Imaging element evaluation device and imaging element evaluation method

Abstract

Provided is a technology for quantitatively evaluating the result of a test.　The present technology provides an imaging element evaluation device comprising: a load generation unit that generates a load to be applied to an imaging element; and an evaluation unit that evaluates the load resistance of the imaging element on the basis of an image signal output by the imaging element under the load. The evaluation unit may evaluate the load resistance on the basis of the amount of noise included in the image signal and a predetermined threshold value. The amount of noise may include the pixel value of each pixel.

Description

Image sensor evaluation device and image sensor evaluation method

[0001] The technology according to the present disclosure (hereinafter also referred to as "the technology") relates to an imaging element evaluation device and an imaging element evaluation method.

[0002] Electrical and electronic devices may generate electromagnetic interference during operation, which can adversely affect other devices. Conversely, they may be affected by electromagnetic interference generated by other devices, causing them to malfunction.

[0003] A technology called EMC (Electromagnetic Compatibility) is used to evaluate electrical and electronic equipment. EMC describes the ability of electrical and electronic equipment to neither generate electromagnetic interference nor be affected by electromagnetic interference. EMC is classified into EMI (Electromagnetic Interference) and EMS (Electromagnetic Susceptibility). EMI refers to the electromagnetic energy emitted by equipment (interfering electromagnetic energy), while EMS refers to the possibility (immunity) of equipment malfunctioning due to the influence of external electromagnetic energy. EMI and EMS are further classified into conductive and radiative types based on the propagation path. Conductive refers to the propagation of electromagnetic energy via equipment circuit boards or cables, while radiative refers to the propagation of electromagnetic energy through space. Methods for evaluating these EMI and EMS characteristics are defined in international standards.

[0004] As an example of this evaluation method, Patent Document 1 discloses a technology relating to "a method for evaluating an electric circuit, characterized by comprising the steps of: determining a first malfunction frequency characteristic representing the magnitude of a noise signal at the limit at which a device under test including a target electric circuit will malfunction; and determining a second malfunction frequency characteristic representing the magnitude of a noise signal at the limit at which the target electric circuit will malfunction, based on the first malfunction frequency characteristic, an equivalent circuit of the device under test, and an equivalent circuit of the target electric circuit."

[0005] Japanese Patent Application Laid-Open No. 2015-1517

[0006] However, in conventional evaluation methods, the definition of malfunction is left up to the tester, and the load tolerance evaluation obtained from the test results is not uniform.

[0007] Therefore, a main object of the present technology is to provide an imaging element evaluation device and an imaging element evaluation method that quantitatively evaluate test results.

[0008] The present technology provides an imaging element evaluation device including: a load generation unit that generates a load to be applied to an imaging element; and an evaluation unit that evaluates the load tolerance of the imaging element based on an image signal output by the imaging element to which the load is applied. The evaluation unit may evaluate the load tolerance based on a noise amount included in the image signal and a predetermined threshold. The noise amount may include a pixel value of each pixel. The evaluation unit may evaluate the load tolerance based on at least one of an average value of the pixel values, a maximum value of the pixel values, a minimum value of the pixel values, a variance of the pixel values, and a difference between the pixel value of each pixel and the pixel values ​​of its neighboring pixels. The evaluation unit may classify the images into classes according to the load tolerance. The evaluation unit may classify the images into classes using a trained model that has been machine-learned to determine a correlation between the noise amount included in the image signal and a class corresponding to the noise amount. The imaging element evaluation device may be used for immunity testing of the imaging element. The imaging element evaluation device may be used for DPI method testing of the imaging element. The evaluation unit may evaluate the load tolerance of the image sensor based on the power frequency characteristics of the image sensor obtained by a DPI method test and the amount of noise contained in the image signal. The image signal may be at least one of a still image signal and a video signal, and the still image signal and the video signal may include at least one of a white image signal, a black image signal, a gray image signal, a geometric pattern image signal, and a video signal. The image sensor evaluation device may further include a control unit that controls driving of the image sensor, and the load may be applied to a signal transmission line that does not transmit the image signal among multiple signal transmission lines connecting the image sensor and the control unit. The image sensor evaluation device may further include a control unit that controls driving of the image sensor, and may further include a noise suppression unit that suppresses noise generated by injecting a noise signal into an input signal of the image sensor from propagating to the control unit. The noise suppression unit may be a GND via. The noise suppression unit may have a first ground pad connected to an input terminal of the image sensor and a second ground pad connected to an output terminal of the image sensor. The noise suppression unit may be a filter that blocks signals of a predetermined frequency.The filter may be at least one of a low-pass filter, a high-pass filter, a band-pass filter, and a band-stop filter. The present technology also provides an imaging element evaluation method including: generating a load to be applied to an imaging element; and evaluating the load tolerance of the imaging element based on an image signal output from the imaging element to which the load is applied.

[0009] According to the present technology, it is possible to provide an imaging element evaluation device and an imaging element evaluation method that quantitatively evaluate test results. Note that the effects described herein are not necessarily limited to those described herein and may be any of the effects described in the present disclosure.

[0010] 1 is a block diagram showing an example of a configuration of an imaging element evaluation device 100 according to an embodiment of the present technology. FIG. 1 is a flowchart showing an example of a flow of a DPI method test. FIG. 2 is a block diagram showing an example of a configuration of an imaging element evaluation device 100 according to an embodiment of the present technology. FIG. 2 is a flowchart showing an example of a flow of an imaging element evaluation device according to an embodiment of the present technology. FIG. 3 is a diagram showing an example of a screen displayed by an imaging element evaluation device 100 according to an embodiment of the present technology. FIG. 3 is a diagram showing an example of a screen displayed by an imaging element evaluation device 100 according to an embodiment of the present technology. FIG. 4 is a schematic diagram showing an example of a configuration of a test object 2 etc. according to an embodiment of the present technology. FIG. 4 is a schematic diagram showing an example of a configuration of an imaging element evaluation device 100 according to an embodiment of the present technology. FIG. 5 is a schematic diagram showing an example of a configuration of a noise suppression unit 25 according to an embodiment of the present technology. FIG. 5 is a schematic diagram showing an example of a configuration of a noise suppression unit 25 according to an embodiment of the present technology. FIG. 6 is a graph showing characteristics of a low-pass filter. FIG. 6 is a circuit diagram showing an example of a configuration of a low-pass filter. FIG. 7 is a graph showing characteristics of a high-pass filter. FIG. 7 is a circuit diagram showing an example of a configuration of a high-pass filter. Fig. 15A is a graph showing characteristics of a band-pass filter. Fig. 15B is a circuit diagram showing a configuration example of a band-pass filter. Fig. 16A is a graph showing characteristics of a band-stop filter. Fig. 16B is a circuit diagram showing a configuration example of a band-stop filter. Fig. 16 is a flowchart showing an example of the flow of an imaging element evaluation method according to an embodiment of the present technology.

[0011] Hereinafter, preferred embodiments for implementing the present technology will be described with reference to the drawings. Note that the embodiment described below shows an example of a typical embodiment of the present technology, and does not limit the scope of the present technology. In addition, the present technology can be combined with any of the following examples and their modifications.

[0012] In the following description of the embodiments, configurations may be described using terms including "approximately," such as "approximately parallel" and "approximately perpendicular." For example, "approximately parallel" does not only mean completely parallel, but also means substantially parallel, i.e., including a state where the orientation is deviated from the completely parallel state by, for example, a few percent. The same applies to other terms including "approximately." Furthermore, each figure is a schematic diagram and is not necessarily an accurate depiction. The scale of the drawings is exaggerated to make the features of the technology easier to understand. Therefore, it should be noted that the scale of the drawings and the scale of the actual device are not necessarily the same.

[0013] Unless otherwise specified, in the drawings, "top" means the top or upper side in the drawing, "bottom" means the bottom or lower side in the drawing, "left" means the left or left side in the drawing, and "right" means the right or right side in the drawing. Furthermore, in the drawings, the same or equivalent elements or members are given the same reference numerals, and redundant explanations will be omitted.

[0014] The description will be given in the following order: 1. First embodiment of the present technology (example 1 of image sensor evaluation device) (1) Overview (2) DPI method test (3) Configuration of image sensor evaluation device (4) Flow of image sensor evaluation device 2. Second embodiment of the present technology (example 2 of image sensor evaluation device) 3. Third embodiment of the present technology (example 3 of image sensor evaluation device) 4. Fourth embodiment of the present technology (example 4 of image sensor evaluation device) 5. Fifth embodiment of the present technology (example of image sensor evaluation method)

[0015] [1. First Embodiment of the Present Technology (Example 1 of Image Sensor Evaluation Device)] [(1) Overview] The present technology provides an image sensor evaluation device including: a load generation unit that generates a load to be applied to an image sensor; and an evaluation unit that evaluates the load tolerance of the image sensor based on an image signal output by the image sensor to which the load is applied.

[0016] An example configuration of an imaging element evaluation device according to an embodiment of the present technology will be described with reference to Fig. 1. Fig. 1 is a block diagram showing an example configuration of an imaging element evaluation device 100 according to an embodiment of the present technology. As shown in Fig. 1, the imaging element evaluation device 100 includes a load generation unit 1, a test object 2, and an evaluation unit 3.

[0017] The load generating unit 1 generates a load to be applied to an imaging element constituting a device under test 2. The present technology can be used in an immunity test of an imaging element, which evaluates the characteristics of the imaging element by applying a load to the imaging element. Specific examples of immunity tests include a human body model test (HBM test), a machine model test (MM test), a charged device model test (CDM test), a bulk current injection test (BCI test), a direct power injection test (DPI test), a whole body conducted immunity test (WBFC test), a transverse electromagnetic cell test (TEM-cell test), an IC stripline test, and an electrostatic test.

[0018] The HBM test is an electrostatic discharge test based on the human body model, which uses a circuit that mimics the human body model to apply an electrostatic discharge (ESD) pulse and observes any malfunction or damage to a semiconductor device.

[0019] The MM test is an electrostatic discharge test based on a mechanical model. The MM test is a test method that uses a circuit that mimics a mechanical model to apply an electrostatic discharge pulse and observes the malfunction or damage of a semiconductor device.

[0020] The CDM test is an electrostatic discharge test based on a charged device model. The CDM test is a test method that uses a circuit that mimics a charged device model to apply an electrostatic discharge pulse and observe the malfunction and damage of semiconductor elements.

[0021] The BCI method is an immunity test that uses a bulk current injected into a power line. The BCI method is a test method in which a bulk current is injected into a power line and the semiconductor device is observed for abnormal or malfunctioning behavior.

[0022] The DPI method is an immunity test in which a noise signal is directly injected into the interior of a semiconductor device. The DPI method is a test method in which a noise signal is directly injected into the interior of a semiconductor device and any abnormal or malfunctioning operation of the semiconductor device is observed.

[0023] The WBFC test is a conducted immunity test that irradiates electromagnetic waves and observes abnormal or malfunctioning behavior of systems and equipment.

[0024] TEM-cell testing is a radiated immunity test that uses electromagnetic waves. TEM-cell testing is a testing method that uses a test device called a TEM cell to irradiate electromagnetic waves and observe any abnormal or malfunctioning behavior of systems or equipment.

[0025] The IC stripline test is a conductive immunity test using a microstrip line. The IC stripline test is a test method in which static electricity is applied using a static electricity generator to observe malfunctions and damage to semiconductor elements.

[0026] Electrostatic testing is a test method for evaluating resistance to electrostatic discharge caused by static electricity. Electrostatic testing is a test method in which static electricity is applied using a static electricity generator and the semiconductor device is observed for malfunctions or damage.

[0027] In the following description, the present technology will be described as being used in a DPI test of a semiconductor device, but it goes without saying that the present technology can also be applied to the other test methods mentioned above.

[0028] When applied to DPI testing, the load generation unit 1 generates a noise signal to be injected directly into the image sensor. The equipment constituting the load generation unit 1 generates a specific noise pattern or signal to be applied to the image sensor in accordance with the electromagnetic compatibility (EMC) standards of the image sensor. This generated noise signal is designed to meet the strict requirements of the EMC standards, ensuring appropriate noise injection into the image sensor.

[0029] The device under test 2 has an imaging device to be tested. If it is difficult to directly inject the noise signal generated by the load generator 1 into the imaging device, the imaging device may be mounted on a circuit board. This circuit board may include measuring equipment for generating the voltages and control signals required to drive the imaging device.

[0030] The evaluation unit 3 is composed of measuring instruments for observing the behavior of the device under test 2. In particular, if the device under test has an imaging element, the evaluation unit 3 may have a control IC (Integrated Circuit) such as an FPGA (Field-Programmable Gate Array) for reading register values ​​held internally by the imaging element. This enables detailed monitoring of the internal state and operation of the imaging element and precise analysis of the test results.

[0031] [(2) DPI Test] The flow of a general DPI test for checking whether or not an imaging element has a malfunction will be described with reference to Fig. 2. Fig. 2 is a flowchart showing an example of the flow of a DPI test. In a general DPI test, normal operation is confirmed mainly by monitoring malfunction signals and observing whether each voltage value meets the specifications.

[0032] 2, first, in step S11, the noise signal is set based on the standard, and parameters such as modulation method, frequency, and power are specified.

[0033] Next, in step S12, injection of a noise signal into the imaging device to be tested is started.

[0034] Next, in step S13, it is detected whether or not there is a functional abnormality in the imaging element. If no functional abnormality is detected (step S13: No), it is determined in step S14 that the imaging element is in the best condition (Class A) and has passed the test.

[0035] On the other hand, if a functional abnormality is detected in step S13 (step S13: Yes), the noise injection is stopped in step S15. Then, it is confirmed whether the functional abnormality continues when the noise injection is stopped (step S16). If no abnormality is detected (step S6: No), the abnormality is resolved (class C) and the test is determined to have passed in step S17.

[0036] If the malfunction continues even after the noise injection is stopped (step S16: Yes), the device is determined to be unsuccessful in step S18.

[0037] Next, in step S19, it is determined whether all tests have been completed. If the tests have not been completed (step S19: No), the process returns to step S11 and the noise signal settings are changed. For example, the same test may be performed again with a different power or the frequency may be swept.

[0038] Guidelines for implementing DPI testing are set out in the international standard IEC 62132-4. However, the detailed test methods and evaluation criteria are left to the tester. Because the definition of malfunction is left to the tester, the load tolerance evaluation obtained from the test results is not uniform.

[0039] Therefore, the present technology evaluates the load tolerance of the image sensor 21 based on the image signal output by the image sensor 21 to which a load is applied.

[0040] Conventionally, when the semiconductor device under test is an image sensor, a DPI test is not performed to check the image output from the image sensor during the test, and therefore image distortion due to noise injection is not considered as a malfunction.

[0041] Specifically, for example, when a DPI test is performed on an imaging element, noise may occur in the output image even if the internal voltage of the imaging element is normal. This will be explained with reference to FIG. 3. FIG. 3 is an example of an image output in a DPI test. As shown in FIG. 3, faint gray stripes appear in an area that should be a completely black image. These gray stripes are noise. In this way, noise may occur in the output image even if the internal voltage of the imaging element is normal. When the imaging element is an imaging element, it is undesirable for noise to occur in the output image signal. Therefore, this technology can provide more desirable effects to consumers.

[0042] As such, in the past, there was a lack of indicators for evaluating load tolerance in ensuring product quality, and the image signals output from the image sensor were not used to evaluate load tolerance. This technology is a groundbreaking method that solves these problems and enables quantitative evaluation of the load tolerance of image sensors.

[0043] (3) Configuration of Image Sensor Evaluation Apparatus A specific configuration example of an image sensor evaluation apparatus according to an embodiment of the present technology will be described with reference to Fig. 4. Fig. 4 is a block diagram showing a configuration example of an image sensor evaluation apparatus 100 according to an embodiment of the present technology. As shown in Fig. 4, the image sensor evaluation apparatus 100 includes a load generation unit 1, a test object 2, and an evaluation unit 3. This configuration is defined in the international standard IEC 62132-4.

[0044] The load generating unit 1, the device under test 2, and the evaluation unit 3 are connected to each other by cables such as USB cables or coaxial cables to transmit control signals and image signals. For transmitting these signals, an interface such as CSI2 or I2C is used.

[0045] The load generating unit 1, which generates a load (e.g., a noise signal) to be applied to the imaging element, includes, for example, an RF generator 11, an RF amplifier 12, a directional coupler 13, a power sensor 14, and a power monitor 15.

[0046] The high-frequency generator 11 generates a noise signal according to a frequency, power, and modulation method (e.g., sine wave, AM modulation, PM modulation, etc.). This allows a specific signal pattern or waveform to be applied to the DUT 2, making it possible to evaluate the load tolerance of the imaging element. By changing the frequency, power, etc., it is possible to generate noise signals under different conditions.

[0047] The high frequency amplifier 12 is a device for effectively amplifying the noise signal generated by the high frequency generator 11. This allows the signal applied to the device under test 2 to reach the required strength, enabling accurate and efficient testing.

[0048] The directional coupler 13, power sensor 14, and power monitor 15 are important measuring instruments for observing the input and reflected waves to the DUT 2 and accurately measuring the magnitude of the noise signal to the DUT 2. These measuring instruments are interconnected by, for example, a 50 Ω coaxial cable. The directional coupler 13 distinguishes between the signal's traveling direction and reflected direction, and the power sensor 14 and power monitor 15 measure the intensity of the input and reflected waves. This allows for accurate measurement of the power that actually reaches the DUT 2, ensuring the reliability of the test results.

[0049] The device under test 2 is composed of an imaging element 21 to be tested, a board 22 on which the imaging element is mounted, and a power supply (not shown) for driving the imaging element. When the imaging element 21 to be tested is mounted on the board 22, a decoupling network, which is a circuit for protecting the imaging element, is also mounted. This circuit is designed to ensure the operational stability of the imaging element and minimize the effects of external noise.

[0050] The evaluation unit 3 includes a control unit 31 and a calculation device 32. The control unit 31 controls the operation of the imaging element constituting the test object 2 and performs the important function of monitoring whether the imaging element is operating normally. To detect malfunction of the imaging element, the control unit 31 monitors, for example, whether the internal voltage and current values ​​of the imaging element are within normal ranges by sending and receiving control signals. This makes it possible to confirm whether the imaging element is operating correctly under specified electrical conditions.

[0051] Alternatively, the imaging element may have an internal malfunction signal. The control unit 31 can detect abnormal operation of the imaging element by monitoring this malfunction signal. The malfunction signal can be designed so that its output changes when the analog and digital circuits inside the imaging element malfunction. This method makes it possible to detect an abnormality when the imaging element exhibits unexpected operation.

[0052] The control unit 31 includes a control IC (Integrated Circuit) 311 for controlling the image sensor 21 and a control board 312 on which the control IC 311 is mounted. The control IC 311 may be, for example, an FPGA (Field-Programmable Gate Array). The control IC 311 has a function of transmitting a first control signal C1 for driving the image sensor 21 based on a second control signal C2 received from the arithmetic unit 32. The control IC 311 also has a function of generating a second image signal I2 to be transmitted to the arithmetic unit 32 based on a first image signal I1 received from the image sensor 21. The first control signal C1 can transmit not only a malfunction signal but also information identifying a defective portion transmitted from the image sensor 21.

[0053] The arithmetic unit 32 has a function of transmitting a second control signal C2 for driving the control IC 311 and a third control signal C3 for controlling the output signal of the high-frequency generator 11. The arithmetic unit 32 also has a function of receiving and processing the second image signal I2 output from the control IC 311 and displaying it as an image on, for example, a monitor. In this way, the arithmetic unit 32 evaluates the load tolerance of the image sensor 21 based on the image signal output by the image sensor 21 to which a load (for example, a noise signal) is applied.

[0054] The imaging device 21 used in this test outputs an image signal. Examples of imaging devices include a CCD image sensor, a CMOS image sensor, and a back-illuminated image sensor.

[0055] The computing device 32 may be, for example, a personal computer, a server, a mainframe, a supercomputer, or the like.

[0056] (4) Flow of the Image Sensor Evaluation Apparatus An example of the flow of the image sensor evaluation apparatus according to an embodiment of the present technology will be described with reference to Fig. 5. Fig. 5 is a flowchart showing an example of the flow of the image sensor evaluation apparatus according to an embodiment of the present technology.

[0057] 5, first, in step S21, a noise signal is set based on the standard, as in a general DPI method test, and parameters such as modulation method, frequency, and power are specified.

[0058] Next, in step S22, injection of a noise signal into the image sensor begins.

[0059] Next, in step S23, the presence or absence of a functional abnormality of the imaging element is detected. If no functional abnormality is detected (step S23: No), in step S24, the evaluation unit 3 measures the amount of noise contained in the image signal. For example, an algorithm for evaluating the amount of noise in each pixel or image region can be used for this purpose. The measured amount of noise is recorded in a temporary memory area for use in subsequent processing.

[0060] Next, in step S25, the evaluation unit 3 evaluates the load tolerance based on the amount of noise contained in the image signal and a predetermined threshold. The threshold serves as a criterion for determining whether the noise in the image signal is within an acceptable range. The threshold can be set based on defined specifications and standards. The set threshold is stored, for example, in a setting file or parameter database of the computing device 32, and can be referenced and changed.

[0061] If the amount of noise is, for example, equal to or less than a threshold (step S25: Yes), the image signal is determined to be acceptable and is evaluated as having high load tolerance. Then, in step S26, the image signal is determined to be in the best condition (Class A) and pass the test. Conversely, if the amount of noise exceeds the threshold (step S25: No), the image signal is determined to have a problem and has low load tolerance.

[0062] The amount of noise, which serves as an evaluation index, includes the pixel value of each pixel. The image generated by the imaging element 21 is represented by a collection of multiple pixels. A pixel is a basic element that represents information such as color and brightness, and the pixel value is a numerical value of that color or brightness. Normally, pixel values ​​reflect ideal image signals, but noise may be mixed into pixel values ​​due to external noise signals, etc. The effect of this noise on pixel values ​​is evaluated for each pixel.

[0063] The evaluation unit 3 calculates the difference between the pixel value of each pixel and the ideal pixel value based on the bit depth, and this difference indicates the level of noise. This quantifies the amount of noise in each pixel and performs an overall noise evaluation. The bit depth is the number of bits used to express pixel characteristics such as brightness and chromaticity, and the larger the value, the more diverse brightness and chromaticity can be expressed.

[0064] The evaluation unit 3 can evaluate the load tolerance based on at least one of the average pixel value, the maximum pixel value, the minimum pixel value, the variance of the pixel value, and the difference between the pixel value of each pixel and the pixel value of its neighboring pixels.

[0065] Regarding the average value of pixel values, the evaluation unit 3 calculates the average of each pixel value of the image and evaluates whether or not there is an abnormality. It is verified whether or not the average value exceeds a threshold value.

[0066] Regarding the maximum and minimum pixel values, the evaluation unit 3 checks at least one of the maximum and minimum pixel values ​​in the image, and evaluates the presence or absence of an abnormality depending on whether this exceeds a threshold value.

[0067] Regarding the variance of pixel values, the evaluation unit 3 calculates the variance of pixel values ​​and evaluates the presence or absence of an abnormality in the image based on whether the variance exceeds a threshold. A larger variance indicates a larger variation in pixel values ​​within the image.

[0068] Regarding the difference from the neighboring pixel value, the evaluation unit 3 checks the difference from the neighboring pixel in the image and checks whether there is any noise such as horizontal streaks, thereby detecting abnormal patterns or problems with horizontal streaks.

[0069] The image signal used for evaluation may be at least one of a still image signal and a moving image signal. The still image signal and the moving image signal preferably include at least one of a white image signal, a black image signal, a gray image signal, a geometric pattern image signal, and a moving image signal.

[0070] A white image is composed of a uniform bright color throughout the image. The pixel value of each pixel is maximum, and the color tone is usually close to white. In load tolerance evaluation, since the pixel values ​​in a white image are uniform, it is used to check the homogeneity of the entire image.

[0071] A black image is composed of a uniform dark color throughout the image. The pixel value of each pixel is minimum, and the color tone is usually close to black. In load tolerance evaluation, since the pixel values ​​of a black image are uniform, it is used to check the homogeneity of the entire image.

[0072] A gray image is a gray-based image with different luminance and brightness levels. The pixel value of each pixel represents an intermediate luminance, allowing visual recognition of shades. In load tolerance evaluations, it is used to check whether luminance is represented evenly.

[0073] Geometric pattern images are images that combine geometric shapes and patterns, such as checkerboards and stripes. In load tolerance evaluations, they are used to check whether complex patterns are accurately represented and whether noise is present in the edges and details.

[0074] A still image is a single image that does not change over time. In addition to the above images, still images include, for example, landscapes, portraits, objects, abstract paintings, charts, and illustrations.

[0075] Video is a collection of continuous still images, and the images change over time. In load tolerance evaluation, it is used to check whether the images are stable even in video and whether there is noise or distortion between consecutive frames.

[0076] Here, it can be said that the present technology evaluates the load tolerance of an image sensor by combining the power frequency characteristics of the image sensor obtained from the results of a general DPI test with the amount of noise contained in an image signal specific to the present technology. In other words, the evaluation unit 3 evaluates the load tolerance of the image sensor based on the power frequency characteristics of the image sensor obtained from the results of the DPI test and the amount of noise contained in the image signal.

[0077] On the other hand, if a malfunction is detected (step S23: Yes) or if the amount of noise exceeds the threshold (step S25: No), noise injection is stopped in step S27.

[0078] Then, it is confirmed whether the malfunction continues when the noise injection is stopped (step S28). If no malfunction is detected (step S28: No), in step S29, the evaluation unit 3 measures the amount of noise contained in the image signal. This may be done using an algorithm that evaluates the amount of noise in each pixel or image region. The measured amount of noise is recorded in a temporary memory area for use in subsequent processing.

[0079] Next, in step S30, the evaluation unit 3 evaluates the load tolerance based on the amount of noise contained in the image signal and a predetermined threshold. The threshold serves as a criterion for determining whether the noise in the image signal is within an acceptable range. The threshold is set based on prescribed specifications and standards. The set threshold is stored, for example, in a setting file or parameter database of the calculation device 32, and can be referenced and changed.

[0080] If the amount of noise is, for example, equal to or less than a threshold (step S30: Yes), the image signal is determined to be acceptable and is evaluated as having high load tolerance. Then, in step S31, the test is determined to have passed, with the abnormality resolved (Class C). Conversely, if the amount of noise exceeds the threshold (step S30: No), the image signal is determined to have a problem and has low load tolerance.

[0081] If the malfunction continues even after the noise injection is stopped (step S28: Yes), or if the amount of noise exceeds the threshold (step S30: No), the device is rejected (step S32).

[0082] Next, in step S33, it is determined whether all tests have been completed. If the tests have not been completed (step S33: No), the process returns to step S21 and the noise signal settings are changed. For example, the same test may be performed again with a different power or the frequency may be swept.

[0083] The above description of the imaging element evaluation device according to the first embodiment of the present technology can be applied to other embodiments of the present technology unless there is a particular technical contradiction.

[0084] [2. Second Embodiment of the Present Technology (Example 2 of Image Sensor Evaluation Apparatus)] The evaluation unit 3 can classify the image sensors into classes according to their load tolerance. For example, in step S25 of FIG. 5 , the evaluation unit 3 evaluates the load tolerance based on the amount of noise contained in the image signal and a predetermined threshold. At this time, the evaluation unit 3 can classify the image sensors into multiple classes according to the level of the noise amount. This allows the level of noise to be clearly understood and the performance of the image sensors to be rigorously evaluated.

[0085] Indices for evaluating the amount of noise include the average pixel value, the maximum pixel value, the minimum pixel value, the variance of pixel values, and the difference between the pixel value of each pixel and the pixel values ​​of its neighboring pixels. Based on these evaluation indices, the evaluation unit 3 evaluates the load tolerance of the image sensor and classifies it into Class I, II, III, etc.

[0086] This will be described with reference to Fig. 6 and Fig. 7. Fig. 6 and Fig. 7 show examples of screens displayed by the imaging element evaluation device 100 according to an embodiment of the present technology. These screens can be displayed on an output device such as a display included in the arithmetic device 32, for example.

[0087] FIG. 6 shows an example in which the horizontal axis represents the horizontal pixel coordinate and the vertical axis represents the pixel value of each pixel. The noise distribution is visually represented using the pixel values ​​at each coordinate across the entire image, and classification is performed. This graph allows visual recognition of how the pixel values ​​at each coordinate change across the entire image. When noise is present, irregular fluctuations occur in the pixel values, which can be observed on the graph. Visually understanding the noise distribution clarifies the level and pattern of noise for each pixel, resulting in classification. Classification is performed by classifying the noise into different categories based on its intensity and type. This allows the load tolerance of the image sensor to be evaluated.

[0088] FIG. 7 shows an example in which the horizontal axis represents the frequency of the injected noise and the vertical axis represents the amount of noise. The injected noise level is used as a parameter. As an example of this parameter, graphs corresponding to three different injected noise levels, 0 dBm, 12 dBm, and 24 dBm, are shown. For example, when the injected noise level is 12 dBm, Class III is achieved, but when the injected noise level increases to 24 dBm, only Class II is achieved. In this way, it is possible to explicitly capture the characteristics of image noise for each injected noise frequency and classify it into different classes.

[0089] The shape of the threshold line for classifying into each class is not particularly limited. In this example, the shape of the threshold line for classifying into classes is a step shape, but it may be, for example, a straight line or a curved line.

[0090] The above description of the imaging element evaluation device according to the second embodiment of the present technology can be applied to other embodiments of the present technology unless there is a particular technical contradiction.

[0091] 3. Third Embodiment of the Present Technology (Example 3 of Image Sensor Evaluation Apparatus) The evaluation unit 3 may classify the image signals into classes using a trained model that has been machine-learned to determine the correlation between the amount of noise contained in the image signal and the class corresponding to the amount of noise. This trained model automatically determines which class the amount of noise belongs to. This enables quantitative evaluation of the load tolerance of the image sensor.

[0092] This machine learning can be, for example, supervised learning using training data. A trained model can be generated by supervised learning, where the amount of noise contained in an image signal is the input data and the class label corresponding to this amount of noise is the output data. Supervised learning is a method of learning the relationship between input data, called training data, and a target pair. Based on the training data, a model can be constructed so that an appropriate output can be predicted for new input data.

[0093] Alternatively, machine learning can be unsupervised learning, which uses unlabeled images. A trained model can be generated by training it to recognize patterns in the images. The trained model can analyze the patterns in the images and automatically classify them into their respective classes.

[0094] Alternatively, machine learning can be reinforcement learning, where a trained model learns how to classify noise levels based on rewards and penalties. A reward can be set for each class, and the trained model can earn a reward for making the correct classification.

[0095] The trained model can be constructed using techniques such as deep neural networks (DNN), multi-layer perceptrons (MLP), convolutional neural networks (CNN), recurrent neural networks (RNN), linear regression, logistic regression, support vector machines, decision trees, and random forests.

[0096] The above description of the imaging element evaluation device according to the third embodiment of the present technology can be applied to other embodiments of the present technology unless there is a particular technical contradiction.

[0097] 4. Fourth embodiment of the present technology (example 4 of image sensor evaluation device)] A noise signal injected into an image sensor will be described with reference to Fig. 8. Fig. 8 is a schematic diagram showing a configuration example of a test object 2 etc. according to an embodiment of the present technology.

[0098] 8, the device under test 2 has an input terminal 23, an image pickup element 21, and an output terminal 24 mounted on a substrate 22. An input signal D is transmitted from the input terminal 23 to the image pickup element 21. in is transmitted from the image sensor 21 to the output terminal 24, and an output signal D out The input signal D in The output signal D includes a control signal transmitted from the control unit 31 and a drive voltage for driving the image sensor 21. out includes the image signal output by the image sensor 21.

[0099] In the DPI method test, this input signal D in and the output signal D out When a noise signal is injected into the output terminal 24, the load tolerance of the image sensor 21 is evaluated. out In such cases, it is difficult to determine whether this noise is due to the injected noise signal being mixed into the image signal, or whether the noise injection has caused a malfunction in the driver or regulator inside the image sensor.

[0100] Therefore, the present technology divides multiple signal transmission paths into signal transmission paths that transmit image signals and signal transmission paths that impart loads. Of the multiple signal transmission paths connecting the image sensor 21 and the control unit 31, it is preferable to impart loads to signal transmission paths that do not transmit image signals. This will be described with reference to Fig. 9. Fig. 9 is a schematic diagram showing an example configuration of an image sensor evaluation device 100 according to an embodiment of the present technology.

[0101] 9 shows the imaging element 21 to be tested and the control unit 31. The imaging element 21 and the control unit 31 are connected to each other by a plurality of signal transmission paths 41, 42. Typically, image signals are transmitted via these plurality of signal transmission paths 41, 42. Note that in this configuration example, the number of signal transmission paths is two, but the number of transmission paths is not particularly limited.

[0102] In this technology, of the two signal transmission paths 41, 42, the first signal transmission path 41 transmits an image signal, and the second signal transmission path 42 does not transmit an image signal. Then, a noise generating unit 421 arranged in the second signal transmission path 42 supplies a noise signal to the second signal transmission path 42, which does not transmit an image signal. As a result, when noise occurs in an image, it is determined that the noise is not due to the noise signal being mixed into the image signal, but rather that the noise is being generated by the applied noise signal affecting a control circuit or the like included in the image sensor 21. In this way, it is possible to determine whether the noise generated in the image is due to the injected noise signal being mixed into the image signal, or whether the noise injection is due to an effect on a control circuit or the like inside the image sensor.

[0103] Alternatively, a noise suppression unit may be further provided that suppresses noise generated by injecting a noise signal into an input signal of the image sensor 21 from propagating to the control unit 31. Configuration examples of the noise suppression unit will be described with reference to Figures 10 to 12. Figures 10 to 12 are schematic diagrams showing configuration examples of the noise suppression unit 25 according to an embodiment of the present technology.

[0104] 10, the noise suppression unit 25 may be a GND via. A GND via is a via (conductive hole) that connects the back surface of the package of the image sensor to the ground plane of the substrate. In this configuration example, the noise suppression unit 25 is formed around the signal transmission path that connects the image sensor 21 and the output terminal 24. As a result, the input signal D of the image sensor 21 in This can prevent the noise generated by injecting a noise signal into the control unit 31 from propagating to the control unit 31. As a result, it becomes easier to identify the location causing the malfunction.

[0105] There is no particular limitation on the number of noise suppression units 25. The number of noise suppression units 25 may be one or more.

[0106] Alternatively, as shown in Fig. 11, the ground pad section may be divided into multiple sections. In this configuration example, the noise suppression section has a first ground pad section 251 connected to the input terminal 23 of the image sensor 21 and a second ground pad section 252 connected to the output terminal 24 of the image sensor 21. Since each ground pad section and circuit is independent, it is possible to suppress the propagation of noise. In other words, the input signal D of the image sensor 21 in This can prevent noise generated by injecting a noise signal into the ground pad 31 from propagating to the control unit 31. The number of divided ground pad portions is not particularly limited.

[0107] 12, the noise suppression unit 25 may be a filter that blocks signals of a predetermined frequency. In this configuration example, the filter 25 is disposed on the signal transmission path that connects the image sensor 21 and the output terminal 24. This allows the filter 25 to block noise signals of a predetermined frequency. In other words, the input signal D of the image sensor 21 in This can prevent noise generated by injecting a noise signal into the control unit 31 from propagating to the control unit 31.

[0108] The filter 25 may be at least one of a low-pass filter, a high-pass filter, a band-pass filter, and a band-stop filter.

[0109] 13A is a graph showing the characteristics of a low-pass filter. The low-pass filter reduces the cutoff frequency f c The lower frequency components are hardly attenuated, and the cutoff frequency f c This is a filter that attenuates higher frequency components.

[0110] 13B is a circuit diagram showing an example of the configuration of a low-pass filter. As an example of the configuration of the low-pass filter, an RC low-pass filter composed of a resistor R and a capacitor C is shown. In addition to this RC low-pass filter, for example, an LC low-pass filter composed of an inductor and a capacitor, or an active low-pass filter combining active elements such as transistors may also be used.

[0111] 14A is a graph showing the characteristics of a high-pass filter. The high-pass filter reduces the cutoff frequency f c Higher frequency components are hardly attenuated, and the cutoff frequency f c A filter that attenuates lower frequency components.

[0112] 14B is a circuit diagram showing an example of the configuration of a high-pass filter. As an example of the configuration of the high-pass filter, an RC high-pass filter composed of a resistor R and a capacitor C is shown. In addition to this RC high-pass filter, for example, an LC high-pass filter composed of an inductor and a capacitor, or an active high-pass filter combining active elements such as transistors may also be used.

[0113] 15A is a graph showing the characteristics of a bandpass filter. A bandpass filter is a filter that passes only a specific frequency band of an input signal and attenuates other frequency components. The cutoff frequency f c1 ~f c2 This filter barely attenuates the frequency components of this frequency range, but attenuates the other frequency components.

[0114] 15B is a circuit diagram showing an example of the configuration of a band-pass filter, which is a band-pass filter configured with a resistor R, an inductor L, and a capacitor C.

[0115] 16A is a graph showing the characteristics of a band-stop filter. A band-stop filter is a filter that attenuates only a specific frequency band of an input signal and passes other frequency components. The cutoff frequency f c1 ~f c2 This filter attenuates frequency components and barely attenuates other frequency components.

[0116] 16B is a circuit diagram showing an example of the configuration of a band-stop filter, which is a band-stop filter configured with a resistor R, an inductor L, and a capacitor C.

[0117] The above description of the imaging element evaluation device according to the fourth embodiment of the present technology can be applied to other embodiments of the present technology unless there is a particular technical contradiction.

[0118] 5. Fifth Embodiment of the Present Technology (Example of Image Sensor Evaluation Method) The present technology provides an image sensor evaluation method that includes generating a load to be applied to an image sensor, and evaluating the load tolerance of the image sensor based on an image signal output by the image sensor to which the load is applied.

[0119] An imaging element evaluation method according to an embodiment of the present technology will be described with reference to Fig. 17. Fig. 17 is a flowchart showing an example of the flow of the imaging element evaluation method according to an embodiment of the present technology.

[0120] As shown in FIG. 17, first, in step S1, a load to be applied to the imaging device to be tested is generated.

[0121] Next, in step S2, the load tolerance of the image sensor is evaluated based on the image signal output from the image sensor to which the load is applied. For example, the above-mentioned image sensor evaluation device can be used to generate the load and evaluate the load tolerance.

[0122] The above description of the imaging element evaluation method according to the fifth embodiment of the present technology can be applied to other embodiments of the present technology unless there is a particular technical contradiction.

[0123] It should be noted that the embodiments of the present technology are not limited to the above-described embodiments, and various modifications are possible within the scope of the present technology. The specific numerical values, shapes, materials (including compositions), etc. described in each embodiment are merely examples, and the present technology is not limited to these.

[0124] The present technology may also be configured as follows. [1] An imaging element evaluation device comprising: a load generation unit that generates a load to be applied to an imaging element; and an evaluation unit that evaluates load tolerance of the imaging element based on an image signal output by the imaging element to which the load is applied. [2] The imaging element evaluation device according to [1], in which the evaluation unit evaluates the load tolerance based on a noise amount included in the image signal and a predetermined threshold. [3] The imaging element evaluation device according to [2], in which the noise amount includes a pixel value of each pixel. [4] The imaging element evaluation device according to [3], in which the evaluation unit evaluates the load tolerance based on at least one of an average value of the pixel values, a maximum value of the pixel values, a minimum value of the pixel values, a variance of the pixel values, and a difference between the pixel value of each pixel and the pixel values ​​of its neighboring pixels. [5] The imaging element evaluation device according to any one of [1] to [4], in which the evaluation unit classifies images into classes according to the load tolerance. [6] The imaging element evaluation device according to any one of [2] to [5], wherein the evaluation unit classifies the image signal into the class using a trained model that has been machine-learned to determine the correlation between the amount of noise contained in the image signal and the class corresponding to the amount of noise. [7] The imaging element evaluation device according to any one of [1] to [6], which is used in an immunity test of the imaging element. [8] The imaging element evaluation device according to [7], which is used in a DPI method test of the imaging element. [9] The imaging element evaluation device according to [8], wherein the evaluation unit evaluates the load tolerance of the imaging element based on the power frequency characteristics of the imaging element obtained from the results of the DPI method test and the amount of noise contained in the image signal.

[10] The imaging element evaluation device according to any one of [1] to [9], wherein the image signal is at least one of a still image signal and a moving image signal, and the still image signal and the moving image signal include at least one of a white image signal, a black image signal, a gray image signal, a geometric pattern image signal, and a moving image signal.

[11] The image sensor evaluation device according to any one of [1] to

[10] , further comprising a control unit for controlling the driving of the image sensor, and applying the load to a signal transmission line that does not transmit the image signal among a plurality of signal transmission lines connecting the image sensor and the control unit.

[12] The image sensor evaluation device according to any one of [1] to

[11] , further comprising a control unit for controlling the driving of the image sensor, and further comprising a noise suppression unit that suppresses noise generated by injecting a noise signal into an input signal of the image sensor from propagating to the control unit.

[13] The image sensor evaluation device according to

[12] , wherein the noise suppression unit is a GND via.

[14] The image sensor evaluation device according to

[12] or

[13] , wherein the noise suppression unit has a first ground pad portion connected to an input terminal of the image sensor and a second ground pad portion connected to an output terminal of the image sensor.

[15] The image sensor evaluation device according to any one of

[12] to

[14] , wherein the noise suppression unit is a filter that blocks signals of a predetermined frequency.

[16] The image sensor evaluation device according to

[15] , wherein the filter is at least one of a low-pass filter, a high-pass filter, a band-pass filter, and a band-stop filter.

[17] An image sensor evaluation method comprising: generating a load to be applied to an image sensor; and evaluating the load tolerance of the image sensor based on an image signal output from the image sensor to which the load is applied.

[0125] 100 Image sensor evaluation device 1 Load generation unit 11 High frequency generator 12 High frequency amplifier 13 Directional coupler 14 Power sensor 15 Power monitor 2 Test object 21 Image sensor 22 Substrate 23 Input terminal 24 Output terminal 25 Noise suppression unit 251 First ground pad unit 252 Second ground pad unit 3 Evaluation unit 31 Control unit 311 Control IC 312 Control substrate 32 Arithmetic unit 41 First signal transmission path 42 Second signal transmission path S1 Generating a load to be applied to the image sensor S2 Evaluating the load tolerance of the image sensor

Claims

1. An imaging element evaluation device comprising: a load generation unit that generates a load to be applied to an imaging element; and an evaluation unit that evaluates the load tolerance of the imaging element based on an image signal output by the imaging element to which the load is applied.

2. The imaging element evaluation device according to claim 1, wherein the evaluation unit evaluates the load tolerance based on the amount of noise contained in the image signal and a predetermined threshold value.

3. The imaging element evaluation device according to claim 2, wherein the amount of noise includes a pixel value of each pixel.

4. The imaging element evaluation device according to claim 3, wherein the evaluation unit evaluates the load tolerance based on at least one of the average value of the pixel values, the maximum value of the pixel values, the minimum value of the pixel values, the variance of the pixel values, and the difference between the pixel value of each pixel and the pixel values ​​of its neighboring pixels.

5. The imaging element evaluation device according to claim 1, wherein the evaluation unit classifies the imaging elements into classes according to the load tolerance.

6. The imaging element evaluation device according to claim 2, wherein the evaluation unit classifies the image signals into classes using a trained model that has been machine-learned to determine the correlation between the amount of noise contained in the image signals and the classes corresponding to the amount of noise.

7. The imaging element evaluation device according to claim 1, which is used for immunity testing of the imaging element.

8. The imaging element evaluation device according to claim 7, which is used for DPI method testing of the imaging element.

9. The imaging element evaluation device according to claim 8, wherein the evaluation unit evaluates the load tolerance of the imaging element based on the power frequency characteristics of the imaging element obtained from the results of a DPI method test and the amount of noise contained in the image signal.

10. The imaging element evaluation device according to claim 1, wherein the image signal is at least one of a still image signal and a moving image signal, and the still image signal and the moving image signal include at least one of a white image signal, a black image signal, a gray image signal, and a geometric pattern image signal.

11. The imaging element evaluation device according to claim 1, further comprising a control unit that controls the driving of the imaging element, and wherein the load is applied to a signal transmission line that does not transmit the image signal among a plurality of signal transmission lines connecting the imaging element and the control unit.

12. The imaging element evaluation device according to claim 1, further comprising a control unit that controls the driving of the imaging element, and a noise suppression unit that suppresses the propagation of noise generated by injecting a noise signal into the input signal of the imaging element to the control unit.

13. The imaging element evaluation device according to claim 12, wherein the noise suppression section is a GND via.

14. The imaging element evaluation device according to claim 12, wherein the noise suppression section has a first ground pad section connected to an input terminal of the imaging element, and a second ground pad section connected to an output terminal of the imaging element.

15. The imaging element evaluation device according to claim 12, wherein the noise suppression section is a filter that blocks signals of a predetermined frequency.

16. The imaging element evaluation device according to claim 15, wherein the filter is at least one of a low-pass filter, a high-pass filter, a band-pass filter, and a band-stop filter.

17. A method for evaluating an imaging element, comprising: generating a load to be applied to an imaging element; and evaluating the load tolerance of the imaging element based on an image signal output by the imaging element to which the load is applied.

Images