A testing device and method for simulating light input of an imaging chip

Abstract

The application provides a testing device and method for simulating light input of an imaging chip. The device comprises a light path reconstruction module, a sensor reconstruction module and an RTL reconstruction module. The light path reconstruction module is used to obtain a photon arrival time sequence of each pixel. The sensor reconstruction module is used to obtain a photon time sequence of a simulated photosensitive device response according to the photon arrival time sequence of each pixel. The RTL reconstruction module is used to reconstruct a digital PWM pulse signal of each pixel according to the photon time sequence of the simulated photosensitive device response, as a simulation test input of a subsequent digital circuit. The application can simulate the light input of the photosensitive device as a module in the digital domain, obtain a corresponding response digital signal, verify the function accuracy of the subsequent circuit and the output image quality, and optimize the chip design.

Description

Technical Field

[0001] This invention proposes a testing device and method for the optical input of an analog imaging chip, belonging to the field of algorithms and digital circuits. Background Technology

[0002] The simulated imaging chip optical input testing device includes an optical path reconstruction module, a sensor reconstruction module, and an RTL (Register Transfer Level) reconstruction module. Optical path reconstruction refers to building a model to reconstruct the arrival times of each photon at the corresponding pixel, establishing a complete photon propagation timeline model. This model can simulate the real optical input of the imaging chip, allowing for early verification of the chip's functionality and optimization of its design. Since light inevitably carries Poisson noise during propagation, and the smaller the light signal, the lower the signal-to-noise ratio and the worse the image quality, constructing a realistic photon propagation model is crucial for optimizing the imaging chip design and improving the image quality output by the sensor.

[0003] Sensor reconstruction refers to establishing a digital model of the sensor's light input processing based on the parameters of the photosensitive device. This is equivalent to passing the light through a filter, with the output being the light signal collected by the photosensitive device. Existing technologies offer limited configurable parameters because the input and output do not use a photon time sequence, which significantly limits the simulation of light configuration and prevents a true simulation of the sensor's own light response.

[0004] RTL (Register Transfer Level) reconstruction refers to generating a parallel digital signal array based on the optical path model established for each pixel. Existing technologies often directly configure parameters such as gray values, ignoring the time information of optical path propagation. The model is coarse and cannot configure the PWM (Pulse Width Modulation) pulse width corresponding to each pixel, thus failing to meet the front-end testing requirements of various imaging chips, such as SPAD (Single Photon Avalanche Diode) sensor imaging chips.

[0005] Because custom photosensitive devices cannot be used as a module in the early verification of photosensitive chips to simulate light input and obtain its corresponding light output, it is impossible to intuitively verify the functional accuracy of subsequent circuits. Summary of the Invention

[0006] The purpose of this invention is to provide a testing device and method that can accurately simulate the light input of an imaging chip for front-end testing of various imaging chips.

[0007] The technical solution adopted by the device of the present invention is as follows:

[0008] A testing device for the optical input of an analog imaging chip includes an optical path reconstruction module, a sensor reconstruction module, and an RTL reconstruction module. The optical path reconstruction module is used to obtain the photon arrival time sequence of each pixel. The sensor reconstruction module is used to obtain the photon time sequence of the analog photosensitive device response based on the photon arrival time sequence of each pixel. The RTL reconstruction module is used to reconstruct the digital PWM pulse signal of each pixel based on the photon time sequence of the analog photosensitive device response, as the simulation test input for subsequent digital circuits.

[0009] The present invention also provides a testing method for a testing device for simulating the light input of an imaging chip, comprising the following steps:

[0010] S1, Input image, configure corresponding illumination parameters and output parameters, and obtain a set of two-dimensional arrays representing the arrival time of photons corresponding to different pixels;

[0011] S2, based on the two-dimensional array of photon arrival times, configure the parameters of the photosensitive device to obtain a set of two-dimensional arrays representing the photon arrival times of different pixels;

[0012] S3 uses the photon timing information collected by the pixels to generate corresponding digital pulse signals, configures the corresponding digital output pulse width to simulate the response of the photosensitive device, and obtains the final parallel digital pulse signal.

[0013] Furthermore, the specific steps of step S1 include:

[0014] S11, Read the input image and create the corresponding gray value array;

[0015] S12, configure the illumination parameters, and calculate the relationship between the total number of photons of each pixel and the image gray value accordingly, to obtain a set of vectors that characterize the total number of photons received by each pixel under the set conditions;

[0016] S13. Using the continuous photon flux function, the time interval δ between adjacent photons is derived from the probability distribution function by using the inverse function. Since the photon flux function λ(t) is time-dependent, the cumulative distribution function of the time interval δ is related to the specific time t. When a photon arrives at time t, the cumulative distribution function of the time interval of the next photon is obtained by using the characteristic that the photon arrival time conforms to a non-homogeneous Poisson distribution.

[0017] S14. Using the inverse function method, the cumulative distribution function of the time interval between photon arrivals is known, and the specific time of photon arrival is calculated. The arrival time of the first photon is set to 0, and the arrival time of each subsequent photon is calculated accordingly. Finally, the photon arrival time sequence of each pixel is obtained.

[0018] Furthermore, in step S1, the illumination parameters include the peak quantum efficiency wavelength of the photosensitive device, the photosensitive radius of the photosensitive device, the irradiance corresponding to the highest gray value, and the exposure time window of the photosensitive device.

[0019] Furthermore, in step S2, the parameters of the photosensitive device are configured and a corresponding digital model is established. The parameters of the photosensitive device include quantum efficiency and dead time.

[0020] Furthermore, in step S3, based on the photon time sequence input from the previous stage, a continuous digital pulse signal of all pixels is output, where each PWM pulse represents a photon signal received by the sensor.

[0021] The photon propagation model constructed in this invention during the optical path reconstruction process includes realistic photon Poisson noise and real photosensitive device parameters, enabling a relatively accurate simulation of the optical input conditions of the designed chip. This device allows the photosensitive device to function as a module in the digital domain, accurately simulating the optical input, obtaining the corresponding digital signal, verifying the functional accuracy of subsequent circuits and the output image quality, and optimizing the chip design. Attached Figure Description

[0022] Figure 1 This is a structural block diagram of the device for implementing optical input of an analog imaging chip in an embodiment of the present invention;

[0023] Figure 2 This is a pseudocode flowchart of the optical path reconstruction module in an embodiment of the present invention;

[0024] Figure 3 This is a pseudocode flowchart of the sensor reconstruction module in an embodiment of the present invention;

[0025] Figure 4 These are comparison images of the input image and the output image of the analog sensor in an embodiment of the present invention: (a) initial input image, (b) analog sensor output image;

[0026] Figure 5 This is a schematic diagram of the PWM signals output by different pixels in the RTL reconstruction module in this embodiment of the invention. Detailed Implementation

[0027] This embodiment provides a scheme for optical input of an analog imaging chip, and the structural block diagram of its implementation device is shown below. Figure 1As shown, the system includes an optical path reconstruction module, a sensor reconstruction module, and an RTL reconstruction module. The optical path reconstruction module takes an image P as input, establishes a corresponding grayscale array P', and outputs a photon arrival time sequence X(i,j) for each pixel, where i represents the pixel index and j represents the photon count index. The sensor reconstruction module takes the initial photon arrival time sequence X(i,j) as input and outputs a photon arrival time sequence X'(i,j) simulating the response of the photosensitive device, where i represents the pixel index and j represents the photon count index. The RTL reconstruction module takes the downsampled photon arrival time sequence X'(i,j) as input and outputs a continuous digital pulse signal for all pixels, which is the final PWM signal.

[0028] The optical path reconstruction module in this embodiment requires a test image P as input to establish a corresponding gray value array P'. Next, it configures various illumination parameters: peak quantum efficiency wavelength L of the photosensitive device; photosensitive radius r of the photosensitive device; irradiance I corresponding to the highest gray value; exposure time window n of the photosensitive device; and constant parameters as follows: speed of light is set to c, Planck's constant is set to h, and photon energy is set to E. It can calculate the total number of photons PN under the condition corresponding to the highest gray value, and the coefficient K between the total number of photons and the image gray value.

[0029] The photon energy E at the peak quantum efficiency wavelength can be calculated using the photon energy formula:

[0030]

[0031] Within a set time window, the total number of photons arriving at a single photosensitive device is PN:

[0032]

[0033] The coefficient K between the total number of photons and the image gray value is:

[0034]

[0035] The above method can be used to calculate the linear relationship between the total number of photons in a single pixel and the gray value of the image, and obtain the coefficient K. This relationship is then mapped to all pixels to obtain a set of vectors N(i) that represent the total number of photons received by each pixel under certain conditions, where i represents the pixel number of the image.

[0036] Given the total number of photons per pixel in an image, we begin reconstructing the input optical path model. It is known that the number of photons received per pixel per unit time by the sensor follows a Poisson distribution with parameter λ, while the photon arrival time follows a non-homogeneous Poisson distribution.

[0037] First, let the photon flux function be λ(t), which characterizes the number of photons arriving at the sensor per unit time and can be obtained through image grayscale mapping. Let... Let (p, q) be the number of photons arriving during the time interval. Obey the parameter as Given a Poisson distribution, the probability function of the number of photons arriving within the time interval (p,q) can be obtained as follows:

[0038]

[0039] Next, using the continuous photon flux function and its inverse function, the time interval δ between adjacent photons is derived from the probability distribution function. Since the photon flux function λ(t) is time-dependent, and the cumulative distribution function (CDF) of δ is related to the specific time t, the cumulative distribution function of the time interval between the arrival of the next photon at time t can be expressed as:

[0040]

[0041] in Let y = G(δ|t),

[0042] Calculate its inverse function δ=G -1 (y|t), we can get δ=-t+Λ -1 [Λ(t)-ln(1-y)], where y∈(0,1), is taken as a random variable, and 1-y∈(0,1), is set as a random variable R. The original expression is equivalent to δ=-t+Λ -1 [Λ(t)-ln(R)], since the reconstructed λ(t) is piecewise linearly integrable, therefore the inverse function Λ -1 (t) is easy to obtain.

[0043] Therefore, the arrival time of each photon is T = δ + t = Λ -1 [Λ(t)-ln(R)], (t is the arrival time of the previous photon). Let the arrival time of the first photon be 0, and use this to calculate the arrival time sequence of each subsequent photon.

[0044] Assuming the light intensity remains constant throughout the entire time window, therefore Since Λ(t) is a constant and independent of time, Λ(t) = λ × t. Substituting this into δ = -t + Λ -1 [Λ(t)-ln(R)] yields a new formula for the photon time interval:

[0045]

[0046] The photon arrival time sequence X(i,j) for each pixel is:

[0047]

[0048] Thus, the output X(i,j) of the optical path reconstruction module is obtained, and the specific implementation method is as follows: Figure 2 The pseudocode for the optical path reconstruction module is shown below.

[0049] Given the photon arrival time sequence X(i,j) for each pixel, the next step is to establish a corresponding digital model based on the semiconductor characteristics of the designed photosensitive device. Common reference quantities include quantum efficiency η and dead time τ.

[0050] Taking the dead time τ as an example, we first analyze the first pixel and set the arrival time of the first acquired photon to 0, i.e., X'(1,1)=0. We set up a comparator. If the time difference between the arrival time of the next photon and the photon at time 0 is less than or equal to the dead time, then this photon is ignored. We continue to compare the next photon. If the arrival time is greater than the dead time, then it is considered a valid photon that can be acquired. Its time is stored in the array and recorded as X'(1,2). By analogy, we can obtain the photon time sequence X'(i,j) of all photosensitive devices of all pixels.

[0051] Thus, the output X'(i,j) of the sensor reconstruction module is obtained, and the specific implementation is as follows: Figure 3 The pseudocode flowchart of the sensor reconstruction module is shown.

[0052] Based on the image input from the previous stage and the configured light intensity parameters and sensor parameters, the light signal output time sequence X'(i,j) of each sensor is known. The next step is to use the time sequence information as the input of the RTL reconstruction module. Using Verilog language, it is converted into a continuous digital pulse signal of all pixels. Each pulse represents the photon signal received by the sensor. This signal can be used for simulation testing of the subsequent digital module.

[0053] The specific design method is as follows: Assuming the smallest unit of the pre-stage optical output time sequence is 1ns, the main frequency needs to be set to 1GHz. First, set a set of registers with a size matching the pre-stage, and read in the pre-stage optical signal output time sequence X'(i,j). Second, design a sub-module. The function of this module includes inputting a set of time sequences and the desired configured response PWM pulse width, and outputting a wire-type variable with the corresponding pulse width. Each high level is consistent with the optical signal output time sequence X'(i,j). Finally, batch instantiate this sub-module in the top-level file. Each pixel corresponds to an independent sub-module, and finally obtains the parallel output digital signal of all pixels, which can be directly used as the output of the tb file to provide test signals for the subsequent circuit to verify the function and performance of the chip design.

Claims

1. A method for testing the optical input of an analog imaging chip, characterized in that, Includes the following steps: S1, Input image, configure corresponding illumination parameters and output parameters, and obtain a set of two-dimensional arrays representing the arrival time of photons corresponding to different pixels; S2, based on the two-dimensional array of photon arrival times, configure the parameters of the photosensitive device to obtain a set of two-dimensional arrays representing the photon arrival times of different pixels; S3 uses the photon timing information collected by the pixel to generate the corresponding digital pulse signal, configures the corresponding digital output pulse width to simulate the response of the photosensitive device, and obtains the final parallel digital pulse signal. The specific steps of step S1 include: S11, Read the input image and create the corresponding gray value array; S12, configure the illumination parameters, and calculate the relationship between the total number of photons of each pixel and the image gray value accordingly, to obtain a set of vectors that characterize the total number of photons received by each pixel under the set conditions; S13. Using the continuous photon flux function, the time interval δ between adjacent photons is derived from the probability distribution function by using the inverse function. Since the photon flux function λ(t) is time-dependent, the cumulative distribution function of the time interval δ is related to the specific time t. When a photon arrives at time t, the cumulative distribution function of the time interval of the next photon is obtained by using the characteristic that the photon arrival time conforms to a non-homogeneous Poisson distribution. S14. Using the inverse function method, the cumulative distribution function of the time interval between photon arrivals is known, and the specific time of photon arrival is calculated. The arrival time of the first photon is set to 0, and the arrival time of each subsequent photon is calculated accordingly. Finally, the photon arrival time sequence of each pixel is obtained.

2. The test method according to claim 1, characterized in that, In step S1, the illumination parameters include the peak quantum efficiency wavelength of the photosensitive device, the photosensitive radius of the photosensitive device, the irradiance corresponding to the highest gray value, and the exposure time window of the photosensitive device.

3. The test method according to claim 1, characterized in that, In step S2, the parameters of the photosensitive device are configured and a corresponding digital model is established. The parameters of the photosensitive device include quantum efficiency and dead time.

4. The test method according to claim 1, characterized in that, In step S3, based on the photon time sequence input from the previous stage, a continuous digital pulse signal of all pixels is output, with each PWM pulse representing a photon signal received by the sensor.

Images