Calibration Method, Device, Equipment and Medium for SNSPD Spectral Response Matrix

Abstract

The present application discloses a calibration method, device, equipment and medium for a SNSPD spectral response matrix, which relates to the field of spectral measurement technology, and includes: inputting the average wavelength of monochromatic light wavelengths into the SNSPD to determine the value interval of the bias current, and dividing the value interval to obtain a unit data interval; sampling the bias current according to a preset multiple of the unit data interval under the selected monochromatic light wavelength, and measuring the first photon count rate mean; determining the data fitting interval using the differential processing result of the first photon count rate mean, and performing data fitting on the first photon count rate mean to obtain the second photon count rate mean; sampling the bias current of the remaining interval, and measuring the third photon count rate mean; splicing the second and third photon count rate means to determine the spectral response rate data based on the splicing result; using the spectral response rate data as a column of the matrix, and reselecting the monochromatic light wavelength until the calibration of the spectral response matrix is ​​completed.

Description

Technical Field

[0001] The present invention relates to the field of spectrum measurement technology, and in particular to a calibration method, device, equipment and medium for a SNSPD spectrum response matrix. Background Art

[0002] The basic principle of "computational spectroscopy measurement technology" is different from that of traditional spectral measurement. It converts the spectral data with the number of spectral channels equal to m into the observed data with the modulation order equal to n through the spectral response characteristics of the spectral detector itself. In the conversion process, the spectral response characteristics of the spectral detector play an important role, which can be represented by a spectral response matrix with n rows and m columns.

[0003] For the computational spectroscopy measurement technology using superconducting nanowire single-photon detector (SNSPD), the number of rows n of the spectral response matrix is ​​achieved by setting n different bias currents of the SNSPD. The more n there are, the higher the accuracy of the data calibration. In the prior art, n is generally about 50 to 100; the number of columns m of the spectral response matrix is ​​achieved by setting m different monochromatic light wavelengths. The more m there are, the higher the accuracy of the wavelength channel calibration. In the prior art, m is generally about 100, so the data scale of the spectral response matrix of the SNSPD is generally between 50×100 and 100×100. For each data point in the matrix, an observation time of 10 seconds / point is generally used to reduce the fluctuation error of the data. If n is 50 and m is 100, the entire spectral response matrix requires approximately 50×100×10=50000 seconds≈13.8 hours or more to complete high-precision calibration, and the calibration efficiency is extremely low. If the number of calibration points is too small, the error of the spectral response matrix will be large, resulting in reduced accuracy of the calculated spectrum measurement. That is, when the existing technology completes the calibration of the spectral response matrix, the calibration time required is too long, which seriously affects the practical application of the calculated spectrum measurement.

[0004] In summary, how to achieve rapid calibration of the SNSPD spectral response matrix without reducing the accuracy of calculated spectral measurements is a problem that needs to be solved. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a method, device, equipment and medium for calibrating the SNSPD spectral response matrix, which can realize the rapid calibration of the SNSPD spectral response matrix without reducing the measurement accuracy of the calculated spectrum. The specific scheme is as follows:

[0006] In a first aspect, the present application discloses a method for calibrating a SNSPD spectral response matrix, comprising:

[0007] Determine the average wavelength of a corresponding number of monochromatic light wavelengths corresponding to the number of columns of the spectral response matrix to be calibrated, and input the average wavelength into the SNSPD to determine the value interval of the bias current, and then divide the value interval based on the number of rows of the spectral response matrix to be calibrated to obtain a unit data interval;

[0008] Selecting any monochromatic light wavelength, sampling the bias current at the monochromatic light wavelength according to a preset multiple of the unit data interval, and measuring the first photon count rate average under each bias current after sampling;

[0009] Determine a data fitting interval in the value interval using a differential processing result of the first photon count rate mean value, and perform data fitting on the first photon count rate mean value within the data fitting interval to obtain a second photon count rate mean value under each bias current after fitting;

[0010] Sampling the bias current of the remaining intervals in the value interval according to the unit data interval, measuring the third photon count rate average under each bias current after sampling, and then splicing the second photon count rate average and the third photon count rate average, so as to determine the spectral response rate data corresponding to each bias current at the wavelength of the monochromatic light based on the splicing result;

[0011] The spectral response rate data is used as a column of the spectral response matrix to be calibrated, and then the process jumps back to the step of selecting any monochromatic light wavelength until all monochromatic light wavelengths are selected to obtain the calibrated spectral response matrix.

[0012] Optionally, determining the data fitting interval in the value interval by using the differential processing result of the first photon count rate mean value includes:

[0013] Performing a first-order differential processing on the first photon counting rate mean to obtain a differential processing result;

[0014] Determine the maximum value in the differential processing result, and select a monotonically increasing interval adjacent to the left side of the maximum value and a monotonically decreasing interval adjacent to the right side of the maximum value;

[0015] A data fitting interval in the value interval is determined based on the monotonically increasing interval and the monotonically decreasing interval.

[0016] Optionally, performing data fitting on the first photon count rate mean within the data fitting interval to obtain a second photon count rate mean under each bias current after fitting includes:

[0017] Determine a preset mathematical model for characterizing the variation law of the photon count rate mean with the bias current, substitute the first photon count rate mean and the corresponding bias current value within the data fitting interval into the mathematical model, and then fit and solve the mathematical model to obtain fitting parameters;

[0018] The data fitting interval is divided according to the unit data interval to obtain a divided bias current sequence, and then the bias current sequence and the fitting parameters are substituted into the mathematical model to obtain the second photon counting rate mean under each bias current in the bias current sequence.

[0019] Optionally, determining the spectral response rate data corresponding to each bias current at the wavelength of the monochromatic light based on the splicing result includes:

[0020] Determine the number of photons per second of the monochromatic light wavelength incident on the SNSPD, and determine the average value of the photon count rate corresponding to each bias current in the splicing result;

[0021] The spectral response rate data corresponding to each bias current at the wavelength of the monochromatic light is determined by using the number of photons per second and the average of the photon counting rate.

[0022] Optionally, determining the number of photons per second of the monochromatic light wavelength incident on the SNSPD includes:

[0023] The power value of the optical power meter recorded in advance is determined, and the number of photons per second of the monochromatic light wavelength incident on the SNSPD is determined using the photon energy formula and the power value.

[0024] Optionally, the calibration method of the SNSPD spectral response matrix further includes:

[0025] An optical beam splitter is used to control the wavelength of the monochromatic light to be input into the optical power meter and the SNSPD respectively according to a preset energy ratio, and the current power value of the optical power meter is recorded.

[0026] Optionally, the splicing of the second photon count rate mean value and the third photon count rate mean value comprises:

[0027] Determine the growth order of the corresponding bias currents in the data fitting interval and the remaining interval, and splice the second photon count rate mean value and the third photon count rate mean value according to the growth order.

[0028] In a second aspect, the present application discloses a calibration device for a SNSPD spectral response matrix, comprising:

[0029] An interval division module, used to determine the average wavelength of a corresponding number of monochromatic light wavelengths corresponding to the number of columns of the spectral response matrix to be calibrated, and input the average wavelength into the SNSPD to determine the value interval of the bias current, and then divide the value interval based on the number of rows of the spectral response matrix to be calibrated to obtain a unit data interval;

[0030] A first sampling module, used for selecting any monochromatic light wavelength, sampling the bias current at the monochromatic light wavelength according to a preset multiple of the unit data interval, and measuring a first photon count rate average under each bias current after sampling;

[0031] A data fitting module, used to determine a data fitting interval in the value interval by using a differential processing result of the first photon counting rate mean value, and perform data fitting on the first photon counting rate mean value within the data fitting interval to obtain a second photon counting rate mean value under each bias current after fitting;

[0032] A second sampling module, used for sampling the bias current of the remaining intervals in the value interval according to the unit data interval, and measuring the average value of the third photon counting rate under each bias current after sampling;

[0033] A spectral response rate determination module, used for splicing the second photon count rate mean value and the third photon count rate mean value, so as to determine the spectral response rate data corresponding to each bias current at the wavelength of the monochromatic light based on the splicing result;

[0034] The matrix calibration module is used to use the spectral response rate data as a column of the spectral response matrix to be calibrated, and then jump back to the step of selecting any monochromatic light wavelength until all monochromatic light wavelengths are selected to obtain the calibrated spectral response matrix.

[0035] In a third aspect, the present application discloses an electronic device, comprising:

[0036] Memory, used to store computer programs;

[0037] The processor is used to execute the computer program to implement the steps of the SNSPD spectral response matrix calibration method disclosed above.

[0038] In a fourth aspect, the present application discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the steps of the aforementioned method for calibrating the SNSPD spectral response matrix are implemented.

[0039] It can be seen that the present application determines the average wavelength of a corresponding number of monochromatic light wavelengths corresponding to the number of columns of the spectral response matrix to be calibrated, and inputs the average wavelength into the SNSPD to determine the value interval of the bias current, and then divides the value interval based on the number of rows of the spectral response matrix to be calibrated to obtain a unit data interval; selects any monochromatic light wavelength, and samples the bias current at the monochromatic light wavelength according to a preset multiple of the unit data interval, and measures the first photon count rate mean under each bias current after sampling; uses the differential processing result of the first photon count rate mean to determine the data fitting interval in the value interval, and performs differential processing on the first light within the data fitting interval. The method comprises the steps of: performing data fitting on the second photon counting rate mean to obtain a second photon counting rate mean under each bias current after fitting; sampling the bias current of the remaining intervals in the value interval according to the unit data interval, measuring a third photon counting rate mean under each bias current after sampling, and then splicing the second photon counting rate mean and the third photon counting rate mean, so as to determine the spectral response rate data corresponding to each bias current under the monochromatic light wavelength based on the splicing result; taking the spectral response rate data as a column of the spectral response matrix to be calibrated, and then jumping back to the step of selecting any monochromatic light wavelength, until all monochromatic light wavelengths are selected to obtain the calibrated spectral response matrix. It can be seen that the present application first inputs the average wavelength of the monochromatic light wavelength into the SNSPD to determine the value range of the bias current, and then divides the unit data interval of the value range according to the number of rows of the spectral response matrix to be calibrated; then, under any selected monochromatic light wavelength, the bias current is sampled according to a preset multiple of the unit data interval, and the first photon count rate mean under each bias current after sampling is measured, so as to achieve coarse sampling of the photon count rate data and obtain sparsely spaced photon count rate data; then, the first photon count rate mean obtained after coarse sampling is differentiated to determine the data fitting interval in the value range, and the first photon count rate mean in the data fitting interval is obtained. Data fitting is performed to obtain the second photon count rate mean under each bias current after fitting. Through fitting, densely spaced photon count rate data after fitting can be obtained from sparsely spaced photon count rate data, thereby saving a lot of calibration time; in addition, the remaining intervals in the value interval except the data fitting interval are sampled using unit data intervals to obtain the corresponding third photon count rate mean, and then the second photon count rate mean and the third photon count rate mean are spliced ​​to determine the spectral response rate data corresponding to each bias current under the monochromatic light wavelength based on the splicing result; finally, the monochromatic light wavelength is reselected, and the above steps are repeated until the calibration of the entire spectral response matrix is ​​completed.In this way, the present application can realize the rapid calibration of the SNSPD spectral response matrix by fitting the data in the data fitting interval, and since the data scale of the spectral response matrix finally obtained is not reduced, the measurement accuracy of the calculated spectrum is not affected. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0041] Figure 1 A working principle diagram of an existing spectral response matrix calibration disclosed in this application;

[0042] Figure 2 A flow chart of a calibration method for a SNSPD spectral response matrix disclosed in this application;

[0043] Figure 3 A flow chart of a specific SNSPD spectral response matrix calibration method disclosed in this application;

[0044] Figure 4 A flow chart of another specific SNSPD spectral response matrix calibration method disclosed in this application;

[0045] Figure 5 A schematic diagram of the structure of a calibration device for a SNSPD spectral response matrix disclosed in this application;

[0046] Figure 6 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0048] The "computational spectroscopy measurement technology" uses the spectral response characteristics of the spectral detector itself to convert the spectral data x to be measured with a spectral channel number equal to m into [x 1 ,x 2 ,…x m ], converted into observation data y=[y 1 ,y2 ,…y n ]. In the process of converting x into y, the spectral response characteristics of the spectral detector play an important role. This response characteristic can be represented by a spectral response matrix Φ with n rows and m columns:

[0049]

[0050] At this point, the process of calculating the spectrum measurement to convert x into y can be expressed as the mathematical formula y = Φx. Finally, by solving x = Φ -1 The mathematical inverse problem of y can be used to recover the measured spectral data x from the observed data y. From the above basic principle description, it can be seen that before "computing spectral measurement", it is necessary to pre-measure the value of the spectral response matrix Φ of the spectral detector, that is, pre-measure each φ in the spectral response matrix Φ of n rows and m columns. ji The numerical value of (where j = 1, 2, ..., n; i = 1, 2, ..., m). Only by knowing the real data of the spectral response matrix Φ can the spectral data x to be measured be restored from the observed data y.

[0051] For the computational spectroscopy measurement technology using superconducting nanowire single-photon detectors (SNSPD), the number of rows n of the spectral response matrix Φ is achieved by setting n different bias currents of the SNSPD. The more n there are, the higher the accuracy of the data calibration. In the prior art, n is generally around 50 to 100; the number of columns m of the spectral response matrix Φ is achieved by setting m different monochromatic light wavelengths. The more m there are, the higher the accuracy of the wavelength channel calibration. In the prior art, m is generally around 100, so the data scale of the spectral response matrix Φ of the SNSPD is generally between 50×100 and 100×100. For each data point in the matrix Φ, an observation time of 10 seconds per point is generally used to reduce the fluctuation error of the data.

[0052] From the above content, it can be known that the key to "computational spectroscopy measurement technology" is to convert the measured spectral data x into the observed data y, and it is necessary to pre-calibrate the spectral response matrix Φ involved in the conversion process. The working principle of the existing technology to calibrate the spectral response matrix Φ is as follows Figure 1 The steps are as follows:

[0053] Step 1. Connect the power supply to the SNSPD. Assuming that the number of rows of the spectral response matrix Φ to be calibrated is n, it is necessary to set n uniformly increasing bias currents. Among them, the minimum value of the bias current is the bias current value when the photon count rate of the SNSPD changes from a stable dark count to a photon response count and a counting rate inflection point is generated; the maximum value of the bias current is the bias current when the photon count rate of the SNSPD gradually saturates and no longer increases. First, select one of the bias currents.

[0054] Step 2: Input the laser light of the tunable laser into the SNSPD. Assuming that the number of columns of the planned calibrated spectral response matrix Φ is m, it is necessary to set up m monochromatic lights with uniformly increasing wavelengths. The minimum and maximum wavelengths can be selected according to our actual needs, generally between 400nm-2000nm. First, select one of the monochromatic light wavelengths. Before the monochromatic wavelength is input into the SNSPD, we use an optical beam splitter with an energy ratio of 50:50 to input half of the light energy into a high-precision optical power meter, and the other half of the light energy into the SNSPD. In this way, by recording the power value of the optical power meter, the number of photons per second N incident on the SNSPD can be converted. in .

[0055] Step 3: Input a bias current selected in step 1 and a monochromatic light selected in step 2 to the SNSPD.

[0056] Step 4: Use a photon counter to record the number of photon pulses per second output by the SNSPD. out .

[0057] Step 5: Calculate the spectral response rate φ at a certain monochromatic wavelength and a certain bias current ji =N out / N in , which is an element of the spectral response matrix Φ.

[0058] Finally, to complete each element of the spectral response matrix Φ ji To calibrate, it is necessary to repeat steps 1 to 5 above, changing m monochromatic light wavelengths and n bias currents one by one, that is, under the first monochromatic light wavelength, record the spectral response rate φ of n bias currents. 11 ,φ 21 ,…,φ n1 After recording, change the second monochromatic light wavelength and record the spectral response rate φ of n bias currents again. 12 ,φ 22 ,…,φ n2; Repeat this process until the calibration of the entire n×m-dimensional spectral response matrix Φ is completed. If n is 50 and m is 100, each data point in the matrix Φ requires an observation time of 10 seconds / point. Then, it takes approximately 50×100×10=50000 seconds≈13.8 hours to complete the high-precision calibration of the entire spectral response matrix Φ, and the calibration efficiency is extremely low. If the number of calibration points is too small, the error of the spectral response matrix Φ will be large, resulting in a decrease in the accuracy of the calculated spectral measurement. In other words, when the prior art completes the calibration of the spectral response matrix Φ, the calibration time required is too long, which seriously affects the practical application of the calculated spectral measurement.

[0059] To this end, the embodiments of the present application disclose a method, device, equipment and medium for calibrating the SNSPD spectral response matrix, which can realize the rapid calibration of the SNSPD spectral response matrix without reducing the measurement accuracy of the calculated spectrum.

[0060] See also Figure 2 As shown, the embodiment of the present application discloses a method for calibrating a SNSPD spectral response matrix, the method comprising:

[0061] Step S11: determine the average wavelength of a corresponding number of monochromatic light wavelengths corresponding to the number of columns of the spectral response matrix to be calibrated, and input the average wavelength into the SNSPD to determine the value range of the bias current, and then divide the value range based on the number of rows of the spectral response matrix to be calibrated to obtain a unit data interval.

[0062] In this embodiment, it should be pointed out that when calibrating the spectral response matrix Φ of the SNSPD, it is necessary to set n different bias currents that increase gradually and m different monochromatic light wavelengths that increase gradually to complete the calibration of the n×m dimensional spectral response matrix Φ. That is, the number of bias currents corresponds to the number of rows n of the matrix, and the number of monochromatic lights corresponds to the number of columns m of the matrix, wherein the minimum and maximum values ​​of the monochromatic light wavelengths can be selected according to actual needs, and then the average wavelength is determined. Assume that the range of the m gradually increasing monochromatic light wavelengths to be calibrated is λ min To max , select their average wavelength, that is, 0.5×(λ min +λ max ) is input into SNSPD to determine the value range of bias current. Specifically, after the average wavelength is input into SNSPD, the bias current is adjusted to gradually increase from 0 microamperes, and the change of the photon count rate output by SNSPD is observed. The bias current value I is recorded when the photon count rate of SNSPD changes from a stable dark count to a photon response count and a counting rate inflection point is generated. min ; Record the bias current I when the photon count rate of the SNSPD gradually saturates and no longer increases max . Will I min and Imax As the minimum and maximum values ​​of the bias current, the bias current value interval is obtained. Then, the value interval is divided based on the number of rows of the spectral response matrix to be calibrated to obtain the unit data interval, that is, the bias current value interval is divided into n equal parts, and the data interval between adjacent bias currents, that is, the unit data interval is (I max -I min ) / n.

[0063] Step S12: selecting any monochromatic light wavelength, sampling the bias current at the monochromatic light wavelength according to a preset multiple of the unit data interval, and measuring the average value of the first photon count rate under each bias current after sampling.

[0064] In this embodiment, any monochromatic light wavelength is selected from m different monochromatic light wavelengths that gradually increase, and the bias current is sampled at the selected monochromatic light wavelength according to a preset multiple of the unit data interval. For example, the preset multiple can be set to 4, so that the data interval between adjacent bias currents after sampling is equal to 4 (I max -I min ) / n, which is 4 times larger than the unit data interval. That is to say, under the selected i-th (i∈[1,m]) monochromatic light wavelength, I min To I max The bias current is divided into n / 4 gradually increasing values ​​I 1 ,I 2 ,…,I (n / 4) , and when the bias current is gradually increased, the 10-second average photon count rate N under each bias current is recorded out1 ,N out2 ,…,N (out_n / 4) , to obtain the first photon count rate average under each bias current after sampling. It should be noted that the preset multiple can be any value between 2 and 6 times. For example, the number of bias current divisions is usually around 100. A 4-fold interval only requires 100 / 4=25 bias currents to be divided, and a 5-fold interval only requires 20 bias currents to be divided. In addition, when selecting the wavelength of monochromatic light, the wavelength can be selected in ascending order, that is, from λ min Start until λ is selected max You can also select them in descending order of wavelength, or randomly, as long as all the monochromatic light wavelengths are selected.

[0065] Step S13: Determine a data fitting interval in the value interval using the differential processing result of the first photon count rate mean, and perform data fitting on the first photon count rate mean within the data fitting interval to obtain a second photon count rate mean under each bias current after fitting.

[0066] In this embodiment, the first photon count rate mean is differentiated to obtain a corresponding differential processing result, and then the fitting interval is divided according to the differential processing result to determine the data fitting interval S for data fitting from the value interval, and then the first photon count rate mean within the data fitting interval S is data fitted to obtain the second photon count rate mean under each bias current after fitting. Specifically, when the preset multiple is 4, the corresponding fitting independent variable is the bias current I 1 ,I 2 ,…,I (n / 4) , the dependent variable is the mean photon count rate N out1 ,N out2 ,…,N (out_n / 4) That is, since the data interval after coarse sampling is 4 (I max -I min ) / n, so the purpose of fitting is to increase the data density of the coarse sampling of n / 4 values ​​to the same as the data density of the sampling of n points, while not affecting the accuracy of the calculated spectrum reconstruction. Through fitting, the densely spaced photon count rate data after fitting can be obtained from the sparsely spaced photon count rate data, thus saving a lot of calibration time.

[0067] Step S14: sampling the bias current of the remaining intervals in the value interval according to the unit data interval, and measuring the third photon count rate average under each bias current after sampling, and then splicing the second photon count rate average and the third photon count rate average, so as to determine the spectral response rate data corresponding to each bias current at the monochromatic light wavelength based on the splicing result.

[0068] In this embodiment, the data fitting interval S divided above does not cover the entire value interval of the bias current, that is, it does not cover I min To I max If the range is within the range of , the remaining interval in the value interval that is not covered by the data fitting interval is called the remaining interval L. It should be pointed out that the remaining interval L includes the area with small bias current and the area with large bias current. Since the mean fluctuation of the photon count rate in these two areas is large, multiple data intervals cannot be used for low-density sampling. Instead, it is necessary to use (I max -I min ) / n high-density intervals are used to sample the photon counting rate mean data, and then the third photon counting rate mean under each bias current in the remaining interval L is measured. That is, this embodiment needs to use high-density sampling for the front section of the bias current sequence, low-density sampling for the middle section, and high-density sampling for the end section.

[0069] The interval obtained by fitting in the data fitting interval obtained through the above steps is equal to (I max -I min) / n, and the intervals in the remaining intervals are equal to (I max -I min ) / n, and then by splicing the second photon counting rate mean and the third photon counting rate mean, the interval equal to (I max -I min ) / n photon counting rate average data, that is, the splicing result is obtained. Then, based on the splicing result, the spectral response rate data corresponding to each bias current under the monochromatic light wavelength is determined.

[0070] In a specific embodiment, the above-mentioned determination of the spectral response rate data corresponding to each bias current at the monochromatic light wavelength based on the splicing result includes: determining the number of photons per second of the monochromatic light wavelength incident on the SNSPD, and determining the average photon count rate corresponding to each bias current in the splicing result; using the number of photons per second and the average photon count rate to determine the spectral response rate data corresponding to each bias current at the monochromatic light wavelength. It can be understood that this embodiment needs to determine the number of photons per second N of the monochromatic light wavelength incident on the SNSPD. in , and determine the average photon count rate N corresponding to the monochromatic light wavelength and each bias current of the splicing result out , the spectral response rate data φ corresponding to each bias current at the wavelength of the monochromatic light can be determined according to the number of photons per second and the average photon counting rate ji The specific formula is:

[0071] φ ji =N out / N in ;

[0072] That is, the spectral response rate data φ corresponding to n bias currents at the monochromatic light wavelength can be obtained. 1i ,φ 2i ,…,φ ni .

[0073] Further, the above determination of the number of photons per second of the monochromatic light wavelength incident on the SNSPD specifically includes: determining the power value of the pre-recorded optical power meter, and using the photon energy formula and the power value to determine the number of photons per second of the monochromatic light wavelength incident on the SNSPD. In addition, the above method also includes: using an optical beam splitter to control the monochromatic light wavelength to be input into the optical power meter and the SNSPD respectively according to a preset energy ratio, and recording the current power value of the optical power meter. It can be understood that before the monochromatic light wavelength is input into the SNSPD, the optical beam splitter will be used to control the monochromatic light wavelength to be input into the optical power meter and the SNSPD respectively according to a preset energy ratio. In a specific embodiment, an optical beam splitter with a preset energy ratio of 50:50 is used, half of the light energy is input into the high-precision optical power meter, and the other half of the light energy is input into the SNSPD, and the current power value P of the optical power meter is recorded. Then the photon energy formula and the power value can be used to determine the number of photons per second of the monochromatic light wavelength incident on the SNSPD.

[0074] The formula for photon energy is:

[0075]

[0076] The number of photons per second incident on the SNSPD is N in for:

[0077]

[0078] Among them, h is Planck's constant, c is the speed of light, and λ is the wavelength of light.

[0079] Step S15: taking the spectral response rate data as a column of the spectral response matrix to be calibrated, and then jumping back to the step of selecting any monochromatic light wavelength, until all monochromatic light wavelengths are selected to obtain the calibrated spectral response matrix.

[0080] In this embodiment, the spectral response rate data corresponding to n bias currents under a certain monochromatic light wavelength has been obtained. 1i ,φ 2i ,…,φ ni , but this is only one column of the spectral response matrix to be calibrated. Therefore, it is necessary to continue to change the new monochromatic light wavelength, that is, reselect the new monochromatic light wavelength, and repeat the above steps to obtain the values ​​of each column of the spectral response matrix to be calibrated to complete the calibration of the entire spectral response matrix. Since the data scale of the spectral response matrix obtained in the end has not been reduced and is still n×m dimensional, it does not affect the accuracy of the calculated spectrum measurement.

[0081] It can be seen that the present application determines the average wavelength of a corresponding number of monochromatic light wavelengths corresponding to the number of columns of the spectral response matrix to be calibrated, and inputs the average wavelength into the SNSPD to determine the value interval of the bias current, and then divides the value interval based on the number of rows of the spectral response matrix to be calibrated to obtain a unit data interval; selects any monochromatic light wavelength, and samples the bias current at the monochromatic light wavelength according to a preset multiple of the unit data interval, and measures the first photon count rate mean under each bias current after sampling; uses the differential processing result of the first photon count rate mean to determine the data fitting interval in the value interval, and performs differential processing on the first light within the data fitting interval. The method comprises the steps of: performing data fitting on the second photon counting rate mean to obtain a second photon counting rate mean under each bias current after fitting; sampling the bias current of the remaining intervals in the value interval according to the unit data interval, measuring a third photon counting rate mean under each bias current after sampling, and then splicing the second photon counting rate mean and the third photon counting rate mean, so as to determine the spectral response rate data corresponding to each bias current under the monochromatic light wavelength based on the splicing result; taking the spectral response rate data as a column of the spectral response matrix to be calibrated, and then jumping back to the step of selecting any monochromatic light wavelength, until all monochromatic light wavelengths are selected to obtain the calibrated spectral response matrix. It can be seen that the present application first inputs the average wavelength of the monochromatic light wavelength into the SNSPD to determine the value range of the bias current, and then divides the unit data interval of the value range according to the number of rows of the spectral response matrix to be calibrated; then, under any selected monochromatic light wavelength, the bias current is sampled according to a preset multiple of the unit data interval, and the first photon count rate mean under each bias current after sampling is measured, so as to achieve coarse sampling of the photon count rate data and obtain sparsely spaced photon count rate data; then, the first photon count rate mean obtained after coarse sampling is differentiated to determine the data fitting interval in the value range, and the first photon count rate mean in the data fitting interval is obtained. Data fitting is performed to obtain the second photon count rate mean under each bias current after fitting. Through fitting, densely spaced photon count rate data after fitting can be obtained from sparsely spaced photon count rate data, thereby saving a lot of calibration time; in addition, the remaining intervals in the value interval except the data fitting interval are sampled using unit data intervals to obtain the corresponding third photon count rate mean, and then the second photon count rate mean and the third photon count rate mean are spliced ​​to determine the spectral response rate data corresponding to each bias current under the monochromatic light wavelength based on the splicing result; finally, the monochromatic light wavelength is reselected, and the above steps are repeated until the calibration of the entire spectral response matrix is ​​completed.In this way, the present application can realize the rapid calibration of the SNSPD spectral response matrix by fitting the data in the data fitting interval, and since the data scale of the spectral response matrix finally obtained is not reduced, the measurement accuracy of the calculated spectrum is not affected.

[0082] See also Figure 3 and Figure 4 As shown, the embodiment of the present application discloses a specific method for calibrating the SNSPD spectral response matrix. Compared with the previous embodiment, this embodiment further illustrates and optimizes the technical solution. Specifically, it includes:

[0083] Step S21: determine the average wavelength of a corresponding number of monochromatic light wavelengths corresponding to the number of columns of the spectral response matrix to be calibrated, and input the average wavelength into the SNSPD to determine the value range of the bias current, and then divide the value range based on the number of rows of the spectral response matrix to be calibrated to obtain a unit data interval.

[0084] Step S22: selecting any monochromatic light wavelength, sampling the bias current at the monochromatic light wavelength according to a preset multiple of the unit data interval, and measuring the average first photon count rate under each bias current after sampling.

[0085] Step S23: Perform a first-order differential processing on the first photon count rate mean to obtain a differential processing result, determine the maximum value in the differential processing result, and select a monotonically increasing interval adjacent to the left side of the maximum value and a monotonically decreasing interval adjacent to the right side of the maximum value, and then determine the data fitting interval in the value interval based on the monotonically increasing interval and the monotonically decreasing interval.

[0086] In this embodiment, the first photon counting rate mean value can be subjected to first-order differential processing, and the first photon counting rate mean value N measured when the preset multiple is 4 times is obtained. out1 ,N out2 ,…,N (out_n / 4) For example, this embodiment requires N out1 ,N out2 ,…,N (out_n / 4) Find the first-order differential and get the corresponding D 1 ,D 2 ,…,D n / 4-1 , the first-order differential here is the discrete difference, the formula is: D 1 =(N out2 -N out1 ) / (I 2 -I 1 ), D i =(N out_i+1 -N out_i ) / (I i+1-I i ), and so on.

[0087] Further, determine the differential processing result D 1 ,D 2 ,…,D n / 4-1 The maximum value in , select the monotonically increasing interval adjacent to the left side of the maximum value and the monotonically decreasing interval adjacent to the right side of the maximum value, that is, starting from the maximum value, select the monotonically increasing interval S of the first-order differential to the left 1 , select the monotonically decreasing interval S of the first-order differential to the right 2 , S 1 and S 2 The intervals are combined into a large interval S, and this large interval is a data fitting interval for data fitting. That is, in this embodiment, the first-order differential of the photon count rate data obtained by coarse sampling is obtained to obtain the interval for data fitting.

[0088] In addition, when selecting the interval for data fitting, in addition to judging by the monotonically increasing and decreasing intervals of the first-order differential of the coarse sampling data, the maximum value of the photon counting rate can also be used as a reference to divide the fitting interval according to the percentage of the maximum value. For example, the bias current interval corresponding to the a% to b% of the maximum value of the photon counting rate is identified as the fitting interval, where 0 <a<40,60<b<100。

[0089] Step S24: determine a preset mathematical model for characterizing the variation law of the mean photon count rate with the bias current, substitute the first photon count rate mean value and the corresponding bias current value within the data fitting interval into the mathematical model, and then fit and solve the mathematical model to obtain fitting parameters.

[0090] In this embodiment, it should be pointed out that the variation law of the photon counting rate mean value N with the bias current I can be described by the Fano wave theory, that is, it can be described by the following "Gaussian error function" mathematical model:

[0091]

[0092] Where N(I) is the mean photon count rate N as the bias current I changes, A is the amplitude parameter of the fit, and σ is the variance parameter of the fit. is the average bias current parameter of the fit.

[0093] In this embodiment, in order to accurately fit, the data fitting interval can be fitted using the "Gaussian error function" mathematical model in Fano wave theory to obtain densely spaced photon counting rate data. Specifically, taking the preset multiple as four times as an example, the first photon counting rate mean N measured by coarse sampling can be out1,N out2 ,…,N (out_n / 4) and the corresponding gradually increasing bias current value I 1 ,I 2 ,…,I (n / 4) Substitute the mathematical model into the above model and use the general mathematical fitting tool to solve the mathematical model to solve the fitting parameters A, σ,

[0094] In addition, when performing data fitting, in addition to using the fitting method of the mathematical model, high-order Newton interpolation can also be used for data interpolation fitting, that is, directly inserting a number of uniformly changing data between two original data points; and fitting methods such as data fitting based on neural networks. As long as the first photon count rate mean and the corresponding bias current value can be fitted to obtain the second photon count rate mean under each bias current in the bias current sequence divided according to the unit data interval.

[0095] Step S25: Divide the data fitting interval according to the unit data interval to obtain a divided bias current sequence, and then substitute the bias current sequence and the fitting parameters into the mathematical model to obtain the second photon counting rate mean under each bias current in the bias current sequence.

[0096] In this embodiment, the data fitting interval is divided according to the unit data interval to obtain the bias current sequence I after division. n , and then the bias current sequence I n and fitting parameters A, σ, Substitute it into the mathematical model to obtain the second photon counting rate mean under each bias current in the bias current sequence, that is, the second photon counting rate mean N corresponding to each bias current in the unit data interval can be obtained.

[0097] Step S26: sampling the bias current of the remaining intervals in the value interval according to the unit data interval, and measuring the third photon count rate mean under each bias current after sampling, and then determining the growth order of the corresponding bias currents in the data fitting interval and the remaining interval, and splicing the second photon count rate mean and the third photon count rate mean according to the growth order.

[0098] In this embodiment, when splicing the second photon count rate mean value in the data fitting interval and the third photon count rate mean value in the remaining interval, the splicing is performed in the order of increase of the corresponding bias currents in the data fitting interval and the remaining interval.

[0099] Step S27: taking the spectral response rate data as a column of the spectral response matrix to be calibrated, and then jumping back to the step of selecting any monochromatic light wavelength, until all monochromatic light wavelengths are selected to obtain the calibrated spectral response matrix.

[0100] For more specific processing procedures of the above steps S21, S22 and S27, reference may be made to the corresponding contents disclosed in the aforementioned embodiments, which will not be described in detail here.

[0101] The embodiment of the present application first uses a 4-fold interval bias current sequence to roughly calibrate the photon count rate data, so that it can be determined which section of the curve of the photon count rate and bias current change can be fitted with the "Gaussian error function". Fitting can obtain dense data from sparse calibration data, thereby saving a lot of calibration time. For data intervals that cannot be fitted, conventional density is used for data sampling, which does not save time. Overall, 43% of the calibration time can be saved in the end, but the specific amount of time saved is determined by the photon counting characteristics of the SNSPD detector.

[0102] Experimental results show that when a bias current sequence with a 4-fold interval is used to roughly calibrate the data and the steps of the method are performed to calibrate the spectral response matrix, 43% of the time can be saved. In addition, the root mean square error (Root Mean Square Error) between the spectral response matrix calibrated by the method and the spectral response matrix calibrated by the prior art is less than 0.2%. At the same time, the final calculated spectral measurement accuracy of the method is consistent with that of the prior art.

[0103] It can be seen that the embodiment of the present application performs a first-order differential processing on the first photon count rate mean value obtained to determine the data fitting interval, that is, to determine which section of the curve of the photon count rate and the bias current change can be used for data fitting. Specifically, the monotonically increasing interval adjacent to the left side of the maximum value in the differential processing result and the monotonically decreasing interval adjacent to the right side can be used as the data fitting interval. In addition, in order to accurately fit, the data fitting interval can be fitted using the "Gaussian error function" mathematical model in Fano wave theory to obtain densely spaced photon count rate data. The densely spaced data obtained by fitting are then spliced ​​with the remaining data obtained by sampling, and the spectral response rate calibration of a specific monochromatic light wavelength is completed by calculating the ratio of the photon count values.

[0104] See also Figure 5 As shown, the embodiment of the present application discloses a calibration device for a SNSPD spectral response matrix, the device comprising:

[0105] An interval division module 11 is used to determine the average wavelength of a corresponding number of monochromatic light wavelengths corresponding to the number of columns of the spectral response matrix to be calibrated, and input the average wavelength into the SNSPD to determine the value interval of the bias current, and then divide the value interval based on the number of rows of the spectral response matrix to be calibrated to obtain a unit data interval;

[0106] A first sampling module 12, used for selecting any monochromatic light wavelength, sampling the bias current at the monochromatic light wavelength according to a preset multiple of the unit data interval, and measuring a first photon count rate average under each bias current after sampling;

[0107] A data fitting module 13 is used to determine a data fitting interval in the value interval by using a differential processing result of the first photon counting rate mean value, and perform data fitting on the first photon counting rate mean value within the data fitting interval to obtain a second photon counting rate mean value under each bias current after fitting;

[0108] The second sampling module 14 is used to sample the bias current of the remaining intervals in the value interval according to the unit data interval, and measure the average value of the third photon counting rate under each bias current after sampling;

[0109] A spectral response rate determination module 15, used for splicing the second photon counting rate mean value and the third photon counting rate mean value, so as to determine the spectral response rate data corresponding to each bias current at the wavelength of the monochromatic light based on the splicing result;

[0110] The matrix calibration module 16 is used to use the spectral response rate data as a column of the spectral response matrix to be calibrated, and then jump back to the step of selecting any monochromatic light wavelength until all monochromatic light wavelengths are selected to obtain the calibrated spectral response matrix.

[0111] Since the embodiments of the device part correspond to the above embodiments, please refer to the description of the embodiments of the method part for the embodiments of the device part, and will not be repeated here.

[0112] It can be seen that the present application determines the average wavelength of a corresponding number of monochromatic light wavelengths corresponding to the number of columns of the spectral response matrix to be calibrated, and inputs the average wavelength into the SNSPD to determine the value interval of the bias current, and then divides the value interval based on the number of rows of the spectral response matrix to be calibrated to obtain a unit data interval; selects any monochromatic light wavelength, and samples the bias current at the monochromatic light wavelength according to a preset multiple of the unit data interval, and measures the first photon count rate mean under each bias current after sampling; uses the differential processing result of the first photon count rate mean to determine the data fitting interval in the value interval, and performs differential processing on the first light within the data fitting interval. The method comprises the steps of: performing data fitting on the second photon counting rate mean to obtain a second photon counting rate mean under each bias current after fitting; sampling the bias current of the remaining intervals in the value interval according to the unit data interval, measuring a third photon counting rate mean under each bias current after sampling, and then splicing the second photon counting rate mean and the third photon counting rate mean, so as to determine the spectral response rate data corresponding to each bias current under the monochromatic light wavelength based on the splicing result; taking the spectral response rate data as a column of the spectral response matrix to be calibrated, and then jumping back to the step of selecting any monochromatic light wavelength, until all monochromatic light wavelengths are selected to obtain the calibrated spectral response matrix. It can be seen that the present application first inputs the average wavelength of the monochromatic light wavelength into the SNSPD to determine the value range of the bias current, and then divides the unit data interval of the value range according to the number of rows of the spectral response matrix to be calibrated; then, under any selected monochromatic light wavelength, the bias current is sampled according to a preset multiple of the unit data interval, and the first photon count rate mean under each bias current after sampling is measured, so as to achieve coarse sampling of the photon count rate data and obtain sparsely spaced photon count rate data; then, the first photon count rate mean obtained after coarse sampling is differentiated to determine the data fitting interval in the value range, and the first photon count rate mean in the data fitting interval is obtained. Data fitting is performed to obtain the second photon count rate mean under each bias current after fitting. Through fitting, densely spaced photon count rate data after fitting can be obtained from sparsely spaced photon count rate data, thereby saving a lot of calibration time; in addition, the remaining intervals in the value interval except the data fitting interval are sampled using unit data intervals to obtain the corresponding third photon count rate mean, and then the second photon count rate mean and the third photon count rate mean are spliced ​​to determine the spectral response rate data corresponding to each bias current under the monochromatic light wavelength based on the splicing result; finally, the monochromatic light wavelength is reselected, and the above steps are repeated until the calibration of the entire spectral response matrix is ​​completed.In this way, the present application can realize the rapid calibration of the SNSPD spectral response matrix by fitting the data in the data fitting interval, and since the data scale of the spectral response matrix finally obtained is not reduced, the measurement accuracy of the calculated spectrum is not affected.

[0113] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Specifically, it may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the calibration method of the SNSPD spectral response matrix performed by the electronic device disclosed in any of the aforementioned embodiments.

[0114] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0115] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 can be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 21 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0116] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon include an operating system 221, a computer program 222 and data 223, etc. The storage method can be temporary storage or permanent storage.

[0117] Among them, the operating system 221 is used to manage and control the hardware devices and computer programs 222 on the electronic device 20, so as to realize the operation and processing of the massive data 223 in the memory 22 by the processor 21, which can be Windows, Unix, Linux, etc. In addition to including a computer program that can be used to complete the calibration method of the SNSPD spectral response matrix performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program that can be used to complete other specific tasks. In addition to data transmitted from an external device received by the electronic device, the data 223 can also include data collected by its own input and output interface 25.

[0118] Furthermore, an embodiment of the present application also discloses a computer-readable storage medium, in which a computer program is stored. When the computer program is loaded and executed by a processor, the method steps performed in the calibration process of the SNSPD spectral response matrix disclosed in any of the aforementioned embodiments are implemented.

[0119] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0120] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0121] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0122] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0123] The above is a detailed introduction to the calibration method, device, equipment and storage medium of the SNSPD spectral response matrix provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A calibration method for the SNSPD spectral response matrix, It is characterized in that include: Determine the average wavelength of a corresponding number of monochromatic light wavelengths corresponding to the number of columns of the spectral response matrix to be calibrated, and input the average wavelength into the SNSPD to determine the value interval of the bias current, and then divide the value interval based on the number of rows of the spectral response matrix to be calibrated to obtain a unit data interval; Selecting any monochromatic light wavelength, sampling the bias current at the monochromatic light wavelength according to a preset multiple of the unit data interval, and measuring the first photon count rate average under each bias current after sampling; Determine a data fitting interval in the value interval using a differential processing result of the first photon count rate mean value, and perform data fitting on the first photon count rate mean value within the data fitting interval to obtain a second photon count rate mean value under each bias current after fitting; Sampling the bias current of the remaining intervals in the value interval according to the unit data interval, measuring the third photon count rate average under each bias current after sampling, and then splicing the second photon count rate average and the third photon count rate average, so as to determine the spectral response rate data corresponding to each bias current at the wavelength of the monochromatic light based on the splicing result; The spectral response rate data is used as a column of the spectral response matrix to be calibrated, and then the process is repeated to the step of selecting any monochromatic light wavelength until all monochromatic light wavelengths are selected to obtain a calibrated spectral response matrix; The step of determining the data fitting interval in the value interval by using the differential processing result of the first photon count rate mean value includes: Performing a first-order differential processing on the first photon counting rate mean to obtain a differential processing result; Determine the maximum value in the differential processing result, and select a monotonically increasing interval adjacent to the left side of the maximum value and a monotonically decreasing interval adjacent to the right side of the maximum value; Determine a data fitting interval in the value interval based on the monotonically increasing interval and the monotonically decreasing interval; The step of performing data fitting on the first photon count rate mean within the data fitting interval to obtain a second photon count rate mean under each bias current after fitting includes: Determine a preset mathematical model for characterizing the variation law of the photon count rate mean with the bias current, substitute the first photon count rate mean and the corresponding bias current value within the data fitting interval into the mathematical model, and then fit and solve the mathematical model to obtain fitting parameters; The data fitting interval is divided according to the unit data interval to obtain a divided bias current sequence, and then the bias current sequence and the fitting parameters are substituted into the mathematical model to obtain the second photon counting rate mean under each bias current in the bias current sequence.

2. The calibration method of the SNSPD spectral response matrix according to claim 1, It is characterized in that The step of determining the spectral response rate data corresponding to each bias current at the wavelength of the monochromatic light based on the splicing result includes: Determine the number of photons per second of the monochromatic light wavelength incident on the SNSPD, and determine the average value of the photon count rate corresponding to each bias current in the splicing result; The spectral response rate data corresponding to each bias current at the wavelength of the monochromatic light is determined by using the number of photons per second and the average of the photon counting rate.

3. The calibration method of the SNSPD spectral response matrix according to claim 2, It is characterized in that Determining the number of photons per second of the monochromatic light wavelength incident on the SNSPD includes: The power value of the optical power meter recorded in advance is determined, and the number of photons per second of the monochromatic light wavelength incident on the SNSPD is determined using the photon energy formula and the power value.

4. The calibration method of the SNSPD spectral response matrix according to claim 3, It is characterized in that Also includes: An optical beam splitter is used to control the wavelength of the monochromatic light to be input into the optical power meter and the SNSPD respectively according to a preset energy ratio, and the current power value of the optical power meter is recorded.

5. A method for calibrating a SNSPD spectral response matrix according to any one of claims 1 to 4, It is characterized in that The step of splicing the second photon count rate mean value and the third photon count rate mean value comprises: Determine the growth order of the corresponding bias currents in the data fitting interval and the remaining interval, and splice the second photon count rate mean value and the third photon count rate mean value according to the growth order.

6. A calibration device for SNSPD spectral response matrix, It is characterized in that include: An interval division module, used to determine the average wavelength of a corresponding number of monochromatic light wavelengths corresponding to the number of columns of the spectral response matrix to be calibrated, and input the average wavelength into the SNSPD to determine the value interval of the bias current, and then divide the value interval based on the number of rows of the spectral response matrix to be calibrated to obtain a unit data interval; A first sampling module, used for selecting any monochromatic light wavelength, sampling the bias current at the monochromatic light wavelength according to a preset multiple of the unit data interval, and measuring a first photon count rate average under each bias current after sampling; A data fitting module, used to determine a data fitting interval in the value interval by using a differential processing result of the first photon counting rate mean value, and perform data fitting on the first photon counting rate mean value within the data fitting interval to obtain a second photon counting rate mean value under each bias current after fitting; A second sampling module, used for sampling the bias current of the remaining intervals in the value interval according to the unit data interval, and measuring the average value of the third photon counting rate under each bias current after sampling; A spectral response rate determination module, used for splicing the second photon count rate mean value and the third photon count rate mean value, so as to determine the spectral response rate data corresponding to each bias current at the wavelength of the monochromatic light based on the splicing result; A matrix calibration module is configured to use the spectral responsivity data as a column of the spectral response matrix to be calibrated, and then jump back to the step of selecting any monochromatic light wavelength until the calibrated spectral response matrix is obtained after all monochromatic light wavelengths are selected; Among them, the data fitting module is specifically configured to perform a first-order differential process on the first photon counting rate mean value to obtain a differential processing result; determine the maximum value in the differential processing result, and select a monotonically increasing interval adjacent to the left side of the maximum value and a monotonically decreasing interval adjacent to the right side of the maximum value; determine a data fitting interval in the value interval based on the monotonically increasing interval and the monotonically decreasing interval; The data fitting module is specifically configured to determine a preset mathematical model for characterizing the variation law of the photon counting rate mean value with the bias current, substitute the first photon counting rate mean value and the corresponding bias current value in the data fitting interval into the mathematical model, and then perform fitting and solution on the mathematical model to obtain fitting parameters; Divide the data fitting interval according to the unit data interval to obtain a divided bias current sequence, and then substitute the bias current sequence and the fitting parameters into the mathematical model to obtain the second photon counting rate mean value corresponding to each bias current in the bias current sequence.

7. An electronic device Characterized in that It includes: A memory for storing a computer program; A processor for executing the computer program to implement the steps of the SNSPD spectral response matrix calibration method according to any one of claims 1 to 5.

8. A computer-readable storage medium Characterized in that For storing a computer program; wherein, when the computer program is executed by a processor, the steps of the SNSPD spectral response matrix calibration method according to any one of claims 1 to 5 are implemented.

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