CN109164591B - Computer-aided microscope objective lens assembling and adjusting method - Google Patents
Computer-aided microscope objective lens assembling and adjusting method Download PDFInfo
- Publication number
- CN109164591B CN109164591B CN201810907506.9A CN201810907506A CN109164591B CN 109164591 B CN109164591 B CN 109164591B CN 201810907506 A CN201810907506 A CN 201810907506A CN 109164591 B CN109164591 B CN 109164591B
- Authority
- CN
- China
- Prior art keywords
- objective lens
- wave aberration
- objective
- actual
- neural network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G02—OPTICS
- G02B—OPTICAL ELEMENTS, SYSTEMS OR APPARATUS
- G02B27/00—Optical systems or apparatus not provided for by any of the groups G02B1/00 - G02B26/00, G02B30/00
- G02B27/62—Optical apparatus specially adapted for adjusting optical elements during the assembly of optical systems
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B9/00—Measuring instruments characterised by the use of optical techniques
- G01B9/04—Measuring microscopes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Optics & Photonics (AREA)
- Testing Of Optical Devices Or Fibers (AREA)
Abstract
The invention relates to a computer-aided adjusting method for a microscope objective, which comprises the following steps: s1, establishing and training a multiple neural network regression model according to objective optical design software, and determining the corresponding relation between objective manufacturing assembly errors and objective wave aberration; s2, measuring the actual wave aberration of the objective lens, and if the actual wave aberration meets a preset threshold value, finishing the adjustment operation; s3, if the actual wave aberration is out of the preset threshold range, substituting the actual wave aberration into the multiple neural network regression model, and giving out an installation prediction error by the multiple neural network regression model; s4, adjusting the objective lens according to the debugging prediction error, measuring the actual wave aberration of the objective lens again, and ending the debugging operation if the obtained actual wave aberration meets the preset threshold; otherwise, repeating the steps S3-S4 until the actual wave aberration satisfies the predetermined threshold. The adjusting method can effectively improve the adjusting efficiency of the objective lens.
Description
Technical Field
The invention relates to the technical field of microscope assembly and adjustment, in particular to a computer-assisted assembly and adjustment method for a microscope objective.
Background
In the assembling process of the microscope objective, the adjustment is generally realized by repeating the process of qualitatively determining the aberration by observing star point images and then adjusting the optical system lens group for many times. And computer-aided debugging is a computer-aided debugging process: firstly, the aberration of the optical system is measured, then the adjustment amount of the optical element is calculated by a computer according to the aberration, and the adjustment amount is executed manually or by a machine.
The existing computer-aided debugging methods generally adopt a direct sensitivity matrix method and a Monte Carlo method. In the direct sensitivity method, a linear relationship is firstly assumed between the aberration a and the adjustment amount C, and then a sensitivity matrix M is obtained, according to which C is equal to M-1And A, obtaining an adjustment amount. Since the direct sensitivity matrix method is based on the linear relationship assumption, the linear assumption will be sensitiveThe degree matrix debugging method is limited in a small dynamic range and is small in application range.
The Monte Carlo method is used for generating some systems with random errors, and the limitation of linear approximation of the direct sensitivity matrix method can be broken through to a certain extent. This is effective for optical systems such as astronomical telescopes, which require a small number of adjustable lenses (or reflectors) (2-4 pieces). However, the number of lenses of the microscope objective optical system is large (for example, NA0.8, FOV phi 30 flat field apochromatic 20X objective can reach 7 groups of 13 lenses, which is much larger than the number of lenses of an astronomical telescope), the size of the space formed by each adjustment degree of freedom and the number of degrees of freedom are in an exponential relationship, and at this time, if the monte carlo method is adopted for adjustment, the required calculated amount and the calculated time cannot be realized. Taking 7 sets of 13-piece objectives as an example, only the components of decentration, tilt, lens thickness and air spacing together have 47 degrees of freedom, each degree of freedom using 3 samples, 3 total47sub-Monte Carlo analysis, calculating billions of times per second, requires more than 80 ten thousand years of calculation time.
Disclosure of Invention
The invention aims to provide a high-efficiency computer-aided microscope objective adjusting method.
In order to achieve the above object, the present invention provides a computer-aided adjusting method for microscope objective, comprising:
s1, establishing and training a multiple neural network regression model according to objective optical design software, and determining the corresponding relation between objective manufacturing assembly errors and objective wave aberration;
s2, measuring the actual wave aberration of the objective lens, and if the actual wave aberration meets a preset threshold value, finishing the adjustment operation;
s3, if the actual wave aberration is out of the preset threshold range, substituting the actual wave aberration into the multiple neural network regression model, and giving out an installation prediction error by the multiple neural network regression model;
s4, adjusting the objective lens according to the debugging prediction error, measuring the actual wave aberration of the objective lens again, and ending the debugging operation if the obtained actual wave aberration meets the preset threshold; otherwise, repeating the steps S3-S4 until the actual wave aberration satisfies the predetermined threshold.
According to an aspect of the present invention, the step S1 includes:
s11, establishing an objective lens model in objective lens optical design software, introducing a plurality of random manufacturing assembly errors, and simulating a plurality of corresponding wave aberrations by the objective lens optical design software;
s12, establishing a multiple neural network regression model, training the multiple neural network regression model by taking the plurality of manufacturing assembly errors and the plurality of wave aberration as training data, and determining the corresponding relation between the manufacturing assembly errors and the wave aberration.
According to an aspect of the present invention, in the step S12, the manufacturing assembly error is used as a mark feature of training data, and the wave aberration is used as an attribute feature of the training data.
According to an aspect of the present invention, in the step S3, the actual wave aberration group of the objective lens is input as the attribute feature to the multiple neural network regression model, and the fitting prediction error is given as the mark feature by the multiple neural network regression model according to the obtained relationship between the manufacturing fitting error and the wave aberration.
According to an aspect of the present invention, the step S2 includes:
s21, arranging a beam reducing mirror and a wavefront aberration measuring device on the image side beam path of the microscope objective lens, and measuring the actual wavefront aberration of the objective lens;
and S22, preprocessing the measured actual wave aberration, and filtering noise in the actual wave aberration.
According to an aspect of the present invention, a diameter of the beam passing through the beam reduction mirror is equal to a diameter of the wavefront aberration measuring device.
According to one aspect of the invention, the wavefront aberration measuring device is an interferometer or a wavefront sensor.
According to an aspect of the present invention, the step S4 includes:
s41, inputting the debugging prediction error into the objective optical design software to obtain an actual objective model;
s42, analyzing the sensitivity of each component of the objective lens, and setting one or more compensation groups according to the tolerance sensitivity of each component;
s43, setting the compensation vectors of the compensation groups as variables, and optimizing the compensation vectors by taking the imaging performance of the objective lens as an evaluation function to obtain optimal compensation vectors;
s44, adjusting the objective lens by taking the optimal compensation vector as an objective lens adjusting quantity, and measuring the actual wave aberration of the objective lens again after adjustment;
and S45, judging whether the actual wave aberration meets the preset threshold value, if so, finishing the adjusting operation, otherwise, repeating the steps S3-S4 until the actual wave aberration of the objective lens meets the preset threshold value.
According to one aspect of the invention, the compensation vector includes an air gap before and after the objective lens and an eccentricity of the objective lens.
According to one scheme of the invention, the computer-aided microscope objective lens debugging method firstly establishes and trains a multiple neural network regression model, predicts the manufacturing and assembling errors of the actual objective lens through the multiple neural network regression model during the specific debugging, gives the optimal assembling adjustment amount through analysis, and measures the wave aberration again after the debugging is carried out so as to determine whether the objective lens is qualified or not and finish the debugging. The method breaks through the limitation of linear assumption of a direct sensitivity matrix method in the prior art, and can also solve the problem of huge calculation amount caused by using a Monte Carlo method, so that the method has stronger applicability, quicker installation and adjustment of the objective lens and greatly improved efficiency.
In addition, the microscope objective calculation auxiliary debugging method puts the training of the multiple neural network regression model with large calculated amount in the preprocessing stage before the objective assembling process, directly gives the assembling prediction error according to the received actual wave aberration of the objective during assembling and debugging by the trained multiple neural network regression model, has small calculated amount in the assembling and debugging process, and can improve the assembling and debugging efficiency.
Drawings
Fig. 1 is a flow chart schematically representing a computer-aided setup method of a microscope objective according to the present invention.
FIG. 2 is a flow diagram schematically illustrating a method of building and training a multiple neural network regression model;
FIG. 3 is a flow chart that schematically illustrates a method for tuning an objective lens according to a multiple neural network regression model.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
In describing embodiments of the present invention, the terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in an orientation or positional relationship that is based on the orientation or positional relationship shown in the associated drawings, which is for convenience and simplicity of description only, and does not indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and thus, the above-described terms should not be construed as limiting the present invention.
The present invention is described in detail below with reference to the drawings and the specific embodiments, which are not repeated herein, but the embodiments of the present invention are not limited to the following embodiments.
Referring to fig. 1, 2 and 3, the present invention provides a computer-aided adjusting method for microscope objective, including: s1, establishing and training a multiple neural network regression model according to objective optical design software, and determining the corresponding relation between objective manufacturing assembly errors and objective wave aberration; s2, measuring the actual wave aberration of the objective lens, and if the actual wave aberration meets a preset threshold value, finishing the adjustment operation; s3, if the actual wave aberration is out of the preset threshold range, substituting the actual wave aberration into the multiple neural network regression model, and giving the debugging prediction error by the multiple neural network regression model; s4, adjusting the objective lens according to the installation prediction error, measuring the actual wave aberration of the objective lens again, and ending the installation operation if the obtained actual wave aberration meets the preset threshold; otherwise, steps S3-S4 are repeated until the actual wave aberration satisfies the predetermined threshold.
Namely, the computer-aided adjusting method of the microscope objective lens can be divided into a preprocessing stage, an actual measuring stage and an actual objective lens adjusting stage. In the preprocessing stage, step S1, a multiple neural network regression model needs to be established to obtain the corresponding relationship between the objective lens manufacturing assembly error (machining assembly error) and the wavefront aberration.
As shown in fig. 1 and fig. 2, in step S1, the method specifically includes the following steps: firstly, an objective lens model is built in objective lens optical design software, then a plurality of random manufacturing assembly errors are introduced into the built objective lens model, and the objective lens optical design software simulates the corresponding wave aberration after the manufacturing assembly errors are introduced. Then, a multiple neural network regression model is established, the multiple neural network regression model is trained by using the multiple manufacturing assembly errors and the multiple wave aberrations as training data, namely, the multiple manufacturing assembly errors and the multiple wave aberrations are established in a one-to-one correspondence relationship, each set of corresponding manufacturing assembly errors and wave aberrations are used as a training sample to train the multiple neural network regression model, and the corresponding relationship between the manufacturing assembly errors and the wave aberrations is determined according to the training sample. In the training process, the wave aberration in the training sample is used as an attribute feature, and the manufacturing assembly error is used as a mark feature.
After the multiple neural network regression model is built and trained, step S2 is performed, i.e., the objective lens actual measurement phase. In step S2, the microscope stand is first modified to form a tool for measuring the wave aberration of the microscope objective. In the present embodiment, a specific modification is to remove the microscope tube lens from the frame, and install the beam reduction lens and the wavefront aberration measuring device on the image side beam path of the microscope objective lens. In this embodiment, the exit pupil beam of the objective lens is imaged on the detector of the wavefront sensor by the beam-shrinking mirror, and the diameter of the beam passing through the beam-shrinking mirror is ensured to be equal to the aperture of the wavefront sensor. And then, measuring the actual wave aberration of the objective lens through the wavefront sensor, preprocessing the measured actual wave aberration, and removing noise in the actual wave aberration value.
After the actual wave aberration of the objective lens is measured, it is necessary to determine whether the actual wave aberration of the objective lens meets the adjustment requirement, specifically, when a plurality of objective lenses of a certain microscope are adjusted, there is a certain requirement on the wave aberration of the objective lens according to different requirements on the adjustment precision, that is, in the actual operation process, there is a predetermined threshold value in the actual wave aberration of the objective lens, and it is necessary to compare the measured actual wave aberration of the objective lens with the predetermined threshold value of the wave aberration, and if the measured actual wave aberration of the objective lens is within the predetermined threshold value range, it is indicated that the assembly of the objective lens at this time meets the precision requirement, and it is not necessary to adjust. If the measured actual wave aberration of the objective lens is outside the predetermined threshold range, steps S3-S4, i.e. the actual objective lens setup phase, are required.
Referring to fig. 1 and 3, if the measured actual wave aberration of the objective lens is not within the predetermined threshold range, the measured actual wave aberration of the objective lens needs to be input into the multiple neural network regression model as an attribute feature, and the multiple neural network regression model provides a corresponding mark feature, that is, an adjustment prediction error of the objective lens, according to the obtained corresponding relationship between the manufacturing assembly error and the wave aberration.
Then inputting the predicted error of the adjustment of the objective lens into the objective lens optical design software, establishing an actual objective lens model, carrying out sensitivity analysis on each component of the objective lens, and setting one or more compensation groups according to the tolerance sensitivity of each component of the objective lens. And then, setting the compensation vectors of the compensation groups as variables, taking the imaging performance of the objective lens as an evaluation function, establishing a functional relation between the compensation vectors and the imaging performance of the objective lens, optimizing the compensation vectors by optimizing the imaging quality of the objective lens, and obtaining the optimal compensation vectors. And the optimal compensation vector is the required adjustment amount of the objective lens, and the objective lens is adjusted according to the adjustment amount. Finally, the adjusted objective lens needs to be measured again to obtain the actual wave aberration of the objective lens, and the actual wave aberration of the adjusted objective lens is ensured to be in the preset threshold range. If the actual wave aberration of the objective lens is still not within the predetermined threshold range after the objective lens is adjusted, repeating the steps S3-S4 to continue adjusting the objective lens until the actual wave aberration of the objective lens meets the requirement of the predetermined threshold.
As shown in fig. 1, the complete procedure of the computer-aided microscope objective adjusting method of the present invention is as follows:
establishing an objective lens model through objective lens optical design software, introducing a plurality of random manufacturing assembly errors, simulating a plurality of corresponding wave aberrations through the software, establishing a multiple neural network by using the plurality of manufacturing assembly errors and the plurality of wave aberrations as training data, and training to form a multiple neural network regression model. And then measuring the actual wave aberration of the objective lens, inputting the actual wave aberration of the objective lens into a multiple neural network regression model if the actual wave aberration does not meet a preset threshold value, giving the installation and adjustment prediction error of the objective lens by the multiple neural network regression model, transmitting the installation and adjustment prediction error to objective lens optical design software, and establishing an actual objective lens model. And then optimizing the compensation vector by optimizing the imaging quality of the objective lens to obtain the optimal compensation vector, and adjusting the objective lens by taking the final compensation vector as the adjustment quantity of the objective lens. And after adjustment, measuring the actual wave aberration of the objective lens again, finishing the adjustment operation if the actual wave aberration meets a preset threshold value, otherwise inputting the actual wave aberration of the objective lens into the multiple neural network regression model again, and repeating the operation.
The operation of setting one or more compensation groups according to the tolerance sensitivity of the objective lens components according to the concept of the present invention is not limited to the above-described manner, and may be performed before the multiple neural network regression model is established or trained. In addition, since the number of components of the objective lens is large, in the present embodiment, only the air gap before and after the objective lens and the eccentricity of the objective lens are used as adjustment amounts which are compensation vectors.
The computer-aided assembling and adjusting method for the microscope objective breaks through the limitation of linear assumption of a direct sensitivity matrix method in the prior art, and can also solve the problem of huge calculation amount caused by using a Monte Carlo method, so that the method has stronger applicability, more rapid assembling and adjusting of the objective and greatly improved efficiency.
In addition, the microscope objective calculation auxiliary debugging method puts the training of the multiple neural network regression model with large calculated amount in the preprocessing stage before the objective assembling process, directly gives the assembling prediction error according to the received actual wave aberration of the objective during assembling and debugging by the trained multiple neural network regression model, has small calculated amount in the assembling and debugging process, and can improve the assembling and debugging efficiency.
The foregoing is illustrative of specific embodiments of the present invention and reference should be made to the implementation of apparatus and structures not specifically described herein, which is understood to be a general purpose apparatus and method of operation known in the art.
The above description is only one embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A computer-aided microscope objective lens adjustment method, comprising:
s1, establishing and training a multiple neural network regression model according to objective optical design software, and determining the corresponding relation between objective manufacturing assembly errors and objective wave aberration;
s2, measuring the actual wave aberration of the objective lens, and if the actual wave aberration meets a preset threshold value, finishing the adjustment operation;
s3, if the actual wave aberration is out of the preset threshold range, substituting the actual wave aberration into the multiple neural network regression model, and giving out an installation prediction error by the multiple neural network regression model;
s4, inputting the debugging prediction error into the objective optical design software to obtain an actual objective model, then carrying out sensitivity analysis on each component of the objective, setting one or more compensation groups according to tolerance sensitivity of each component, setting compensation vectors of the compensation groups as variables, and optimizing the compensation vectors by taking the imaging performance of the objective as an evaluation function to obtain optimal compensation vectors; adjusting the objective lens by taking the optimal compensation vector as an objective lens adjusting amount, and measuring the actual wave aberration of the objective lens again after adjustment; and judging whether the actual wave aberration meets the preset threshold value, if so, finishing the debugging operation, otherwise, repeating the steps S3-S4 until the actual wave aberration of the objective lens meets the preset threshold value.
2. The computer-aided installation and adjustment method for microscope objective lenses according to claim 1, wherein the step S1 includes:
s11, establishing an objective lens model in objective lens optical design software, introducing a plurality of random manufacturing assembly errors, and simulating a plurality of corresponding wave aberrations by the objective lens optical design software;
s12, establishing a multiple neural network regression model, training the multiple neural network regression model by taking the plurality of manufacturing assembly errors and the plurality of wave aberration as training data, and determining the corresponding relation between the manufacturing assembly errors and the wave aberration.
3. The computer-aided setup method for microscope objective lens according to claim 2, wherein in step S12, the manufacturing assembly error is used as a mark feature of training data, and the wave aberration is used as an attribute feature of the training data.
4. The computer-aided setup method for microscope objective lens according to claim 3, wherein in step S3, the actual wave aberration of objective lens is inputted into the multiple neural network regression model as an attribute feature, and a setup prediction error is given by the multiple neural network regression model as a mark feature according to the obtained relationship between the manufacturing assembly error and the wave aberration.
5. The computer-aided installation and adjustment method for microscope objective lenses according to claim 1, wherein the step S2 includes:
s21, arranging a beam reducing mirror and a wavefront aberration measuring device on the image side beam path of the microscope objective lens, and measuring the actual wavefront aberration of the objective lens;
and S22, preprocessing the measured actual wave aberration, and filtering noise in the actual wave aberration.
6. A computer-aided setup method for a microscope objective according to claim 5, characterized in that the beam diameter passing through the beam reducer is equal to the aperture of the wavefront aberration measuring device.
7. Computer-aided adjustment method of a microscope objective according to claim 5 or 6, characterized in that the wavefront aberration measuring device is an interferometer or a wavefront sensor.
8. The computer-aided setup method for a microscope objective according to claim 1, wherein the compensation vector includes an air gap before and after the objective and an eccentricity of the objective.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810907506.9A CN109164591B (en) | 2018-08-10 | 2018-08-10 | Computer-aided microscope objective lens assembling and adjusting method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810907506.9A CN109164591B (en) | 2018-08-10 | 2018-08-10 | Computer-aided microscope objective lens assembling and adjusting method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109164591A CN109164591A (en) | 2019-01-08 |
| CN109164591B true CN109164591B (en) | 2021-01-05 |
Family
ID=64895505
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810907506.9A Active CN109164591B (en) | 2018-08-10 | 2018-08-10 | Computer-aided microscope objective lens assembling and adjusting method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109164591B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110231360B (en) * | 2019-05-31 | 2020-06-26 | 华南农业大学 | Method and device for determining and eliminating aberration of electron microscope |
| JP7438749B2 (en) * | 2019-12-26 | 2024-02-27 | キヤノン株式会社 | Optical system inspection methods, programs, and measuring devices |
| CN111488701B (en) * | 2020-06-28 | 2020-10-16 | 中国人民解放军国防科技大学 | Computer-aided debugging method, device and equipment based on point spread function |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101206294A (en) * | 2006-12-14 | 2008-06-25 | 富士胶片株式会社 | Eccentricity regulating method for lens optical system, eccentricity regulating device and program |
| CN101464637B (en) * | 2008-12-30 | 2011-03-30 | 上海微电子装备有限公司 | Measurement apparatus and method for wave aberration of photo-etching machine projection objective |
| DE102010051627A1 (en) * | 2010-11-17 | 2012-05-24 | Rodenstock Gmbh | Method for optimizing a spectacle lens with a diffractive grating |
| CN103984808A (en) * | 2014-04-30 | 2014-08-13 | 中国科学院长春光学精密机械与物理研究所 | Method for establishing computer-aided alignment model based on optical component surface shape |
| CN104036071B (en) * | 2014-05-21 | 2017-02-15 | 中国科学院西安光学精密机械研究所 | Computer-aided adjustment method for large-dynamic-range optical system |
| CN106646867B (en) * | 2016-12-16 | 2018-12-14 | 中国科学院光电研究院 | A kind of confocal alignment device of deep ultraviolet optical system and method |
-
2018
- 2018-08-10 CN CN201810907506.9A patent/CN109164591B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN109164591A (en) | 2019-01-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109164591B (en) | Computer-aided microscope objective lens assembling and adjusting method | |
| US9182289B2 (en) | Apparatus and method for estimating wavefront parameters | |
| US20110153248A1 (en) | Ophthalmic quality metric system | |
| CN110188321B (en) | Primary and secondary mirror calibration method based on neural network algorithm | |
| CN102252763B (en) | Single focal plane high-precision testing method for optical wavefront of optical imaging system | |
| JP2008170981A (en) | Eccentricity adjustment method, apparatus, and program for lens optical system | |
| CN102256535A (en) | Method and apparatus for performing remote calibration verification | |
| US10379387B2 (en) | Method and device for checking refractive power distribution and centering | |
| CN110795780A (en) | XGboost algorithm-based cable-stayed bridge finite element correction method | |
| CN110990987B (en) | Simulation method of optical remote sensing camera imaging full link | |
| CN111811785B (en) | Method, device, equipment and medium for detecting aberration of telescope with large dynamic range | |
| Pakuliak et al. | FON: From start to finish | |
| CN113435699A (en) | Intelligent quality control method and system | |
| CN116105604A (en) | Steel pipe quality detection system and detection method | |
| Angeli et al. | An integrated modeling framework for the Large Synoptic Survey Telescope (LSST) | |
| CN109002567B (en) | Computer-aided debugging method based on actual optical system debugging detection optical path | |
| EP2525188B1 (en) | Adjustment of an optical surface to be tested in a test device | |
| CN115421297B (en) | Method for determining optimal focal plane position of optical system | |
| Jolissaint et al. | Adaptive optics point spread function reconstruction project at WM Keck Observatory: first results with faint natural guide stars | |
| Weddell et al. | Reservoir computing for prediction of the spatially-variant point spread function | |
| DE102022207720A1 (en) | Method for predicting a deviation between a fit measured for an optical element in a measurement state and a fit to be expected in the operating state | |
| Weddell et al. | Wavefront prediction with reservoir computing for minimizing the effects of angular anisoplanatism | |
| CN111488701B (en) | Computer-aided debugging method, device and equipment based on point spread function | |
| EP3702839B1 (en) | Method of reducing effects of lens heating and/or cooling in a lithographic process | |
| CN111611764B (en) | Pupil evaluation method and system and electronic device thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |