Whole process simulation method and system for response of semiconductor nuclear radiation detector

Abstract

The invention belongs to the technical field of semiconductor detectors, and particularly relates to a whole process simulation method and system for response of a semiconductor nuclear radiation detector. According to the method, through cooperative operation of a detector working parameter setting module, a carrier transport simulation module and a detection system electronics response module, whole-process response prediction of the semiconductor detector from radiation interaction to electronics output can be realized under different carrier transport characteristics and device structure conditions; according to the method, the carrier drift process is simulated in a small-step iteration mode, accurate reconstruction of a carrier drift track can be synchronously achieved, CSA signals and main amplifier forming signals can be continuously generated in real time, simulation output of an energy spectrum is finally completed, and therefore core performance indexes of a detector are comprehensively reflected. The method is wide in applicable semiconductor material range and device structure, the main amplifier module type can be flexibly configured, the overall simulation process is simple, convenient and efficient, and the method has good universality and engineering application value.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of semiconductor detectors, and particularly relates to a full-process simulation method and system for the response of a semiconductor nuclear radiation detector. BACKGROUND

[0002] Nuclear radiation detectors, as the key core components of nuclear detection technology, have been widely used in many fields such as medical imaging, homeland security, space exploration and basic scientific research. Compared with traditional scintillator detectors, semiconductor detectors have the advantages of high energy resolution, linear energy response, small volume, etc., which can effectively improve the detection accuracy and system integration, and have become an important innovative direction in the field of nuclear radiation detection technology.

[0003] The direct detection of high-energy rays by semiconductor materials is based on the photoelectric effect: high-energy rays interact with semiconductors to produce a large number of electron-hole pairs, which drift to both poles under an applied bias and form an induced signal at the collection electrode. The performance of semiconductor detectors is closely related to the transport and collection of carriers, and the intrinsic carrier transport characteristics of semiconductor materials are the key factors that determine the performance of the detector. The higher the mobility (μ) and the longer the lifetime (τ) of the semiconductor material, the higher the collection efficiency of photo-generated carriers and the better the energy spectrum resolution performance. In traditional compound semiconductors, the transport performance of electrons and holes usually differs significantly, for example, in CdZnTe semiconductors, the electron mobility-lifetime product (μτ e ) is as high as ~1×10 -2 cm 2 ·V -1 , while the hole μτ h is only 10 -4 ~10 -5 cm 2 ·V -1 , which is easily trapped and its drift contribution can be ignored, and the signal size is generated by the drift of the main carrier electrons, resulting in significant low-energy tailing in the energy spectrum of planar detectors. Single-polarity device design makes the distribution of the weight potential near the collection electrode weighted, so that the detector signal only depends on the collection of the main carrier, which can significantly reduce the adverse effects of carriers with poor transport performance. It is a common and effective way to improve the energy spectrum resolution performance of semiconductor detectors.

[0004] With the continuous progress of material engineering technology, a large number of new semiconductor materials have been developed and shown good application potential. However, the intrinsic transport characteristics of different materials are significantly different, and there is still a certain lack of adaptability in device structure design, which limits the full play of its performance. Especially for new perovskite semiconductors, their hole transport performance is better than that of electrons, which is significantly different from traditional compound semiconductors. The energy resolution of unipolar devices based on hole collection is as high as 1.4% at 662keV, which is comparable to commercial CZT (cadmium zinc telluride), and shows broad application prospects.

[0005] In recent years, with the continuous optimization of crystal growth process and defect control technology, the electron transport performance of perovskite semiconductors has also been significantly improved, making it possible to become a new generation of double-carrier compound semiconductors. However, the coupling between the double-carrier transport characteristics and the weight potential distribution in different device structures makes the energy spectrum response characteristics more complex, bringing new challenges to device structure design and performance optimization in different application scenarios.

[0006] In summary, the performance of semiconductor detectors is not only affected by the intrinsic properties of the material, but also closely related to complex physical effects and changes in device structure. At present, for new semiconductor materials or new device structures, it is often difficult to accurately reflect their performance in actual application scenarios by measuring carrier transport performance and electrode geometry parameters to predict detector response. To accurately understand the response characteristics of the detector, it is usually necessary to manufacture actual detector devices and conduct experimental verification. However, this process not only takes time, significantly prolonging the development and application verification period of new detectors, but also increases experimental costs and economic burden.

[0007] At present, numerical simulation methods play an increasingly important role in the research and development of semiconductor detectors, and have become a key tool for promoting device structure optimization and performance evaluation. By establishing a physical model based on the intrinsic properties of the material and the geometry of the device, the charge generation, drift, diffusion, recombination and collection kinetic processes can be systematically analyzed without the need to manufacture actual detectors, and the performance can be predicted in combination with pulse signal formation and energy spectrum response characteristics. This not only significantly reduces the research and development cost and cycle, but also provides theoretical support for the application prospect evaluation of new semiconductor materials in high-energy detection, and has important scientific significance and engineering value for the design and performance optimization of innovative detector structures.

[0008] For semiconductor radiation detectors, the working process mainly includes the following three steps: (1) interaction between rays and matter, rays deposit energy in the detector and generate electron-hole pairs; (2) under the action of external bias, electrons and holes drift in opposite directions; (3) when the charges move in the detector, induced current is generated on the electrode, and the voltage signal is output after being amplified by the charge sensitive preamplifier (CSA), the amplitude and rise time of which can reflect the re-carrier transport and collection characteristics under the structure of the device. Since the number of photo-generated carriers in semiconductor materials is proportional to the energy of incident photons, and the amplitude of the voltage signal is proportional to the number of photo-generated carriers, therefore, by measuring the amplitude of the CSA output signal, the energy of the incident photons can be obtained, and photon energy measurement can be realized.

[0009] In actual nuclear detection systems, the voltage signal obtained by the CSA usually has a low signal-to-noise ratio (SNR). Therefore, the CSA signal needs to be input into the main amplifier for further filtering and shaping processing to improve the signal-to-noise ratio of the signal. After processing by the main amplifier, a signal with higher signal-to-noise ratio can be obtained. By statistically analyzing the amplitudes of these high signal-to-noise ratio signals, the final energy spectrum response results can be obtained. The energy spectrum results are the key basis for evaluating and analyzing the characteristics and performance of radiation detectors.

[0010] Overall, semiconductor detectors involve a series of mutually coupled physical processes in the process of realizing photon energy detection. However, existing simulation studies of semiconductor detectors still mainly focus on some local links, such as the energy deposition process of high-energy rays in semiconductor materials or the mechanism of the initial charge generation pulse signal, and have not effectively integrated the key filtering, shaping and amplification processes in the subsequent signal processing circuit, resulting in that the simulation results are difficult to fully reflect the response characteristics of the actual detection system, thereby limiting the accurate evaluation of the overall performance of the device. Especially for new semiconductor materials represented by perovskite with special transport performance, there is still a lack of systematic and perfect simulation methods with universality, which can realize the whole process modeling and prediction analysis from energy deposition to final energy spectrum response.

[0011] Currently, the simulation method for semiconductor detectors has the following problems:

[0012] 1. In the simulation of the detector, the existing semiconductor detector response simulation method only stays at the simulation stage of the CSA output signal, and does not cover the filtering and shaping functions of the main amplifier. This makes it difficult for researchers to obtain energy spectrum data that can be used to evaluate the energy response characteristics of the detector, and the simulation results cannot fully reflect the real response characteristics of the entire detection system, thereby limiting the comprehensive evaluation of the overall performance of the detector.

[0013] 2. Existing simulation methods struggle to capture the gradual charge induction behavior of charge carriers during drift in real time. This limits the real-time monitoring and analysis of the dynamic processes of charge carrier transport within the detector, hindering a deeper understanding of the impact of microscopic processes such as drift paths, changes in device weight potential distribution, and defect trapping on CSA pulse pattern formation. Consequently, this restricts the analysis of detector response mechanisms and the improvement of device structure design.

[0014] 3. In terms of carrier transport algorithms, existing technologies generally do not adopt carrier motion mechanisms based on limited step size, resulting in inaccurate simulation results of carrier transport paths, weakening the quantitative correlation between carrier spatial position and CSA signal, and thus affecting the reliability of waveform formation mechanism and signal feature analysis results. Summary of the Invention

[0015] To address the existing technical problems, this application provides the following technical solution:

[0016] This invention provides a method for simulating the entire process of a semiconductor nuclear radiation detector response, comprising the following steps:

[0017] S11: Obtain the detector's geometric parameters, material properties, and carrier transport characteristics, and calculate the internal potential distribution and weighted potential distribution of each electrode to obtain the detector's physical model.

[0018] S12: Configure the detector physical model using Geant4 as the source parameters for the radiation to obtain energy deposition points and radiation energy deposition distribution data E. d And use electromagnetic field simulation software to output the potential field distribution data of the detector physical model;

[0019] S13: Based on X-ray energy deposition distribution data E d and based on semiconductor average ionization energy Calculate the number N of electron-hole pairs generated at each energy deposition point, and merge the energy deposition points within a preset distance into a voxel charge cloud;

[0020] S14: Based on the electric potential field distribution data and the simulated carrier drift using voxel charge clouds, output the weighted potential change calculated for each drift step. and charge Q;

[0021] S15: Based on the change in weight potential The electrode induced charge q is calculated from the charge Q, and the electrode induced charge q is convolved with the system transfer function of the charge-sensitive preamplifier to generate the original voltage signal; the input terminal of the charge-sensitive preamplifier is connected to the detector;

[0022] S16: applying a filtering algorithm to the original voltage signal to generate a measurable pulse signal;

[0023] S17: constructing a detector energy spectrum curve using the electrode-induced charge quantity q, the original voltage signal, or the amplitude distribution of the measurable pulse signal.

[0024] Preferably, in step S11, the geometric parameters are selected from electrode structure and volume size, the material properties are selected from resistivity p and dielectric constant e, and the carrier transport characteristics are selected from electron mobility lifetime product μτ e and hole mobility lifetime product μτ h .

[0025] Preferably, the electrode structure is planar, pixel, coplanar grid, quasi-hemisphere, Frisch grid, virtual Frisch grid, orthogonal strip, or coaxial.

[0026] Preferably, in step S11, the detector material is an elemental semiconductor, a binary semiconductor, and a ternary semiconductor; the elemental semiconductor is selected from silicon or germanium, the binary semiconductor is selected from cadmium telluride, mercury iodide, or thallium bromide, and the ternary semiconductor is selected from perovskite or cadmium zinc telluride; the perovskite is selected from three-dimensional perovskite, pure inorganic perovskite, two-dimensional perovskite, zero-dimensional perovskite, or inverse perovskite.

[0027] Further, the three-dimensional perovskite is selected from A1B1X13; wherein A1 is selected from one or more of Na, K, Rb, Cs, Cu, Tl, and H3O, B1 is selected from one or more of Pb, Si, Ge, Sn, Mg, Ca, Sr, Ba, Zn, Cd, and Hg, and X1 is selected from one or more of F, Cl, Br, I, BF4, HCOO, OH, CN, SCN, NCS, SH, NO3, and H2POO; the two-dimensional perovskite is selected from TlPbI3 or A23B22X29; wherein A2 is selected from one or both of Cs and Rb, B2 is selected from one or both of Sb and Bi, and X2 is selected from one or more of F, Cl, Br, and I; the zero-dimensional perovskite is selected from Cs3Bi2I9, Cs2TeI6, Cs2TeBr6, or Cs2TeCl6; and the inverse perovskite is selected from Hg3Se2Br2 or TlSn2I5.

[0028] Preferably, in step S11, the electrode material of the detector is one or more of platinum, gold, gallium, indium, bismuth, nickel, lead, and chromium.

[0029] Preferably, in step S12, the type of radiation is gamma rays, X-rays, alpha particles, beta particles, proton, or neutron ionizing radiation.

[0030] Preferably, in step S13, the calculation method of the number of electron-hole pairs N is: wherein, is the average ionization energy of the semiconductor.

[0031] Preferably, in the step S13, the voxel charge cloud is balanced between calculation efficiency and accuracy by adjusting the voxel size. In the acquisition of the simulation data of the interaction between the ray and the material, each energy deposition position recorded in Geant4 can be used as the starting position of the carrier cloud drift. For high-energy rays (such as 662 keV gamma rays), they can undergo multiple Compton scattering in the detector, and finally realize complete energy deposition through photoelectric effect and generate electron-hole pairs, resulting in the formation of a high-density deposition site distribution in the local area. In order to optimize the utilization rate of computing resources, an adjustable voxel strategy is adopted: all deposition sites in a cubic voxel with a spatial size of a × a × a are approximately equivalent to a single deposition site. The method sets the voxel edge length a = 0.1 mm, and quantitative analysis shows that this parameter configuration can reduce the number of calculation events by 50.8%, significantly improving the calculation efficiency.

[0032] Preferably, in the step S14, the charge amount Q is calculated by the Hecht formula: Q k+1 = Q k · exp(-Δx / λ); wherein, Q k+1 is the charge amount at the next step k+1, Q k is the charge amount at the current step k, Δx is the unit step length of movement, and λ is the average drift distance of the carrier.

[0033] Further, λ = μτE; wherein, E is the electric field strength applied to the detector, and μτ is the product of the mobility and the lifetime of the carrier;

[0034] In the step S14, the weight potential change is calculated by the following formula: wherein, is the weight potential value of the coordinate at the kth step, is the weight potential value of the coordinate at the (k-1)th step.

[0035] Preferably, in the step S15, the electrode-induced charge amount q is calculated by the following formula: wherein, Q is the charge amount, is the weight potential change.

[0036] Preferably, in the step S15, the system transfer function of the charge-sensitive preamplifier (CSA) is in the form of exponential decay h(t) = exp(-t / τ), and through Laplace transform, the formula is as follows: wherein, τ = R f · C f , R f is the feedback resistance of the CSA, and Cf is the feedback capacitance of the CSA, τ is the time constant of the CSA, and s is a complex frequency variable.

[0037] After the convolution operation of the arbitrary input signal F(s) and the CSA system response function H(s), the time-domain expression G(t) of the CSA output signal can be obtained by inverse Laplace transform, and the formula is G(t)=L -1 {G(s)}=L -1 {F(s)·H(s)}.

[0038] Preferably, in the step S16, the filtering algorithm is CR-RC 4 shaping algorithm.

[0039] Preferably, in the step S16, the preset sampling frequency of the generated measurable pulse signal is 10MHz.

[0040] In the step S17, the amplitude distribution of the electrode induced charge amount q in the step S15 is counted, and the detector theoretical spectral response can also be constructed according to q.

[0041] The method of the application can be realized as a computer software program, including running on a readable storage medium (and can be stored on a readable computer storage medium).

[0042] The technical details of each module and the data flow are described below.

[0043] The application also provides a full-process simulation system of a semiconductor nuclear radiation detector response, which is used to realize the full-process simulation method of the semiconductor nuclear radiation detector response, and specifically comprises:

[0044] A detector physical model module is used to obtain the geometric parameters, material properties and carrier transport characteristics of the detector, and calculate the internal potential distribution and electrode weight potential distribution of the detector to obtain a detector physical model.

[0045] A distribution data output module is used to configure the detector physical model as a source parameter of a ray by using Geant4, obtain energy deposition point and ray energy deposition distribution data E d , and output the potential field distribution data of the detector physical model by using electromagnetic field simulation software.

[0046] A voxel charge cloud generation module is used to calculate the number of electron-hole pairs N generated by each energy deposition point according to the ray energy deposition distribution data E d and the average ionization energy ε of the semiconductor, and combine the energy deposition points within a preset distance into a voxel charge cloud.

[0047] A drift step length calculation output module is configured to simulate carrier drift according to the potential field distribution data and the voxel charge cloud, and output a weight potential change obtained by each drift step length calculation and a charge quantity Q.

[0048] A raw voltage signal generation module is configured to calculate an electrode induced charge quantity q according to the weight potential change and the charge quantity Q, and to generate a raw voltage signal by convolving the electrode induced charge quantity q and a system transfer function of a charge sensitive preamplifier, an input end of which is connected to the detector.

[0049] A measurable pulse signal generation module is configured to generate a measurable pulse signal by applying a filtering algorithm to the raw voltage signal.

[0050] A detector energy spectrum curve construction module is configured to construct a detector energy spectrum curve according to a distribution of the electrode induced charge quantity q, the raw voltage signal or the measurable pulse signal amplitude. The process of directly constructing an energy spectrum curve based on the inductive charge quantity q obtained by statistical calculation does not involve hardware circuit processing, and can reflect inherent performance characteristics of the detector in an ideal design state. The energy spectrum curve can be used to analyze the influence of electrode structure parameters and material characteristics on the performance of the detector. The raw voltage signal is generated by a charge sensitive preamplifier according to the electrode induced charge quantity q, and is not processed by a filtering shaping algorithm. The detector energy spectrum constructed by the raw voltage signal amplitude can not only more truly reflect the characteristics of the original output of the detector, but also facilitate the analysis of the inherent performance of the detector, and can also evaluate the influence of CSAs with different decay times on the output energy spectrum. In actual operation, the output of the detector will be affected by electronic noise and dark current noise. The filtering shaping algorithm is a necessary processing step for suppressing noise, stabilizing the signal baseline and improving the signal-to-noise ratio (SNR). Therefore, the measurable pulse signal energy spectrum curve constructed by simulation can be closer to the energy spectrum result obtained by actual detector testing.

[0051] The technical scheme of the present application has the following advantages compared with the prior art:

[0052] The present application develops a full-process semiconductor detector response simulation method, which has the following obvious advantages:

[0053] 1. The present application relates to a simulation method for the whole process of the response of a semiconductor detector, which can directly and accurately obtain the energy spectrum response result of the detector based on the geometric model of the detector and the intrinsic transport characteristics of the material, and the simulation result has high consistency with the experimental result, which is beneficial to quickly estimate and judge the response performance of the semiconductor material and the device structure.

[0054] 2、The CSA waveform signal can be obtained in real time in the application, which is beneficial to analyze different carrier transport characteristics, the movement of carriers in the detector under different device structures, and the charge induction and collection.

[0055] 3、The application is based on the working principle of the semiconductor detector, and is suitable for a wide range of semiconductor material systems and device structures.

[0056] 4、The application can realize efficient screening of device structure design schemes based on the intrinsic transport characteristics of semiconductor materials and specific application scenarios, so as to quickly determine the optimal configuration. This not only significantly shortens the development cycle of the detector based on new semiconductor materials, but also effectively reduces the actual trial and error cost, which is of great significance for accelerating the design and engineering application of the detector. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 It is a working principle diagram of the semiconductor detector of the application.

[0058] Figure 2 It is a response simulation method framework flowchart of the semiconductor detector of the application; there is data transmission between different simulation modules; wherein, the detector physical model module and the distribution data output module constitute the detector working parameter setting, the voxel charge cloud generation module and the drift step calculation output module constitute the carrier transport, the original voltage signal generation module, the measurable pulse signal generation module and the detector energy spectrum curve construction module constitute the detector electronics response.

[0059] Figure 3 It is a voxel processing schematic diagram of the detector; a plurality of original deposition sites are merged into an equivalent charge cloud in a specified voxel, and the equivalent charge cloud center coordinates are obtained by energy weighting of the original deposition sites.

[0060] Figure 4 (a) in the figure is a modeling schematic diagram of a pixel type CsPbBr3 detector, (b) is a profile diagram of the weight potential distribution inside the detector, y=2.5mm; the solid points in the figure are the positions of the interaction of part of the rays with the material.

[0061] Figure 5 It is a visualization diagram of the voxel charge cloud generation module and the drift step calculation output module; wherein, (a) is a drift schematic diagram of the carriers under the action of the electric field, (b) is the CSA waveform signal of the plane and the pixel electrode generated by simulating different drift paths, and (c) is the actual test waveform signal corresponding to the pixel detector.

[0062] Figure 6 It is a main amplifier (CR-RC 4The output results of the digital filter); wherein (a) is the main amplifier output waveform, (b) is the pixel type CsPbBr3 detector energy spectrum obtained by statistically processing the amplitudes of the signals of the respective main amplifiers.

[0063] Figure 7 The simulation energy spectrum results of different radioactive sources; wherein (a) is the simulation energy spectrum result of the radioactive source set as 57 Co, the full energy peak energy is 122 keV, (b) is the simulation energy spectrum result of the radioactive source set as 22 Na, the full energy peak energy is 511 keV and 1274 keV, (c) is the simulation energy spectrum result of the radioactive source set as 131 I, the full energy peak energy is 364.5 keV, (d) is the simulation energy spectrum result of the radioactive source set as 99m Tc, the full energy peak energy is 141 keV.

[0064] Figure 8 The energy spectrum results of the pixel type CsPbBr3 detector to 137 Cs source; wherein (a) is the experimental energy spectrum result of the single-dual-polarity pixel detector, (b) is the simulation energy spectrum result of the single-dual-polarity pixel detector.

[0065] Figure 9 (a) in the figure is a schematic diagram of a three-dimensional model of a planar detector, (b) is the energy spectrum result of the planar CsPbBr3 detector to 57 Co source.

[0066] Figure 10 (a) in the figure is a schematic diagram of a quasi-hemispherical CsPbBr3 detector, (b) is a schematic diagram of a cross-section of the model result, and (c) is a weight potential distribution diagram of the cathode of the detector.

[0067] Figure 11 (a to c) in the figure are the induced charge energy spectrum diagrams when the carrier transport performance μτ e = 4 × 10 -3 cm 2 · V -1 , μτ h = 8 × 10 -2 cm 2 · V -1 , and the external voltage is 500 V, 1000 V and 1500 V, respectively; (d to e) are the induced charge energy spectrum diagrams when the carrier transport performance μτ e = 4 × 10 -6 cm 2 · V -1 , μτ h = 8 × 10 -2 cm 2 · V -1 , and the external voltage is 500 V and 1500 V, respectively; (f) is the induced charge energy spectrum diagram when the carrier transport performance μτe = 4 x 10 -4 cm 2 · V -1 , μτ h = 8 x 10 -3 cm 2 · V -1 The induced charge spectrum diagram at 500V external voltage. DETAILED DESCRIPTION

[0068] The present application will be further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand and implement the present application, but the embodiments are not limiting to the present application.

[0069] Example 1:

[0070] This embodiment relates to a full-process simulation method of a semiconductor nuclear radiation detector response, as shown in Figure 1 , comprising the following steps:

[0071] S11: Obtain the geometric parameters, material properties and carrier transport characteristics of the detector, and calculate the internal potential distribution and electrode weight potential distribution of the detector to obtain a detector physical model;

[0072] This embodiment constructs a cubic pixel type radiation detector three-dimensional model based on perovskite cesium lead bromide (CsPbBr3) semiconductor, and the effective detection volume is 5.0mm x 5.0mm x 5.0mm. An array type pixel structure design is adopted, and the single pixel unit size is 0.9mm x 0.9mm, 0.1mm interval is set between the pixel matrix, and a 0.45mm wide protection ring structure is set at the periphery of the detector, as shown in Figure 4 (a). In terms of electrode configuration, gold (Au) is used as the cathode electrode and the protection ring material, and copper (Cu) is used as the anode electrode material, and a cathode-anode potential difference is established by applying a 500V working voltage. Based on this configuration, the internal working electric field distribution of the detector is solved by finite element analysis method, as shown in Figure 4 (b), a strong electric field gradient is formed in the cathode region, showing a significant small pixel effect.

[0073] S12: Configure the detector physical model as the source parameters of the rays to obtain the ray energy deposition distribution data E d and the energy deposition points, and output the potential field distribution data of the detector physical model;

[0074] The model is imported into the Geant4 package for ray and matter interaction simulation. The selected physics list is FTFP_BERT built-in Geant4, which contains basic electromagnetic interaction physics processes. The gamma ray source parameters are set as follows: a single-energy 662 keV ray source is placed 8 mm away from the anode surface, the incident direction is perpendicular to the anode plane, and the medium between the radiation source and the detector is air. Since the interaction process of a single photon with matter is random, in order to ensure statistical significance, the particle number is set to 5 x 10 5 Figure 4

[0075] S13: Calculate the number of electron-hole pairs N generated by each energy deposition point based on the ray energy deposition distribution data E d and the average ionization energy of the semiconductor , and merge adjacent energy deposition points into a voxel charge cloud;

[0076] The potential field distribution and the ray energy deposition distribution data E d are input into the voxel charge cloud generation module and the drift step calculation output module to calculate the carrier charge and the step drift process. First, according to the ray deposition energy and the average ionization energy of the material, the number of electron-hole pairs N generated by ionization inside the detector and their corresponding spatial coordinates P are calculated. The average ionization energy of CsPbBr3 is set to The effective volume of the detector is divided into voxels with a size of 0.1 mm x 0.1 mm x 0.1 mm, and the electron-hole pairs in the same voxel are regarded as a charge cluster, whose spatial coordinates are corrected by the charge weight algorithm.

[0077] S14: Simulate carrier drift based on the potential field distribution data and the voxel charge cloud, and output the weight potential change and the charge Q calculated at each drift step;

[0078] Since electrons and holes have opposite charges, they will drift towards the anode and cathode, respectively, under the action of the electric field. In order to ensure the accuracy of the simulation, the drift step d of the carrier in the detector is set to d ≤ 0.02 x λ, where λ is the average drift free path of the carrier, defined as λ = μτE, where μτ is the mobility-lifetime product of the carrier, and E is the electric field strength inside the detector. Defects inevitably exist in the actual detection material, and when the carrier drifts through the defect region, some charges will be captured, causing the total charge to decay along the drift path, which follows the Hecht formula. The larger the mobility-lifetime product (μτ) of the carrier, the stronger the transport ability of the carrier, and the less the charge decay per unit drift distance. In this embodiment, the electron transport ability is set to μτ​​e = 1 x 10 -4 cm 2 · V -1 , the hole transport ability is μτ h = 4 x 10 -3 cm 2 · V -1 . In addition, the carrier cloud is affected by diffusion and self-same charge repulsion during the drift process, resulting in the gradual increase of the size of the carrier cloud. Therefore, in this process, in addition to recording the center coordinate of the carrier cloud particle, the size of the carrier cloud is also calculated synchronously.

[0079] When the carrier continuously drifts in the effective volume of the detector with a unit step size d, the amount of charge N carried by the carrier and the center coordinate P thereof are iteratively updated until the carrier escapes the boundary of the detector volume, as shown in Figure 5 During the drift iteration process, the weight potential change time interval Δt and the charge change ΔQ data sequence are updated synchronously. These data are input into the next module for generating an analog response signal.

[0080] S15: calculating the electrode-induced charge amount q according to the weight potential change and the charge amount Q, and convolving the electrode-induced charge amount q and the system transmission function of the charge-sensitive preamplifier to generate an original voltage signal;

[0081] In the original voltage signal generation module, the main amplifier adopts a CR-RC 4 digital filter with a shaping parameter RC = 2. Using the Δt, ΔQ data sequence generated by the last module, the simulated CSA signal can be obtained according to the system transmission function of the CSA. Figure 5 (b) of FIG. 16 is an analog pulse of the anode and the pixel cathode of the detector output. The rising edge of the signal reflects the weight potential distribution of the collection electrode, so the slopes of the rising edges of the cathode and the anode are different; the time point at which the signal amplitude reaches the maximum is the time point at which the carrier completes the collection; after reaching the highest point, the amplitude of the signal exponentially decays with the time constant of the CSA; the waveforms of the adjacent pixels show induced signals, the waveforms have smaller amplitudes, and the amplitudes eventually return to the vicinity of the baseline. The fact that the simulated waveforms have the same characteristics as the signal waveforms of the anode and the cathode collected by the actual pixel detector under the same configuration shows that the simulation method can accurately simulate the response process of the detector and has excellent accuracy and reliability;

[0082] S16: applying a filtering algorithm to the original voltage signal to generate a measurable pulse signal;

[0083] Finally, the sampled CSA signal is filtered through a CR-RC 4 ​The digital filter (main amplifier) filters and shapes the output waveform to be close to a Gaussian distribution, as shown in (a) of FIG. 3. Figure 6 The filtering function of the main amplifier not only effectively removes the noise in the CSA signal, improves the signal-to-noise ratio (SNR) of the final signal, but also to some extent reduces the influence of the baseline drift in the CSA on the stability of the signal amplitude.

[0084] S17: According to the amplitude distribution of the measurable pulse signal obtained above, the detector energy spectrum curve is constructed.

[0085] Finally, the peak amplitude of the shaped waveform is taken as the radiation energy representation, and the amplitudes of multiple events are counted to obtain the energy spectrum response of the detector, as shown in (b) of FIG. 3. Figure 6 Without changing the material property parameters and spatial geometry parameters described above, only the energy property of the radioactive source is modified in Geant4, and the energy spectrum of different radioactive sources can be simulated, as shown in (b) of FIG. 4. Figure 7

[0086] Unlike the unipolar charge transport characteristics of traditional compound semiconductors, new semiconductor materials represented by perovskite have special charge transport performance, with excellent transmission ability of both electrons and holes, showing bipolar charge transport ability. Under the same simulation conditions as above, by setting different charge transport properties of CsPbBr3 material, the performance simulation of unipolar CsPbBr3 and bipolar CsPbBr3 detectors is realized. In the unipolar CsPbBr3 semiconductor, the hole transport ability is set to μτ h = 1 × 10 -2 cm 2 V -1 , and the electron transport ability is μτ e = 4 × 10 -6 cm 2 V -1 ; in the bipolar CsPbBr3 semiconductor, the hole transport ability is set to μτ h = 1 × 10 -2 cm 2 V -1 , and the electron transport ability is μτ e = 4 × 10 - 3 cm 2 V -1 . Figure 8 The energy spectrum results of the CsPbBr3 pixel type detector with different charge transport characteristics are shown. The full energy peak and escape peak in the experimental results are highly consistent with the simulation results, indicating that the simulation method can be applied to the response performance simulation analysis of semiconductor detector devices with different charge transport characteristics.

[0087] ​The modules in the simulation method are independent in parameter adjustment, coupled with each other in operation, and can conveniently realize the detector response simulation after combination of various radiation source spatial positions, detector structures, charge transmission characteristics, and electronic characteristics.

[0088] The embodiment solves the following technical problems:

[0089] 1. Based on the working principle of the semiconductor detector system, a computer simulation method is developed for realizing the whole process response prediction of the semiconductor detector from radiation interaction to electronic response.

[0090] 2. Through the method, the response results of the semiconductor detector in multiple aspects can be obtained, including the weight potential distribution of the semiconductor detector, the electric field distribution, the energy deposition distribution, the carrier transport process, the pulse waveform corresponding to the CSA output, the main amplifier waveform and the energy spectrum, which are helpful for comprehensively analyzing the response behavior of the semiconductor detector.

[0091] 3. Based on the carrier motion mechanism design with a limited step, the real-time monitoring and analysis of the carrier transport state process in the detector can be realized, and the carrier spatial position and the CSA signal are more accurately associated, which is helpful for analyzing the drift path, the change of the device weight potential distribution and the microscopic process such as defect trapping, and further providing theoretical support for the analysis of the response mechanism of the detector and the optimization design of the device structure.

[0092] 4. The application has good universality and generality, and can adapt to semiconductor detectors of various structural types, and is suitable for performance simulation and response prediction of detectors under different material systems, different carrier transport characteristics and device configurations.

[0093] 5. The simulation accuracy and calculation complexity of the application can be flexibly adjusted by setting the voxel size. In addition, the simulation framework is composed of various functional modules, and any module can be conveniently optimized and replaced, for example, the CR-RC algorithm currently used by the main amplifier can be directly replaced by the ladder filter, triangular filter and other algorithms, and the front-end simulation module does not need to be modified. 4

[0094] The whole process semiconductor detector simulation method based on the multi-physical field coupling mechanism can realize integrated simulation and response prediction of various physical processes from detector structure modeling, radiation energy deposition, electric field distribution calculation, charge generation and transport to pulse signal processing and energy spectrum response.

[0095] ​This method tightly couples the microscopic physical mechanisms at the carrier scale with the macroscopic pulse signal formation process, generating detector response data with greater physical realism and predictive power. Using this simulation framework, the signal formation mechanism can be analyzed in depth, systematically linking carrier transport characteristics, device structural parameters, and pulse signal and energy spectrum response features. It comprehensively studies the impact of carrier processes such as drift, diffusion, and trapping on performance indicators such as energy spectrum resolution and peak shape characteristics, providing theoretical support for the analysis of complex phenomena such as energy spectrum distortion.

[0096] Meanwhile, this method can also effectively predict the energy spectrum response behavior of novel semiconductor materials, especially perovskite materials with dual-carrier transport characteristics, under different device structure couplings, providing an important basis for their performance evaluation and device structure optimization. Furthermore, this simulation platform can also be used to evaluate the response performance of detectors in specific application scenarios, providing forward-looking theoretical support for detector function development.

[0097] Example 2:

[0098] This embodiment constructs a three-dimensional model of a planar detector based on perovskite CsPbBr3 semiconductor, using a method similar to that of Embodiment 1, except that its effective detection volume is 5.0mm × 5.0mm × 2.0mm. A planar electrode structure is adopted, as shown below. Figure 9 As shown in (a), gold (Au) is used as the cathode electrode and copper (Cu) as the anode electrode material. By applying a -200V operating voltage to the gold electrode, a potential difference is established between the cathode and anode, thereby obtaining the potential distribution inside the detector. After importing the detector model into Geant4, the ray generator properties are set to... 57 The Co radioactive source, with other spatial parameters the same as in Example 1, was used. Electron transport performance and hole transport performance were set to μτ. e =2×10 -5 cm 2 V -1 ,μτ h =1×10 - 4 cm 2 V -1 Simultaneously, Gaussian noise was introduced as electronic noise, with a noise variance of 1.0. The final output simulated energy spectrum results are as follows. Figure 9 As shown in (b), the results are consistent with the energy spectrum of the actual tested planar CsPbBr3 detector.

[0099] Example 3:

[0100] The embodiment constructs a three-dimensional model of a planar detector based on cadmium zinc telluride (CZT) semiconductor, which is similar to the method and embodiment 1, except that the effective volume is 5.0 mm x 5.0 mm x 2.0 mm. The model is consistent with the planar CsPbBr3 detector used in the above embodiments in terms of geometric structure, and only the material properties are modified accordingly. Specifically, the detection medium is set to CZT, with an average ionization energy of 4.6 eV / pair and a relative dielectric constant of 10.9. In terms of electrode material, the cathode and anode in the CZT detector are both Au, and a constant voltage of +200 V is applied to the anode. According to the single-polarity characteristics of the CZT crystal, the charge transport performance is set as: μτ e = 2 x 10 -2 cm 2 V -1 , μτ h = 1 x 10 -5 cm 2 V -1 . Using the above parameters for simulation, the simulation results of the CZT planar detector can be obtained.

[0101] Embodiment 4:

[0102] This embodiment constructs a three-dimensional model of a quasi-semi-spherical radiation detector based on cesium lead bromide (CsPbBr3) semiconductor, which is similar to the method and embodiment 1, except that the effective detection volume is 5.0 mm x 5.0 mm x 5.0 mm. The cathode electrode is a circular gold (Au) electrode with a radius of r = 0.25 mm, located at the center of the plane. Except for the plane where the cathode is located, the other planes are uniformly covered with copper (Cu) electrodes as anodes, with a uniform electrode thickness of 0.1 mm, as shown in Figure 10 (a) and (b). Under different bias conditions (500 V, 1000 V and 1500 V), the cathode-anode voltage difference is applied, and the cathode weight potential distribution inside the detector is obtained, as shown in Figure 10 (c). Due to the significant difference in electrode area between the cathode and anode, a strong electric field region is formed near the cathode, resulting in a significant small pixel effect.

[0103] Similarly, to obtain the energy deposition data of the rays in the quasi-semi-spherical detector, the same energy deposition settings as in embodiment 1 are used. On this basis, the energy deposition data is input into the voxel charge cloud generation module and the drift step calculation output module, and the steps of energy to electron-hole pair conversion, particle center coordinate calculation, etc. are completed in turn to obtain the initial conditions of carrier drift. In this example, three different electron and hole transport performances are set to predict the energy spectrum response performance of the quasi-semi-spherical detector of cesium lead bromide (CsPbBr3) semiconductor, which are: μτ e = 4 x 10 -3 cm 2·V -1 ,μτ h =8×10 - 2 cm 2 ·V -1 ;μτ e =4×10 -6 cm 2 ·V -1 ,μτ h =8×10 -2 cm 2 ·V -1 ;μτ e =4×10 -4 cm 2 ·V -1 ,μτ h =8×10 - 3 cm 2 ·V -1 .

[0104] After the charge carriers are collected by the electrodes, the corresponding induced charge is calculated based on Shockley-Ramo theory, taking into account the weighted potential distribution, carrier drift trajectory, and charge recombination effect. This yields the final result. Figure 11 The energy spectrum results are shown in the figure. The figure illustrates the energy spectrum performance of the CsPbBr3 quasi-hemispherical detector under different bias voltages and electron transport properties. The results show that the detector's energy spectrum performance improves with increasing external electric field. By setting different electron and hole transport property parameters, the coupling effect of the two types of charge carriers on detector performance can be analyzed in depth. This method effectively solves the problem of complex energy spectrum response in traditional nonlinear weighted potential detectors. By quantitatively analyzing the synergistic effect between electric field strength and charge carrier transport characteristics, it provides an intuitive and reliable simulation method for detector performance optimization.

[0105] Example 5:

[0106] This embodiment provides a full-process simulation system for the response of a semiconductor nuclear radiation detector. Figure 2 The overall framework diagram of this embodiment consists of seven core functional modules: detector physical model module, distributed data output module, voxel charge cloud generation module, drift step size calculation and output module, raw voltage signal generation module, measurable pulse signal generation module, and detector energy spectrum curve construction module. Each module works together to accurately simulate the performance of the semiconductor detector.

[0107] Specifically, it includes:

[0108] (1) Detector physical model module, used to construct the detector physical model;

[0109] The specific implementation steps are as follows:

[0110] -Input detector geometry parameters (electrode structure, effective volume size), material properties (resistivity p, dielectric constant e) and carrier transport characteristics (electron mobility lifetime product m t e , hole mobility lifetime product m t h ).

[0111] - Based on the finite element method, calculate the potential distribution inside the detector, and solve the electrode weight potential distribution according to the Shockley-Ramo theory.

[0112] (2) Distribution data output module, for generating electric field distribution and radiation energy deposition data;

[0113] The specific implementation steps are as follows:

[0114] -Import the detector model into the Geant4 simulation package, configure the ray source parameters (energy, direction, momentum, particle number).

[0115] -Through Geant4 simulation of the interaction between rays and matter, output the position coordinates and corresponding energy values of the energy deposition events, form the energy deposition distribution diagram inside the detector, and use the electromagnetic field simulation software Ansys Maxwell to output the potential field distribution data of the detector model.

[0116] Data output:

[0117] a) Potential field distribution data (including weight potential and detector working potential field);

[0118] b) Radiation energy deposition distribution data;

[0119] (Note: The above data are transmitted as input to the voxel charge cloud generation module and the drift step calculation output module).

[0120] (3) Voxel charge cloud generation module, for simulating the generation and transport of electron-hole pairs;

[0121] The specific implementation steps are as follows:

[0122] - According to the radiation energy deposition data E d , calculate the number of electron-hole pairs N generated at each deposition point (based on the average ionization energy of the semiconductor ).

[0123] - Adopt energy-weighted voxelization strategy, combine adjacent energy deposition points into equivalent charge clouds (voxels), balance calculation efficiency and accuracy by adjusting voxel size, such asFigure 3 as shown.

[0124] In the implementation process of the voxel strategy, the spatial aggregation of multiple deposition sites needs to determine the equivalent deposition coordinates. In view of the difference in energy deposition values of each deposition site, an energy weighted average algorithm is used to determine the equivalent position, and the formula is as follows:

[0125]

[0126] wherein is the position coordinate of the equivalent deposition site, E i is the energy deposition value of the i-th deposition site, p i (x, y, z) represents the position coordinate of the i-th deposition site.

[0127] (4) Drift step calculation output module, used for simulating the charge collection process;

[0128] Involving carrier drift iterative calculation, the specific implementation steps are as follows:

[0129] - For each voxel charge cloud, the carrier drift is simulated step by step according to the local electric field direction (electrons to the positive electrode, holes to the negative electrode).

[0130] - Update the following parameters at each drift step:

[0131] ① Spatial coordinates: calculate the new position based on the electric field strength and time step (Voxel i → Voxel i+1 );

[0132] ② Spatial size: calculate the new size of the carrier cloud according to the combined formula of carrier diffusion and electric repulsion.

[0133] ③ Residual charge: calculate the charge decay caused by carrier recombination / capture (Q i → Q i+1 ) through Hecht formula. Hecht formula: Q k+1 = Q k ·exp(-Δx / λ).

[0134] ④ Weight potential difference and time difference (Δt): record the weight potential field change and time consumption in the drift path.

[0135] Data output:

[0136] The Δt, Q data sequence obtained by each drift step calculation (note: the above data are transmitted as input to the original voltage signal generation module, the measurable pulse signal generation module and the detector energy spectrum curve construction module).

[0137] (5) Raw voltage signal generation module, for signal generation of analog electronic system;

[0138] Involving induced charge calculation and signal shaping, the specific implementation steps are as follows:

[0139] Based on the Shockley-Ramo formula, the induced charge q of the electrode is calculated using The formula is as follows:

[0140] Where Q is the current charge, is the weight potential change.

[0141] At the same time, signal shaping is involved, and the specific implementation steps are as follows:

[0142] Charge sensitive preamplifier (CSA) response: Convolve the induced charge q with the CSA transfer function to generate the raw voltage signal.

[0143] Data output: Real-time CSA waveform data.

[0144] (6) Measurable pulse signal generation module, for the generation of measurable pulse signals;

[0145] Involving measurable pulse signal shaping, the specific implementation steps are as follows:

[0146] Main amplifier filter shaping: Apply a filter algorithm (such as CR-RC 4 shaping algorithm) and a preset sampling frequency to the CSA output signal to generate a measurable pulse signal.

[0147] Data output: Main amplifier waveform data.

[0148] (7) Detector energy spectrum curve construction module, for energy spectrum analysis;

[0149] Involving energy spectrum extraction, the specific implementation steps are as follows:

[0150] Energy spectrum statistics: Analyze the amplitude distribution of the electrode induced charge q, raw voltage signal or measurable pulse signal, and construct the detector energy spectrum curve.

[0151] Data output: Main amplifier energy spectrum.

[0152] Through the cooperative work of the above seven modules, the specific performance parameters of the semiconductor detector under the transport characteristics of a specific material and the device structure can be simulated. Compared with the actual measurement data, the results obtained by the simulation method have high consistency, verifying its effectiveness in detector response analysis, performance evaluation and device optimization design.

[0153] Obviously, the above embodiments are merely example for clearly illustrating, and are not limitation to the embodiments. Based on the above description, other different forms of changes or variations can be made by those skilled in the art. Here, all the embodiments are not required to be enumerated, and the obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. A method of full process simulation of a response of a semiconductor nuclear radiation detector, characterized by, The method comprises the following steps: S11: obtaining the geometric parameters, material properties and carrier transport characteristics of the detector, and calculating the internal potential distribution and the weight potential distribution of each electrode to obtain a detector physical model; S12: configure the detector physical model as a source parameter of the ray, obtain the energy deposition point and the ray energy deposition distribution data E d and output the potential field distribution data of the detector physical model; S13: Calculate the number of electron-hole pairs N generated by each energy deposition point based on the average ionization energy of the semiconductor material d and based on the average ionization energy of the semiconductor material Calculate the number of electron-hole pairs N generated by each energy deposition point based on the average ionization energy of the semiconductor material S14: According to the potential field distribution data and the carrier drift of the voxel charge cloud simulation, the weight potential change calculated at each drift step is output and a charge amount Q; S15: according to the weight potential change and the charge quantity Q, and convolving the electrode induced charge quantity q and a system transfer function of a charge sensitive preamplifier to generate an original voltage signal; an input end of the charge sensitive preamplifier is connected to the detector; S16: applying a filtering algorithm to the original voltage signal to generate a measurable pulse signal; S17: constructing a detector energy spectrum curve according to the amplitude distribution of the electrode-induced charge q, the original voltage signal or the measurable pulse signal.

2. The method of claim 1, wherein the full process simulation of the response of the semiconductor nuclear radiation detector is performed by a computer program product comprising a computer readable medium having stored computer readable program means for performing the method of claim 1. In the step S11, the geometric parameters are selected from electrode structure and volume dimensions, the material properties are selected from resistivity p and dielectric constant e, and the carrier transport characteristics are selected from electron mobility lifetime product μτ e and hole mobility lifetime product μτ h .

3. The method of claim 1, wherein the full process simulation of the response of the semiconductor nuclear radiation detector is performed by a computer program product comprising a computer readable medium having stored computer readable program means for performing the method of claim 1. In the step S13, the method for calculating the number N of electron-hole pairs is as follows:

4. The method of claim 1, wherein the full process simulation of the response of the semiconductor nuclear radiation detector is performed by a computer program product comprising a computer readable medium having stored computer readable program means for performing the method of claim 1. In the step S13, the preset distance is 0.1 mm.

5. The method of claim 1, wherein the full process simulation of the response of the semiconductor nuclear radiation detector is performed by a computer program product comprising a computer readable medium having stored computer readable program means for performing the method of claim 1. The charge amount Q is calculated by Hecht formula in the step S14: Q k+1 = Q k · exp(-Δx / λ); where Q k+1 is the charge amount of next step k+1, Q k is the charge amount of current step k, Δx is the unit step length of movement, and λ is the average drift distance of the carrier.

6. The method of claim 1, wherein the full process simulation of the response of the semiconductor nuclear radiation detector is performed by a computer program product comprising a computer readable medium having stored computer readable program means for performing the method of claim 1. The weight potential change is calculated by the following formula: wherein is the weight potential value of the kth step, is the weight potential value of the k-1th step.

7. The method of claim 1, wherein the full process simulation of the response of the semiconductor nuclear radiation detector is performed by a computer program product comprising a computer readable medium having stored computer readable program means for performing the method of claim 1. In the step S15, the system transfer function of the charge-sensitive preamplifier is where τ = R f · C f , R f is the feedback resistance of the charge-sensitive preamplifier, C f is the feedback capacitance of the charge-sensitive preamplifier, τ is a time constant characterizing the charge-sensitive preamplifier, and s is a complex frequency domain variable.

8. The method of claim 1, wherein the full process simulation of the response of the semiconductor nuclear radiation detector is performed by a computer program product comprising a computer readable medium having stored computer readable program means for performing the method of claim 1. The electrode induced charge quantity q is calculated by the following formula: wherein Q is the charge quantity, is the weight potential change.

9. The method for simulating the entire process of a semiconductor nuclear radiation detector response as described in claim 1, characterized in that, In the step S11, the material of the detector is an elemental semiconductor, a binary semiconductor or a ternary semiconductor; the elemental semiconductor is selected from silicon or germanium, the binary semiconductor is selected from cadmium telluride, mercury iodide or thallium bromide, and the ternary semiconductor is selected from a perovskite or cadmium zinc telluride; the perovskite is selected from a three-dimensional perovskite, a two-dimensional perovskite, a zero-dimensional perovskite or an inverse perovskite; the three-dimensional peroviskite is selected from A1B1X13; wherein A1 is selected from one or more of Na, K, Rb, Cs, Cu, Tl and H3O, B1 is selected from one or more of Pb, Si, Ge, Sn, Mg, Ca, Sr, Ba, Zn, Cd and Hg, and X1 is selected from one or more of F, Cl, Br, I, BF4, HCOO, OH, CN, SCN, NCS, SH, NO3 and H2POO; the two-dimensional peroviskite is selected from TlPbI3 or A23B22X29; wherein A2 is selected from one or both of Cs and Rb, B2 is selected from one or both of Sb and Bi, and X2 is selected from one or more of F, Cl, Br and I; the zero-dimensional peroviskite is selected from Cs3Bi2I9, Cs2TeI6, Cs2TeBr6 or Cs2TeCl6; and the inverse peroviskite is selected from Hg3Se2Br2 or TlSn2I5.

10. A full process simulation system of a response of a semiconductor nuclear radiation detector, characterized by, Specifically comprising: a detector physical model module, configured to obtain the geometric parameters, material properties and carrier transport characteristics of the detector, and calculate the internal potential distribution and the weight potential distribution of each electrode to obtain a detector physical model; a distribution data output module, configured to configure the detector physical model as source parameters of the rays, obtain energy deposition points and ray energy deposition distribution data E d and output potential field distribution data of the detector physical model; a voxel charge cloud generation module configured to generate a voxel charge cloud according to the ray energy deposition distribution data E d and calculate the number of electron-hole pairs N generated by each energy deposition point based on the average ionization energy ε of the semiconductor, and combine the energy deposition points within a preset distance into a voxel charge cloud. a drift step calculation output module, configured to simulate carrier drift according to the potential field distribution data and the voxel charge cloud, and output a weight potential change obtained by each drift step calculation and a charge quantity Q; The original voltage signal generation module is used for calculating the electrode induced charge quantity q according to the weight potential change and the charge quantity Q, and convolving the electrode induced charge quantity q and the system transfer function of the charge sensitive preamplifier to generate the original voltage signal; the input end of the charge sensitive preamplifier is connected with the detector. a measurable pulse signal generation module, configured to apply a filtering algorithm to the original voltage signal to generate a measurable pulse signal; a detector energy spectrum curve construction module, configured to construct a detector energy spectrum curve according to the amplitude distribution of the electrode-induced charge q, the original voltage signal or the measurable pulse signal.

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