communication, power, data, or energy signals, and these signals may communicate information, power, or energy from a first system, device, component, module, or submodule to a second system, device, component, module, or submodule, or between a first system, device, component, module, or submodule and a second system, device, component, module, or submodule along a signaling path. The signaling path may include physical, electrical, magnetic, electromagnetic, electrochemical, optical, wired, or wireless connections. The signaling path may also include additional systems, devices, components, modules, or submodules between a first system, device, component, module, or submodule and a second system, device, component, module, or submodule.

[0278] More generally, terms such as “communicate” and “communicate” (e.g., “the first component communicates” or “is communicating” with the second component) are used herein to describe structural, functional, mechanical, electrical, signaling, optical, magnetic, electromagnetic, ionic, or fluidic relationships between two or more components or elements. Thus, saying that one component communicates with a second component is not intended to preclude the possibility that an additional component exists between the first and second components and / or is operably related to or engaged with one another.

[0279] It should be understood that various aspects or details of the present invention can be modified without departing from the scope of the present invention. Furthermore, the above description is for illustrative purposes only and not intended to limit, and the present invention is defined by the claims. Related applications This application asserts the interests under Section 119(e) of Provisional Patent Application No. 62 / 884,116, filed on 7 August 2019, entitled “Optical Imaging Performance Test System and Method,” the contents of which are incorporated herein by reference in their entirety.< / ee> < / ee> < / ee> < / ee> < / ee>

Claims

1. A method for testing the imaging performance of an optical system, Positioning the test target on the object plane of the optical system, The optical system is operated to illuminate the test target and generate an image beam, The focusing stage of the optical system is operated to acquire an image corresponding to one or more imaging channels of the test target, based on the image beam, using one or more imaging devices. From each image, calculate the edge spread function at multiple locations within the test target, Constructing a point spread function from the aforementioned edge spread function, The calculation involves determining imaging performance values ​​mapped to the plurality of locations based on the point spread function, wherein the imaging performance values ​​are based on a metric selected from the group consisting of ensquared energy, encircled energy, and Strehr ratio. The imaging performance of the imaging channel is calculated based on the distribution of the imaging performance values ​​at the multiple locations, and a map corresponding to the imaging channel is generated. Based on a map corresponding to the imaging channel of the imaging device with respect to the object plane, it is determined whether to optimize the alignment or focusing of the optical system. Includes, A method comprising the following: the plurality of locations include a plurality of field coordinates (x, y) in the object plane within the test target, and further include a plurality of focal positions (z) along the optical axis passing through the test target, wherein operating the optical system includes acquiring a plurality of images of the test target at coordinates (x, y, z) including the plurality of focal positions (z).

2. The method according to claim 1, wherein there are multiple imaging channels, and the alignment and focusing of the optical system are optimized based on a comparison of maps in the multiple imaging channels.

3. The method according to claim 2, wherein the plurality of maps correspond to different imaging channels, and the different imaging channels correspond to different wavelengths of the image acquired by the imaging device.

4. Optimizing the alignment or focus of the optical system is The method according to claim 1, comprising adjusting the position of one or more optical components of the optical system, or replacing one or more optical components.

5. The method according to claim 4, wherein the one or more optical components are selected from the group consisting of the imaging device, the objective lens of the optical system, the tube lens of the optical system, a mirror or dichroic mirror, and two or more combinations of the above.

6. The method according to claim 1, comprising calculating one or more global scores for imaging performance based on the distribution of the imaging performance values.

7. (a) Based on one or more global scores, penalizing or rewarding the non-uniformity of the imaging performance values ​​across a range of field coordinates (x, y) in the object plane within the test target, or through a range of focal positions (z) in the object plane, or both thereof. (b) Penalizing or rewarding similarity of different imaging channels based on one or more global scores as a function of the field coordinates (x, y) in the object plane within the test target, or as a function of the focal position (z) of the object plane, or both thereof, wherein the different imaging channels correspond to different imaging devices of the optical system operating to acquire the image, or to different wavelengths of the image acquired by the imaging device. The method according to claim 6, comprising at least one of the above.

8. The method according to claim 1, wherein positioning the test target includes aligning the test target with respect to a datum shared with one or more optical components of the optical system.

9. The method according to claim 1, wherein operating the optical system includes utilizing an objective lens in the image beam, and further includes adjusting the position of the objective lens along the axis of the image beam to acquire a plurality of images of the test target at a plurality of focal positions (z).

10. The method according to claim 1, wherein operating the optical system includes utilizing an objective lens in the image beam, and further includes adjusting the position of the objective lens along the axis of the image beam to acquire a plurality of images of the test target at a plurality of focal positions (z), wherein the objective lens has a configuration selected from the group consisting of the objective lens being configured for an infinite conjugate microscope and the objective lens being configured for a finite conjugate microscope.

11. The method according to claim 1, wherein operating the optical system includes operating two or more imaging devices to acquire images of each of the test targets.

12. The method according to claim 1, wherein operating the optical system includes operating two or more imaging devices to acquire each image of the test target, the two or more imaging devices including acquiring each image at two or more different wavelengths.

13. The method according to claim 1, wherein operating the optical system includes operating two or more imaging devices to acquire each image of the test target, the two or more imaging devices further include acquiring each image at two or more different wavelengths, dividing 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.

14. The method according to claim 1, wherein operating the optical system includes operating a filter assembly to filter the image beam at a selected wavelength.

15. The method according to claim 1, wherein operating the optical system includes utilizing a tube lens in the image beam, and further includes adjusting the relative positions of one or more lenses or lens groups within the tube lens to acquire a plurality of images of the test target at different positions of the tube lens.

16. The method according to claim 1, wherein the test target comprises a dark material and an array of bright properties arranged on the dark material.

17. The method according to claim 1, wherein the test target comprises a dark material and an array of bright properties arranged on the dark material, the bright properties being polygonal.

18. The method according to claim 1, wherein the test target comprises a dark material and an array of bright features arranged on the dark material, the bright features being polygonal, and the bright features being inclined such that the edges of the bright features are oriented at an angle with respect to the pixel array of the optical system that acquires the image.

19. An optical imaging performance test system, A target holder configured to hold a test target, A light source configured to illuminate the aforementioned test target, An imaging device configured to acquire an image of the aforementioned test target, An objective lens positioned in the imaging optical path between the test target and the imaging device, wherein at least one position of the objective lens or the target holder is adjustable along the imaging optical path. A controller comprising an electronic processor and memory, configured to control the following actions according to the method of claim 1: calculating the edge spread function, constructing the point spread function model, calculating the plurality of imaging performance values, generating an imaging performance map based on the distribution of the imaging performance values, and optimizing the alignment or focusing of the optical system based on the map; An optical imaging performance testing system equipped with [specific features / features].

20. The system according to claim 19, wherein the objective lens has a configuration selected from the group consisting of the objective lens being configured for an infinite conjugate microscope and the objective lens being configured for a finite conjugate microscope.

21. The system according to claim 19, wherein the imaging device comprises a plurality of imaging devices, and each imaging device further comprises an image separation mirror configured to divide the imaging optical path into a plurality of imaging optical paths, each directed toward the imaging device.

22. The system according to claim 19, comprising a filter assembly configured to select the wavelength of an image beam in the imaging optical path for propagation to the imaging device.

23. The system according to claim 19, comprising a tube lens positioned in the imaging optical path, wherein the relative positions of one or more lenses or lens groups within the tube lens are adjustable.

24. The system according to claim 19, wherein the test target comprises a dark material and an array of bright properties arranged on the dark material.

25. The system according to claim 19, wherein the test target comprises a dark material and an array of bright properties arranged on the dark material, the bright properties being polygonal.

26. The system according to claim 19, wherein the test target comprises a dark material and an array of bright features arranged on the dark material, the bright features being polygonal, and the bright features being inclined such that the edges of the bright features are oriented at an angle with respect to the pixel array of the optical system that acquires the image.

27. A non-temporary computer-readable medium recording a program for causing a processor to perform the following actions of the method according to claim 1: calculating the edge spread function, constructing the point spread function model, calculating the plurality of imaging performance values, generating an imaging performance map based on the distribution of the imaging performance values, and optimizing the alignment or focusing of the optical system based on the map.

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