Visual system compensation method and device in high temperature environment and storage medium

Abstract

The present application relates to the field of chip production, and mainly relates to a visual system compensation method and device under high-temperature environment and a storage medium, the method comprises the following steps: S1: acquiring a visual system input image under high-temperature environment; S2: detecting the image through an image processing algorithm; S3: compensating the detected visual distortion; S4: post-processing the compensated image and outputting a final image. The present application detects the visual distortion caused by high-temperature effect in the image through the image processing algorithm, and references a compensation model and trains the model based on the visual characteristic data under high-temperature environment, so as to compensate the thermal deformation and optical distortion of the optical system caused by high temperature, and the device offset and alignment error, so that the visual system can realize the effective method of image quality compensation, optical deformation correction and alignment error under high-temperature environment.

Description

Technical Field

[0001] This invention relates to the field of chip manufacturing, and mainly to a method, device and storage medium for vision system compensation under high temperature environment. Background Technology

[0002] With the continuous advancement of semiconductor technology, the demand for image processing technology in chip manufacturing is increasing, especially in the precision inspection and quality control stages of chip production, where vision systems have become an indispensable tool. Vision inspection systems can monitor and analyze various defects in the chip manufacturing process in real time, such as wafer surface defects, mask alignment errors, and interlayer alignment problems, ensuring product accuracy and yield.

[0003] However, many stages of the chip manufacturing process, especially those involving high-temperature environments, pose significant challenges to vision systems. Chip manufacturing typically involves high-temperature thermal processing (such as annealing, soldering, chemical vapor deposition, and bonding), which exposes production equipment, sensors, and optical components to high temperatures, leading to a series of problems:

[0004] 1. Thermal deformation and optical distortion of optical systems: High temperatures can cause thermal expansion of lenses, optical elements, and other critical components, resulting in distortion or changes in focal length of the optical system. Especially in micron-level chip manufacturing, even tiny imaging distortions can lead to failure or misjudgment in defect detection.

[0005] 2. Temperature-induced equipment misalignment and alignment errors: In high-temperature environments, mechanical components of chip manufacturing equipment may shift or deform due to thermal expansion. This misalignment can lead to alignment errors in the vision system, causing the chip features in the image to deviate from their actual positions, thus affecting the accuracy of automated inspection systems.

[0006] Most existing high-temperature visual compensation technologies rely on hardware improvements, such as using high-temperature stable optical materials, improving sensor design and heat dissipation systems, or reducing ambient temperature by adding cooling systems. However, these methods still cannot completely solve the effects of image distortion, thermal deformation, and temperature inhomogeneity at high temperatures.

[0007] Therefore, how to address the impact of high-temperature environments on vision systems during chip manufacturing has become a pressing technical challenge. An effective method for image quality compensation, optical distortion correction, and alignment error correction under high-temperature conditions could not only improve chip manufacturing yield and precision but also reduce scrap rates, thereby increasing production efficiency and cost-effectiveness. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention proposes a vision system compensation method for high-temperature environments. This method can effectively and in real-time address issues such as image degradation, optical distortion, and equipment errors caused by high temperatures, ensuring high-precision detection capabilities even in high-temperature production environments. This significantly improves the level of automated quality control in chip manufacturing processes. The specific technical solution is as follows:

[0009] A method for compensating a visual system under high-temperature conditions, characterized by comprising the following steps:

[0010] S1: Acquire the input image of the vision system under high temperature conditions;

[0011] S2: Detect visual distortion in images caused by high-temperature effects using image processing algorithms;

[0012] S3: A pre-trained compensation model is used to compensate for the detected visual distortion. The compensation model is trained based on visual characteristic data under high temperature conditions.

[0013] S4: Post-process the compensated image and output the final image.

[0014] Preferably, the image processing algorithm includes any one or more of the following methods: ray tracing and refraction simulation, edge detection and deformation analysis, image registration algorithm, geometric transformation detection method, frequency domain analysis method, and image quality assessment method, to detect image distortion caused by high temperature effect.

[0015] Preferably, visual distortion includes image color deviation, image distortion, and image blurring.

[0016] Preferably, in S3, the training method of the compensation model includes the following steps:

[0017] S3.1 Collect image data under different high temperature environments, including the correlation information between temperature and visual distortion;

[0018] S3.2 Select a deep learning method to simulate and compensate for visual distortion;

[0019] S3.3. Use the collected high-temperature data to train the compensation model, so that the compensation model can learn to recognize and correct visual distortion, and obtain a trained compensation model.

[0020] S3.4 Apply the trained compensation model to real-time image data to automatically correct image distortion.

[0021] Preferably, in S4, the compensated image is post-processed. The specific processing methods include any one or a combination of methods such as noise removal, contrast adjustment, color correction, sharpening, cropping and edge repair, and format conversion.

[0022] Preferably, the present invention further includes the following steps:

[0023] S5. Based on the temperature distribution in different regions of the input image, dynamically adjust the compensation model parameters to adapt to visual distortion under different temperature conditions.

[0024] The present invention also provides an apparatus for applying the above-mentioned visual system compensation method under high temperature environment, comprising:

[0025] The input module is used to acquire input images for the vision system in high-temperature environments;

[0026] The processing module is used to execute image processing algorithms and detect visual distortion caused by high temperature effects;

[0027] The compensation module is used to compensate for detected visual distortions using a pre-trained compensation model.

[0028] The output module is used to output the final compensated image.

[0029] Preferably, the device also includes a temperature sensor for real-time monitoring of ambient temperature and dynamic adjustment of compensation model parameters.

[0030] Preferably, the processing module includes an image filter, an edge detection module, and a contrast enhancement module to improve the distortion detection accuracy of the image.

[0031] The present invention also provides a computer storage medium, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the above-described vision system compensation method under high temperature conditions.

[0032] Compared with the prior art, the visual system compensation method under high temperature environment described in this invention has the following characteristics:

[0033] 1. The vision system compensation method under high temperature environment described in this invention uses an image processing algorithm to detect visual distortion in the image caused by the high temperature effect, and uses a compensation model and trains the model based on visual characteristic data under high temperature environment to compensate for thermal deformation and optical distortion of the optical system, as well as equipment offset and alignment error caused by high temperature. This enables the vision system to achieve effective image quality compensation, correction of optical deformation and alignment error under high temperature environment. It can not only improve the yield and accuracy of chip manufacturing, but also reduce the scrap rate in the production process, and improve production efficiency and cost-effectiveness.

[0034] 2. The visual system compensation method under high temperature environment described in this invention specifically combines one or more of the following methods: ray tracing and refraction simulation, edge detection and deformation analysis, image registration algorithm, geometric transformation detection method, frequency domain analysis method, and image quality assessment method, in order to better detect image distortion caused by high temperature effect.

[0035] 3. The visual system compensation method under high temperature environment described in this invention performs post-processing after image compensation. The specific processing methods include any one or more of the following: noise removal, contrast adjustment, color correction, sharpening, cropping and edge repair, and format conversion, in order to improve the accuracy of the image. Attached Figure Description

[0036] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0037] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0040] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0041] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0042] Please see Figure 1 A method for compensating the visual system under high temperature conditions, characterized by comprising the following steps:

[0043] S1: Acquire the input image of the vision system under high temperature conditions;

[0044] S2: Detect visual distortion in images caused by high-temperature effects using image processing algorithms;

[0045] S3: A pre-trained compensation model is used to compensate for the detected visual distortion. The compensation model is trained based on visual characteristic data under high temperature conditions.

[0046] S4: Post-process the compensated image and output the final image.

[0047] The vision system comprises an acquisition system and a computer system. The acquisition system can be a high-speed camera, intelligent monitor, etc. The images acquired by the vision system are fed into the computer system, which uses image processing algorithms to detect visual distortions caused by high-temperature effects. Specifically, visual distortions include color deviation, image falsification, and image blurring. Subsequently, a pre-trained compensation model is used to compensate for the detected visual distortions. This compensation model is trained based on visual characteristic data under high-temperature conditions to obtain a compensated image. The compensated image is then post-processed to output the final image.

[0048] Furthermore, the image processing algorithm includes the following specific schemes:

[0049] (1) Thermal effects and refraction model;

[0050] High-temperature effects are often caused by changes in refractive index due to differences in air temperature, especially near heat sources, where temperature gradients lead to light refraction. Physically based detection methods can identify distortions by simulating and analyzing refractive effects in images. Refraction effects can cause distortion, ghosting, or blurring of object edges in images. To detect these distortions, the following methods can be used:

[0051] (1.1) Ray tracing and refraction simulation: Using light refraction caused by a layer of hot air to simulate the distortion of objects in an image, thereby predicting and identifying visual distortion caused by changes in temperature gradient.

[0052] (1.2) Edge detection and deformation analysis: Distortion in an image can affect the edges and shape of an object. Edge detection algorithms (such as Canny, Sobel, etc.) can be used to extract the edges in an image and analyze whether there are irregular twists or deformations.

[0053] (2) Image distortion-based detection method;

[0054] High-temperature effects can cause visual distortions, leading to changes in the shape or position of objects in an image. Common image distortion detection methods include:

[0055] (2.1) Image registration: The original image is aligned with the ideal or reference image using an image registration algorithm (such as a feature-point-based registration method). If the image is distorted due to thermal effects, the registration process will reveal the shape differences.

[0056] (2.2) Geometric Distortion Detection: Geometric transformation detection methods (such as affine transformation, perspective transformation, etc.) are used to analyze the image. By estimating the geometric shape changes of objects in the image, distortion caused by temperature effects can be detected.

[0057] (3) Frequency domain analysis;

[0058] Visual distortions caused by high-temperature effects often exhibit certain frequency characteristics. Frequency domain analysis can help detect these distortion features. Common frequency domain methods include:

[0059] (3.1) Fourier Transform: By performing a Fourier transform on an image, the image can be converted from the spatial domain to the frequency domain. In the frequency domain, distortion caused by thermal effects manifests as high-frequency or low-frequency noise. By analyzing the spectrum, it is possible to detect whether distortion caused by thermal effects exists in the image.

[0060] (3.2) Wavelet transform: Wavelet transform can be used for multi-scale analysis. By analyzing the frequency components at different scales, it can effectively identify local distortions or deformations caused by high-temperature effects in images.

[0061] (4) Image quality assessment

[0062] Since high temperatures can cause images to become blurry or lose detail, some common image quality assessment methods can be used to quantify the visual distortion of an image:

[0063] (4.1) Structural Similarity Index (SSIM): SSIM is used to measure the structural similarity of images and is suitable for detecting structural distortions caused by high-temperature effects. If the SSIM value between the original image and the image affected by high temperature decreases significantly, it indicates that the image has been distorted.

[0064] (4.2) Peak Signal-to-Noise Ratio (PSNR): PSNR is used to measure image quality. A lower PSNR value indicates poorer image quality. If an image is distorted due to high temperature effects, the PSNR value will be low, which may manifest as image blurring or decreased contrast.

[0065] (5) Deep learning methods

[0066] With the widespread application of deep learning, methods based on convolutional neural networks (CNNs) can automatically learn distortion features in images. A CNN can be trained to identify distortions caused by high-temperature effects. The steps of deep learning methods include:

[0067] (5.1) Data collection and annotation: Collect image datasets containing visual distortions caused by thermal effects and annotate the distorted regions.

[0068] (5.2) Network Training and Recognition: A deep neural network (such as CNN or U-Net) is used to train the model to identify distortions caused by thermal effects in the image. The advantage of this method is that it can automatically identify complex thermal effect features without requiring the manual design of specific image processing algorithms.

[0069] (6) Image restoration based on fluid dynamics simulation

[0070] In some cases, visual distortions caused by thermal effects can be recovered by simulating fluid dynamics and temperature gradients. For example, using a fluid simulation model based on the Navier-Stokes equations, the effect of temperature changes in the air on light propagation can be simulated to correct the image.

[0071] (7) Local region analysis

[0072] Since distortion caused by high-temperature effects is limited to local areas in the image (such as near the heat source or areas with large temperature differences), the impact of thermal effects can be identified by analyzing these local areas. For example:

[0073] (7.1) Local contrast analysis: By analyzing the contrast changes in local areas of the image, visual distortion caused by high temperature effect can be detected.

[0074] (7.2) Local geometric feature analysis: By analyzing the changes in local shape features (such as circles, straight lines, etc.), we can determine whether there is geometric deformation due to thermal effects.

[0075] This embodiment allows for the selection and combination of one or more of the above-mentioned schemes, depending on the specific circumstances, to effectively detect and analyze visual distortions caused by high-temperature effects. Image registration, frequency domain analysis, and deep learning methods can be used to identify the specific type and location of the distortion. For specific application scenarios, it may be necessary to combine physical models with image processing techniques for joint analysis to obtain more accurate detection results.

[0076] Furthermore, the training method for the compensation model includes the following steps:

[0077] S3.1 Collect image data under different high temperature environments, including the correlation information between temperature and visual distortion;

[0078] S3.2 Select a deep learning method to simulate and compensate for visual distortion;

[0079] S3.3. Use the collected high-temperature data to train the compensation model, so that the compensation model can learn to recognize and correct visual distortion, and obtain a trained compensation model.

[0080] S3.4 Apply the trained compensation model to real-time image data to automatically correct image distortion.

[0081] The deep learning methods mentioned above include Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). Both CNNs and GANs are very powerful models in the field of image processing and can be used to train on visually distorted images. These two network architectures can be used to repair or enhance the quality of distorted images (e.g., image distortion removal, image super-resolution, image inpainting, etc.). The following sections describe how to train CNNs and GANs on visually distorted images:

[0082] Convolutional Neural Networks (CNNs) are used for three tasks: denoising, distortion correction, and image inpainting. Denoising is the process of recovering a clean image from a noisy image; distortion correction is the process of correcting image distortion caused by optical distortion (such as lens distortion) or geometric transformations; and image inpainting is the process of correcting missing or damaged image regions.

[0083] Convolutional Neural Networks (CNNs) consist of multiple convolutional and pooling layers, allowing them to extract useful features from the input image. When dealing with visual distortion, the network aims to learn how to recover the original sharp image using these features. For distortion correction tasks, the U-Net architecture can be used in CNNs. U-Net is well-suited for image inpainting and denoising because it has an encoder-decoder structure that captures both global and local features of the image.

[0084] Convolutional Neural Networks (CNNs) utilize loss functions: a commonly used loss function is the Mean Squared Error (MSE), which aims to minimize the difference between the generated image and the target image. For more complex tasks (such as image denoising), perceptual loss can also be used to improve visual quality.

[0085] The optimizer of a Convolutional Neural Network (CNN) uses common optimization algorithms, such as the Adam optimizer, to adjust the network weights.

[0086] The training process is as follows:

[0087] Given a distorted image as input, the network extracts features through convolutional layers and generates a predicted sharp image. The network parameters are then updated by calculating a loss function until the generated image is sufficiently close to the real sharp image.

[0088] Generative Adversarial Networks (GANs) consist of a generator and a discriminator, and improve the quality of generated images through adversarial training. GANs are particularly well-suited for generating realistic images and can be used to solve tasks such as image distortion correction and image restoration. The generator's task is to generate a result that is as close as possible to a real, clear image from a distorted image. It is composed of a Convolutional Neural Network (CNN), taking the distorted image as input and outputting the restored image. The discriminator's task is to determine whether an image is a real image (from the dataset) or a fake image generated by the generator. It is also a CNN, outputting a probability value representing the probability that the input image is a real image. Through adversarial training, the generator continuously improves to fool the discriminator, making the generated images increasingly realistic.

[0089] In Generative Adversarial Networks (GANs), the discriminator and generator compete against each other. The generator aims to "deceive" the discriminator into producing realistic images, while the discriminator tries to distinguish between real and fake images. Furthermore, additional reconstruction losses are used to help the generator more accurately recover image details. For example, L1 loss can help generate images that are as close to real images as possible. To improve the visual quality of the images, a perceptual loss function can be introduced, which optimizes the image's structure and texture by comparing high-level convolutional features (rather than pixel-level errors).

[0090] The generator can be trained by minimizing the difference between the generated image and the real image, and the generator will continuously improve its output; while the discriminator can be trained by maximizing its ability to distinguish between real and fake images.

[0091] Generative Adversarial Networks (GANs) can generate more realistic and detailed images, making them particularly suitable for image restoration tasks. Through adversarial training, they can effectively reduce the blurring problem that may be encountered in traditional Convolutional Neural Networks (CNNs), resulting in higher quality generated images.

[0092] Furthermore, in S4, the compensated image undergoes post-processing, which mainly includes the following aspects:

[0093] (1) Noise removal: Remove noise that may be introduced during the compensation process by filtering (such as Gaussian filtering, median filtering, etc.).

[0094] (2) Contrast adjustment: Adjust the contrast of the image as needed to improve the visibility of details.

[0095] (3) Color correction: If color changes are involved in the compensation process, color mapping or white balance correction can be used to make the image colors more realistic.

[0096] (4) Sharpening: Enhance image details through sharpening algorithms (such as Laplacian filtering, Unsharp Mask).

[0097] (5) Cropping and edge repair: If compensation causes image edge distortion or black borders, the image can be cropped or edge repair can be performed.

[0098] (6) Format conversion: Save the image in the desired file format, such as JPEG, PNG, etc.

[0099] To address the issues of uneven and unstable temperatures in different parts of the environment, this invention further includes the following steps for dynamically adjusting the compensation model parameters:

[0100] S5. Based on the temperature distribution in different regions of the input image, dynamically adjust the compensation model parameters to adapt to visual distortion under different temperature conditions.

[0101] The present invention also provides an apparatus for applying the above-mentioned visual system compensation method under high temperature environment, comprising:

[0102] The input module is used to acquire input images for the vision system in high-temperature environments;

[0103] The processing module is used to execute image processing algorithms and detect visual distortion caused by high temperature effects;

[0104] The compensation module is used to compensate for detected visual distortions using a pre-trained compensation model.

[0105] The output module is used to output the final compensated image.

[0106] This device allows for the application of the aforementioned vision system compensation method under high-temperature conditions, which can not only improve the yield and precision of chip manufacturing, but also reduce the scrap rate during the production process, thereby increasing production efficiency and cost-effectiveness.

[0107] Furthermore, to address uneven or unstable temperatures in different parts of the environment, the device also includes a temperature sensor for real-time monitoring of the ambient temperature and dynamic adjustment of the compensation model parameters.

[0108] Furthermore, the processing module includes an image filter, an edge detection module, and a contrast enhancement module to improve the accuracy of image distortion detection.

[0109] The present invention also provides a computer storage medium, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the above-described vision system compensation method under high temperature conditions.

[0110] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0111] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0112] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0113] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for compensating a visual system under high-temperature conditions, characterized in that, Includes the following steps: S1: Acquire the input image of the vision system under high temperature conditions; S2: Detect visual distortion in an image caused by high temperature effect using an image processing algorithm. The image processing algorithm includes any one or more of the following methods: ray tracing and refraction simulation, edge detection and deformation analysis, image registration algorithm, geometric transformation detection method, frequency domain analysis method, and image quality assessment method, in order to detect image distortion caused by high temperature effect. S3: A pre-trained compensation model is used to compensate for the detected visual distortion. The compensation model is trained based on visual characteristic data under high temperature conditions. The training method of the compensation model includes: collecting image data under different high-temperature environments, including the correlation information between temperature and visual distortion; selecting a deep learning method to simulate and compensate for visual distortion; using the collected high-temperature data to train the compensation model, so that the compensation model learns to recognize and correct visual distortion, and obtains a trained compensation model; applying the trained compensation model to real-time image data to automatically correct image distortion. S4: Post-process the compensated image and output the final image. The post-processing includes any one or more of the following methods: noise removal, contrast adjustment, color correction, sharpening, cropping and edge repair, and format conversion. S5: Based on the temperature distribution in different regions of the input image, dynamically adjust the parameters of the compensation model to adapt to visual distortion under different temperature conditions.

2. The visual system compensation method under high temperature environment according to claim 1, characterized in that, Visual distortion includes image color deviation, image distortion, and image blurring.

3. The visual system compensation method under high temperature environment according to claim 1, characterized in that, In S4, the compensated image is post-processed. Specific processing methods include noise removal, contrast adjustment, color correction, sharpening, cropping and edge repair, and format conversion, or a combination of any one or more of these methods.

4. A vision system compensation device for high-temperature environments, characterized in that, The method for compensating a visual system under high-temperature conditions, as described in any one of claims 1-3, comprises: The input module is used to acquire input images for the vision system in high-temperature environments; The processing module is used to execute image processing algorithms and detect visual distortion caused by high temperature effects; The compensation module is used to compensate for detected visual distortions using a pre-trained compensation model. The output module is used to output the final compensated image. The adjustment module is used to dynamically adjust the parameters of the compensation model based on the temperature distribution in different regions of the input image.

5. The vision system compensation device under high temperature conditions according to claim 4, characterized in that, It also includes a temperature sensor for real-time monitoring of ambient temperature and dynamic adjustment of compensation model parameters.

6. The vision system compensation device under high temperature environment according to claim 4, characterized in that, The processing module includes an image filter, an edge detection module, and a contrast enhancement module to improve the accuracy of image distortion detection.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the vision system compensation method under high temperature conditions as described in any one of claims 1 to 3.

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