A method, system, energy spectrum detector and medium for energy spectrum analysis

Abstract

The application provides a kind of energy spectrum analysis method, system, energy spectrum detector and medium, in the method, by obtaining the characteristic X-ray excited by the measured sample and the element energy range of the measured sample;The characteristic X-ray is converted into characteristic electric signal, and the digital signal value corresponding to the characteristic electric signal is dynamically allocated based on the element energy range address, to obtain a plurality of optimization channel addresses;The baseline noise spectrum of each optimization channel address is obtained, and the digital signal value on each optimization channel address is statistically differentially denoised based on each baseline noise spectrum, to generate the optimized spectrum for the characteristic X-ray.Energy resolution for low-energy signal in energy spectrum analysis process is improved in this way.

Description

Technical Field

[0001] This application relates to the field of signal processing technology, and in particular to an energy spectrum analysis method, system, energy spectrum detector and medium. Background Technology

[0002] An X-ray energy dispersive detector (EDD) is a device used to measure the energy distribution of X-ray photons. It acquires characteristic X-rays reflected from the sample through an internal silicon drift detector and converts these characteristic X-rays into electrical signals for energy spectral analysis. In practical applications, the energy of the characteristic X-rays generated by the sample is often affected by the material properties of the sample itself. When the atomic number of the sample material is low, the X-rays reflected from these low-energy materials are diluted with most of their energy when passing through the dead layer of the silicon drift detector. Only a small portion of the energy reaches the active layer of the silicon drift detector for subsequent energy spectral analysis. The resulting problem is that when the X-ray EDD receives low-energy characteristic X-rays, it can only perform energy spectral analysis based on a small portion of the energy, leading to low energy resolution for low-energy elements and an inability to accurately acquire the energy spectral data of the characteristic rays.

[0003] Therefore, how to solve the problem of low energy resolution for low-energy elements when performing energy spectrum analysis using current energy spectrum detectors has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of the above problems, and in order to solve the problem of low energy resolution for low-energy elements when performing energy spectrum analysis using current energy spectrum detectors, this application provides an energy spectrum analysis method, system, energy spectrum detector and medium.

[0005] The embodiments of this application disclose the following technical solutions:

[0006] In a first aspect, embodiments of this application provide an energy spectrum analysis method applied in an energy spectrum detector, the method comprising:

[0007] Obtain the characteristic rays excited by the sample under test and the elemental energy range of the sample under test;

[0008] The characteristic ray is converted into a characteristic electrical signal, and the digital signal value corresponding to the characteristic electrical signal is dynamically allocated based on the energy range of the element to obtain multiple optimized addresses;

[0009] The baseline noise energy spectrum of each optimized channel address is obtained, and statistical differential noise reduction processing is performed on the digital signal value of each optimized channel address based on the baseline noise energy spectrum to generate an optimized energy spectrum for the characteristic ray; the baseline noise energy spectrum is used to characterize the energy spectrum collected by the energy spectrum detector when the energy spectrum detector does not perform energy spectrum detection on the sample under test.

[0010] In one possible implementation, the step of acquiring the baseline noise energy spectrum and performing statistical differential noise reduction processing on the digital signal values ​​at each of the optimized addresses based on the baseline noise energy spectrum to generate an optimized energy spectrum for the feature ray includes:

[0011] Obtain multiple sets of baseline noise energy spectra for each optimized address within a preset time period to calculate the average baseline noise value for each optimized address;

[0012] Multiple sets of digital signal values ​​for each optimized address are obtained within the preset time period to calculate the average signal value and signal value deviation for each optimized address.

[0013] Based on the average signal value, the average baseline noise value, and the signal value deviation of each optimized channel address, differential calculation is performed on the digital signal value of each optimized channel address to obtain the optimized digital signal value of each optimized channel address.

[0014] The optimized energy spectrum is generated based on the optimized digital signal values ​​of each optimized address.

[0015] In one possible implementation, the energy spectrum detector includes: a charge-sensitive preamplifier, wherein the charge-sensitive preamplifier is provided with a preset controllable capacitor network; the preset controllable capacitor network is composed of multiple capacitor units and multiple individually corresponding switching units; the type of the switching units is at least one of single transistors and transmission gates composed of complementary transistors.

[0016] Before converting the characteristic ray into a characteristic electrical signal, the method further includes:

[0017] Based on the energy range of the element, the switching units within the preset controllable capacitor network are controlled to turn off or merge, thereby adjusting the feedback capacitance value of the charge-sensitive preamplifier.

[0018] In one possible implementation, the dynamic channel address allocation based on the element energy range for the digital signal value corresponding to the characteristic electrical signal yields multiple optimized channel addresses, including:

[0019] The channel address allocation density is determined based on the energy range of the elements;

[0020] Dynamically allocate digital signal values ​​corresponding to the characteristic electrical signals according to the address allocation density to obtain the multiple optimized addresses.

[0021] In one possible implementation, the energy spectrum detector includes a low-energy signal filtering circuit, which is used to filter out electrical signals in the characteristic electrical signals whose element energy range is greater than the preset first threshold when the element energy range is less than a preset first threshold.

[0022] In one possible implementation, after converting the characteristic ray into a characteristic electrical signal, the method further includes:

[0023] Based on dynamic baseline correction technology, background noise and baseline drift in the characteristic electrical signal are adaptively removed to obtain the denoised electrical signal;

[0024] The energy spectrum of the denoised electrical signal is modeled using the Markov chain Monte Carlo method to estimate the posterior distribution of the peak parameters corresponding to the feature rays.

[0025] The energy distribution of the characteristic rays is quantified based on a dynamic compensation algorithm based on pulse amplitude analysis and the posterior distribution of the peak parameters to optimize the response function of the energy spectrum detector.

[0026] Secondly, embodiments of this application provide an energy spectrum analysis system applied in an energy spectrum detector, the system comprising:

[0027] The acquisition module is used to acquire the characteristic rays excited by the sample under test and the elemental energy range of the sample under test;

[0028] The channel address allocation module is used to convert the characteristic ray into a characteristic electrical signal, and to dynamically allocate the digital signal value corresponding to the characteristic electrical signal based on the element energy range to obtain multiple optimized channels.

[0029] A statistical differential noise reduction module is used to acquire the baseline noise energy spectrum of each of the optimized channels, and to perform statistical differential noise reduction processing on the digital signal values ​​at each of the optimized channels based on the baseline noise energy spectrum to generate an optimized energy spectrum for the characteristic ray; the baseline noise energy spectrum is used to characterize the energy spectrum collected by the energy spectrum detector when the energy spectrum detector does not perform energy spectrum detection on the sample under test.

[0030] In one possible implementation, the statistical difference noise reduction module is specifically used for:

[0031] Obtain multiple sets of baseline noise energy spectra for each optimized address within a preset time period to calculate the average baseline noise value for each optimized address;

[0032] Multiple sets of digital signal values ​​for each optimized address are obtained within the preset time period to calculate the average signal value and signal value deviation for each optimized address.

[0033] Based on the average signal value, the average baseline noise value, and the signal value deviation of each optimized channel address, differential calculation is performed on the digital signal value of each optimized channel address to obtain the optimized digital signal value of each optimized channel address.

[0034] The optimized energy spectrum is generated based on the optimized digital signal values ​​of each optimized address.

[0035] Thirdly, embodiments of this application provide an energy spectrum detector for performing any of the possible energy spectrum analysis methods described in the first aspect.

[0036] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the possible energy spectrum analysis methods in the first aspect.

[0037] Compared to existing technologies, this application offers the following advantages: This application provides an energy spectrum analysis method, system, energy spectrum detector, and medium. The method first requires acquiring the characteristics excited by the sample under test and the elemental energy range of the sample. Subsequently, the characteristic rays are converted into characteristic electrical signals, and based on the elemental energy range of the sample, the digital signal values ​​in the characteristic electrical signals are dynamically assigned channels to obtain multiple optimized channels. This allows for dynamic channel assignment based on the actual energy levels of the constituent elements of the sample, ensuring that the detector maintains high energy resolution for low-energy signals, thus solving the problem of low energy resolution for low-energy signals in traditional channel assignment schemes based on uniform allocation. Furthermore, based on dynamic channel address allocation, it is also necessary to obtain the baseline noise energy spectrum corresponding to each optimized channel address individually, and perform statistical differential noise reduction processing on the digital signal values ​​of each optimized channel address based on each baseline noise energy spectrum. This is to eliminate the influence of system electronic noise and environmental noise on energy resolution, and then eliminate the influence of system noise in the energy spectrum detector on energy resolution to the greatest extent through statistical and differential methods, thereby effectively improving the energy resolution of the energy spectrum detector when performing energy spectrum analysis. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A schematic flowchart of an energy dispersive spectroscopy (EDS) analysis method provided in an embodiment of this application;

[0040] Figure 2 This is a schematic diagram of the structure of an energy spectrum detector in a practical application scenario provided by an embodiment of this application;

[0041] Figure 3 A schematic diagram illustrating the distribution of the detection element range and the number of energy channels in the spectrum, provided for an embodiment of this application;

[0042] Figure 4 A schematic diagram illustrating dynamic address allocation provided in an embodiment of this application;

[0043] Figure 5 This application provides a schematic diagram of a statistical differential noise reduction process in an embodiment.

[0044] Figure 6 A circuit diagram of a conventional charge-sensitive preamplifier provided for an embodiment of this application;

[0045] Figure 7 This application provides a schematic diagram of a pre-defined controllable capacitor network parallel structure as an embodiment of the present application.

[0046] Figure 8 This is a connection diagram of a pre-defined controllable capacitor network series structure provided in an embodiment of this application;

[0047] Figure 9 A schematic diagram of a main amplifier circuit provided in an embodiment of this application;

[0048] Figure 10 A schematic diagram of a low-energy signal filtering circuit provided in an embodiment of this application;

[0049] Figure 11 This application provides a schematic diagram of the structure of an energy spectrum analysis system. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and accompanying drawings. It should be particularly noted that the embodiments described in this application are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0051] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0052] As described earlier, in practical applications, the energy of the characteristic X-rays generated by the sample being tested is often affected by the material properties of the sample itself. When the atomic number of the sample material is low, because such materials belong to low energy levels, the X-rays reflected by these low-energy materials are mostly diluted in energy when passing through the dead layer of the silicon drift detector. Only a small portion of the energy reaches the active layer of the silicon drift detector for subsequent energy spectrum detection. The problem this causes is that when the X-ray energy spectrum detector receives low-energy characteristic X-rays, it can only perform energy spectrum detection based on a small portion of the energy, resulting in low energy resolution for low-energy elements and an inability to accurately obtain the energy spectrum data of the characteristic rays.

[0053] To address the aforementioned issues, this application provides an energy spectrum analysis method, system, detector, and medium. The method first obtains the characteristics excited by the sample and the elemental energy range of the sample. Then, the characteristic rays are converted into characteristic electrical signals, and based on the elemental energy range of the sample, the digital signal values ​​in the characteristic electrical signals are dynamically assigned channels to obtain multiple optimized channels. This allows for dynamic channel assignment based on the actual energy levels of the constituent elements of the sample, ensuring the detector maintains high energy resolution for low-energy signals and solving the problem of low energy resolution for low-energy signals in traditional uniform channel assignment schemes. Furthermore, based on the dynamic channel assignment, the baseline noise energy spectrum corresponding to each optimized channel is obtained, and statistical differential noise reduction is performed on the digital signal values ​​at each optimized channel based on the baseline noise energy spectrum. This eliminates the influence of system electronic noise and environmental noise on energy resolution, thereby maximizing the elimination of system noise in the energy spectrum detector and effectively improving the energy resolution of the energy spectrum detector during energy spectrum analysis through statistical and differential methods.

[0054] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0055] See Figure 1 and Figure 2 , Figure 1 This is a schematic flowchart of an energy dispersive spectroscopy (EDS) analysis method provided in an embodiment of this application. Figure 2 This is a schematic diagram of the structure of an energy spectrum detector in a practical application scenario provided by an embodiment of this application. Figure 1 The flowchart specifically includes the following steps:

[0056] S101: Obtain the characteristic rays excited by the sample under test and the elemental energy range of the sample under test.

[0057] In the initial stage of energy dispersive spectroscopy (EDS) analysis, a characteristic X-ray is emitted from an external excitation source to the sample under test, causing the sample to emit corresponding characteristic X-rays. In practical applications, the characteristic X-rays emitted by the sample can be of various types; X-rays are used as an example here. When a high-energy X-ray is emitted from an external excitation source to the sample, the sample is irradiated by the high-energy X-rays, thereby emitting characteristic X-rays (corresponding to the characteristic X-rays emitted by the sample in this embodiment). Furthermore, the characteristic X-rays emitted by the sample are detected by a silicon drift detector in the EDS detector, simultaneously determining the elemental energy range of the sample.

[0058] In one possible implementation, the elemental energy range of the sample being tested can be determined based on the sample's elemental index and a pre-defined energy range. For example, for light elements (such as Na-Cl), the corresponding elemental energy range can be 0.1-2 keV, while for heavy elements (such as Fe-U), the corresponding elemental energy range can be 6-20 keV.

[0059] S102: Convert the characteristic ray into a characteristic electrical signal, and dynamically allocate the digital signal value corresponding to the characteristic electrical signal based on the element energy range to obtain multiple optimized addresses.

[0060] After detecting the characteristic rays excited by the sample under test, the silicon drift detector needs to convert the characteristic rays into characteristic electrical signals and perform dynamic channel address allocation based on the specific digital signal values ​​in the electrical signals, so as to improve the energy resolution of the energy spectrum detector when detecting light elements.

[0061] See Figure 3The disclosed schematic diagram illustrates the distribution of the detection element range and the number of energy spectrum channels. In current channel allocation schemes for characteristic electrical signals, the channel range and channel allocation interval of the energy spectrum detector are usually fixed. In energy spectrum analysis of light element samples, since the characteristic rays excited by light element samples are often in the low-energy range, when a fixed channel allocation is used for the converted electrical signals, it is impossible to accurately detect elements with similar or even partially overlapping spectral peaks. This leads to low energy resolution for low-energy elements. This type of uniform channel allocation is not conducive to distinguishing low-energy elements with similar energies. For example, for the light elements Be, B, C, N, and O, their corresponding elemental energy ranges are 0.108 keV, 0.183 keV, 0.277 keV, 0.392 keV, and 0.525 keV, respectively. If the channel range is set to 1024-8096, then according to the current fixed uniform channel allocation method, the channel allocation density is 108 / 8096 = 0.0133 keV / channel. Under this address allocation method, even if the address range is increased from 1024-8096 to 1024-65536, the address allocation density is only 108 / 65536 = 0.00165 keV / channel. Based on this, combined with the influence of noise, the distinguishability of these light elements in terms of address is poor.

[0062] Therefore, in order to address the problems existing in the current uniform fixed address allocation, the embodiments of this application aim to dynamically allocate digital signal values ​​corresponding to characteristic electrical signals according to the actual elemental energy range of the sample being tested. Thus, under the premise of a fixed address range, the address allocation is transformed from fixed allocation to dynamic allocation, allocating more addresses to low-energy electrical signals, so that the low-energy signals have higher address density, thereby improving the energy resolution of the energy spectrum detector for low-energy elements.

[0063] Specifically, the process of performing dynamic address allocation in step S102 is completed by the following two steps:

[0064] Step 1: Determine the channel address allocation density based on the energy range of the elements;

[0065] Step 2: Perform dynamic channel address allocation on the digital signal value corresponding to the characteristic electrical signal according to the channel address allocation density to obtain the multiple optimized channel addresses.

[0066] Dynamic channel address allocation needs to be based on the actual energy range of the sample elements. The aim is to determine whether the characteristic electrical signal generated by the sample belongs to the high-energy or low-energy range. For example, for light elements such as Be, B, C, N, and O, the energy ranges are 0.108 keV, 0.183 keV, 0.277 keV, 0.392 keV, and 0.525 keV, respectively. In this case, the characteristic electrical signal excited by these light elements can be determined to be a low-energy signal, and a high-density channel address allocation can be applied. Similarly, when the element is a heavy element, such as AM-95, whose energy range is approximately 108 keV, the characteristic electrical signal excited by this heavy element can be determined to be a high-energy signal, and a low-density channel address allocation can be applied.

[0067] In one possible implementation, to ensure the accuracy of address allocation, this embodiment can also determine the address allocation density based on the specific values ​​of the element energy range. For example, when the element energy range is 0.1 keV-1 keV and the predetermined address range is 1024-8096, the corresponding address allocation density for dynamic address allocation is 1 / 8096 = 0.000124 keV / channel. When the predetermined address range is 1024-65536, the corresponding address allocation density is 1 / 65536 = 0.0000153 keV / channel. The actual address allocation effect can be seen in [reference needed]. Figure 4 The disclosed schematic diagram of a dynamic address allocation scheme shows that, through the dynamic address allocation scheme for light elements in this embodiment, the distinguishability of light elements in the address can be improved by at least two orders of magnitude, thereby improving the energy resolution of light elements in energy spectrum analysis.

[0068] The above is a description of the process for improving energy resolution through dynamic address allocation in the embodiments of this application. Next, we will continue to combine... Figure 1 The energy dispersive spectroscopy (EDS) analysis method in the embodiments of this application is described below:

[0069] S103: Obtain the baseline noise energy spectrum of each of the optimized addresses, and perform statistical differential noise reduction processing on the digital signal values ​​of each of the optimized addresses based on the baseline noise energy spectrum to generate an optimized energy spectrum for the characteristic ray; the baseline noise energy spectrum is used to characterize the energy spectrum collected by the energy spectrum detector when the energy spectrum detector does not perform energy spectrum detection on the sample under test.

[0070] While current energy resolution enhancement schemes disclose independent denoising and noise reduction processing for characteristic electrical signals, these methods do not consider the potential system noise inherent in the energy spectrum detector itself. Therefore, they cannot minimize the negative impact of signal noise on energy resolution. Thus, this step in the embodiments of this application aims to eliminate signal noise by performing statistical differential denoising processing on the digital signal values ​​at each optimized address, thereby maximizing the elimination of noise that may exist in the characteristic electrical signal and ultimately improving energy resolution.

[0071] The statistical differential noise reduction process includes a statistical calculation step and a differential calculation step for the digital signal values ​​at each channel address. Next, the specific implementation process in step S103 will be introduced with reference to the accompanying drawings of specific embodiments.

[0072] See Figure 5 The figure is a schematic diagram of a statistical differential noise reduction process provided in an embodiment of this application, which specifically includes the following steps:

[0073] S1031: Obtain multiple sets of baseline noise energy spectra for each optimized address within a preset time period to calculate the average baseline noise value for each optimized address.

[0074] Baseline noise energy spectrum is used to characterize the energy spectrum collected by the energy spectrum detector when the sample under test is not being detected. It reflects the system noise of the detector under normal operating conditions. In order to accurately reflect the system noise of the detector under normal operating conditions, the baseline noise energy spectrum of each optimized channel needs to be acquired multiple times within a certain preset time period. By statistically analyzing the multiple baseline noise energy spectra acquired at each optimized channel and averaging them, the average baseline noise of each optimized channel can be calculated, completing the preliminary statistical step for the baseline noise average.

[0075] In another possible implementation, by using multiple baseline noise energy spectra at each optimized address, the variance of the baseline energy spectrum values ​​at each optimized address can be statistically calculated. This variance can be used as an optimization term for the baseline noise average value, which can further improve the accuracy of the baseline noise average value calculation.

[0076] S1032: Obtain multiple sets of digital signal values ​​for each optimized address within the preset time period, so as to calculate the average signal value and signal value deviation for each optimized address.

[0077] After statistically analyzing the system noise deviation at each optimized address without energy spectrum detection of the sample, it is then necessary to statistically analyze the energy spectrum acquisition at each optimized address when the detector performs energy spectrum detection analysis on the sample. Therefore, similar to the statistical method for baseline noise deviation, for the digital signal value corresponding to each optimized address individually, it is also necessary to acquire multiple sets of digital signal values ​​at each optimized address within a preset time period, and calculate the average signal value and signal value deviation at each optimized address to improve the accuracy of the average signal value calculation.

[0078] S1033: Based on the average signal value, the average baseline noise value, and the signal value deviation of each optimized address, perform differential calculation on the digital signal value of each optimized address to obtain the optimized digital signal value of each optimized address;

[0079] S1034: Generate the optimized energy spectrum based on the optimized digital signal values ​​of each optimized address.

[0080] Subsequently, based on the average signal value, average baseline noise value, and signal value deviation at each optimization, differential operation is performed on the digital signal value at each optimized address to eliminate system noise error at each optimized address to the greatest extent possible, thereby improving energy resolution.

[0081] Specifically, the average signal value is set to μ. meas The signal value deviation is σ meas The baseline noise average is μ null Then the difference operation between these three can be performed using μ. meas +σ meas -μ null Or a single μ meas -μ null This is used to perform differential calculations among the three. This calculation logic is then iterated through each optimized address to minimize environmental noise during energy spectrum analysis and improve energy resolution.

[0082] The above describes the technical content of improving the energy resolution of the energy spectrum detector through dynamic channel address allocation and statistical differential noise reduction in the embodiments of this application. In addition to these two parts, the embodiments of this application also improve the energy resolution of the energy spectrum detector from three additional aspects: the feedback capacitance of the energy spectrum detector, high-energy signal filtering, and characteristic electrical signal processing. These will be described in detail below with reference to the accompanying drawings.

[0083] See Figure 6 , Figure 6 This is a circuit diagram of a conventional charge-sensitive preamplifier provided for an embodiment of this application.

[0084] Depend on Figure 6 It is evident that in current traditional charge-sensitive preamplifiers, a fixed-capacitance feedback capacitor is used. The core contradiction of this fixed-capacitor architecture lies in the trade-off between energy dynamic range and detection sensitivity. Taking Cf = 1 pF as an example, the detection limit for carbon (CKα = 0.28 keV) is approximately 0.5 keV, with a signal-to-noise ratio (SNR) barely reaching 3:1. For heavy elements (such as MoKα = 17.4 keV), the ΔV can reach several volts, and although not saturated, the dynamic range utilization is less than 40%. If Cf is reduced to 0.1 pF to improve the detection sensitivity of light elements, the ΔV corresponding to high-energy X-rays will exceed the saturation threshold of a typical operational amplifier, forcing the system to sacrifice high-energy resolution. This contradiction is particularly prominent in broadband detection scenarios. Existing technologies, by compromising and selecting Cf values ​​(typically 0.5-2 pF), can only achieve partial element coverage, resulting in a limited lower limit for light element detection and a compressed dynamic range for heavy elements.

[0085] Therefore, to address the problem of feedback capacitors in existing energy spectrum detectors, this application provides an externally controllable preset controllable capacitor network. This capacitor network consists of multiple capacitor units and multiple corresponding switching units, and can be a complex combination such as series, parallel, or a combination of series and parallel (see details). Figure 7 and Figure 8 The diagrams disclosed are a parallel connection diagram of a preset controllable capacitor network and a series connection diagram of a preset controllable capacitor network. The core of the preset controllable capacitor network lies in adjusting the feedback capacitor of the charge-sensitive preamplifier by controlling the switching on or off, thereby achieving the purpose of adapting to the energy spectrum analysis of different elements and improving energy resolution.

[0086] Therefore, in applications based on a pre-defined controllable capacitor network, users can determine whether the constituent elements of a sample are heavy or light based on the actual elemental energy range. By adjusting the switching units in the capacitor network, the feedback capacitance value of the charge-sensitive preamplifier can be adjusted, thereby improving energy resolution. Figure 7 The pre-defined controllable capacitor network in parallel structure shown is used as an example. When all switches S1, S2, S3, and S4 are closed, capacitors C1, C2, C3, and C4 are connected in parallel to form a feedback capacitor Cf = C1 + C2 + C3 + C4. The feedback capacitance value is relatively large, suitable for detecting heavy elements. When some of the switches S1, S2, S3, and S4 are closed, the feedback capacitance value is moderate, suitable for detecting intermediate elements. When only one of the switches S1, S2, S3, and S4 is closed, and the capacitor connected to that switch is the smallest among them, the feedback capacitance value is the smallest, suitable for detecting lightweight elements, or even ultralight elements.

[0087] Therefore, by using the charge-sensitive preamplifier set in the energy spectrum detector in this embodiment, the merging or shutting down of each switching unit in the capacitor network is adjusted according to the actual value of the element energy range, thereby adjusting the feedback capacitor value of the charge-sensitive preamplifier and thus improving the energy resolution.

[0088] The above describes the adjustment of the feedback capacitor in the embodiments of this application. Next, the high-energy signal filtering process in the embodiments of this application will be described.

[0089] In current energy dispersive spectroscopy (EDS) detectors, silicon drift detectors generate a large amount of charge in response to heavy element excitation radiation. After conversion by a charge-sensitive preamplifier, the output voltage can reach several volts. This high-amplitude signal forces the main amplifier to adopt a conservative single-gain design, requiring the main amplifier circuit to amplify signals of all amplitudes (see [reference]). Figure 9 The schematic diagram of a disclosed main amplifier circuit is shown to prevent the signal from exceeding the range of the analog-to-digital converter. However, when detecting light elements, the amount of charge generated is amplified by the same gain link, resulting in an output voltage of only tens of millivolts. This, combined with circuit noise and quantization noise, leads to an effective signal-to-noise ratio of less than 5:1 and an energy resolution deteriorating to >200eV, which cannot meet the requirements for light element detection (requires FWHM < 50eV).

[0090] Therefore, to address this issue, embodiments of this application further incorporate a low-energy signal filtering circuit in the energy spectrum detector, as detailed in [reference needed]. Figure 10 The disclosed schematic diagram illustrates a low-energy signal filtering circuit. This circuit filters out high-energy signals, allowing only low-energy signals to pass through. Specifically, it determines that a characteristic electrical signal is a low-energy signal when the energy range of its elements is less than a preset first threshold, and filters out high-energy signals whose energy range exceeds the preset first threshold, thereby improving energy resolution.

[0091] Finally, the signal processing steps for the characteristic electrical signals in this embodiment are described, and the specific process includes the following three steps:

[0092] Step 1: Adaptively remove background noise and baseline drift from the characteristic electrical signal based on dynamic baseline correction technology to obtain the denoised electrical signal.

[0093] Since the amplitude and shape of an electrical signal directly reflect the energy and intensity of characteristic X-rays, dynamic baseline correction technology indirectly improves the energy resolution capability of characteristic X-rays by optimizing the processing of electrical signals.

[0094] The core of dynamic baseline correction technology lies in real-time tracking of low-frequency drift and background noise in the signal. In practice, the original electrical signal is first continuously acquired using a high-speed ADC, and then a digital filter bank is used to separate the high-frequency characteristic signal from the low-frequency baseline component. A sliding window mechanism is used to dynamically calculate the local baseline mean, and the baseline drift trend function is fitted using the least squares method. Finally, the time-varying baseline component is subtracted from the original signal in real time. This technology can suppress baseline drift to within ±0.1mV, while eliminating power supply frequency interference and 1 / f noise caused by detector leakage current, thereby improving the signal-to-noise ratio of light element characteristic peaks and laying the foundation for subsequent energy spectrum analysis.

[0095] Step 2: Perform energy spectrum modeling on the denoised electrical signal using the Markov chain Monte Carlo method to estimate the posterior distribution of the peak parameters corresponding to the characteristic rays;

[0096] Step 3: Based on the dynamic compensation algorithm based on pulse amplitude analysis and the posterior distribution of the peak parameters, quantify the energy distribution of the characteristic rays to optimize the response function of the energy spectrum detector.

[0097] Based on Markov chain Monte Carlo energy spectrum modeling, a Bayesian hierarchical model incorporating Gaussian peaks, exponential background, and escape peaks is constructed. A Gibbs sampler is used to iteratively solve for the posterior distributions of parameters such as peak position, peak width, and peak area. An adaptive step-size control strategy is implemented to shorten the burn-in phase to within 5000 iterations, while a NUTS sampler is used to improve convergence efficiency in high-dimensional parameter spaces. This modeling method can accurately deconvolute overlapping peaks with a peak position determination accuracy of ±2 eV. Combined with a dynamic compensation algorithm based on pulse amplitude analysis, the energy spectrum drift is corrected in real time by establishing a detector response matrix, ultimately optimizing the system's energy resolution to below 130 eV, significantly improving the simultaneous detection capability of light and heavy elements.

[0098] This application provides an energy spectrum analysis method, system, detector, and medium. The method first obtains the characteristics excited by the sample and the elemental energy range of the sample. Then, the characteristic rays are converted into characteristic electrical signals, and based on the elemental energy range of the sample, the digital signal values ​​in the characteristic electrical signals are dynamically assigned channels to obtain multiple optimized channels. This allows for dynamic channel assignment based on the actual energy levels of the constituent elements of the sample, ensuring the detector maintains high energy resolution for low-energy signals and solving the problem of low energy resolution for low-energy signals in traditional uniform channel assignment schemes. Furthermore, based on the dynamic channel assignment, the baseline noise energy spectrum corresponding to each optimized channel is obtained, and statistical differential noise reduction is performed on the digital signal values ​​at each optimized channel based on the baseline noise energy spectrum. This eliminates the influence of system electronic noise and environmental noise on energy resolution, thereby maximizing the elimination of system noise in the energy spectrum detector and effectively improving the energy resolution of the energy spectrum detector during energy spectrum analysis through statistical and differential methods.

[0099] This application provides an energy spectrum analysis method. First, it obtains the characteristics excited by the sample and the energy range of its elements. Then, the characteristic rays are converted into characteristic electrical signals, and based on the energy range of the sample's elements, the digital signal values ​​in these signals are dynamically assigned channels to obtain multiple optimized channels. This allows for dynamic channel assignment based on the actual energy levels of the constituent elements of the sample, ensuring the detector maintains high energy resolution for low-energy signals and solving the problem of low energy resolution for low-energy signals in traditional uniform channel assignment schemes. Furthermore, based on the dynamic channel assignment, the baseline noise energy spectrum corresponding to each optimized channel is obtained, and statistical differential noise reduction is performed on the digital signal values ​​at each optimized channel based on the baseline noise energy spectrum. This eliminates the influence of system electronic noise and environmental noise on energy resolution, thereby maximizing the elimination of system noise in the energy spectrum detector and effectively improving the energy resolution of the energy spectrum detector during energy spectrum analysis.

[0100] The following describes an energy spectrum analysis system provided in an embodiment of this application. The energy spectrum analysis system described below can be referred to in correspondence with the energy spectrum analysis method described above.

[0101] See Figure 11 The figure is a schematic diagram of the structure of an energy spectrum analysis system provided in an embodiment of this application, which specifically includes the following modules:

[0102] The acquisition module 100 is used to acquire the characteristic rays excited by the sample under test and the elemental energy range of the sample under test.

[0103] The address allocation module 200 is used to convert the characteristic ray into a characteristic electrical signal, and to dynamically allocate the digital signal value corresponding to the characteristic electrical signal based on the element energy range to obtain multiple optimized addresses;

[0104] The statistical differential noise reduction module 300 is used to acquire the baseline noise energy spectrum of each of the optimized channels, and perform statistical differential noise reduction processing on the digital signal values ​​at each of the optimized channels based on the baseline noise energy spectrum to generate an optimized energy spectrum for the characteristic ray; the baseline noise energy spectrum is used to characterize the energy spectrum collected by the energy spectrum detector when the energy spectrum detector does not perform energy spectrum detection on the sample under test.

[0105] In one possible implementation, the statistical differential noise reduction module 300 is specifically used for:

[0106] Obtain multiple sets of baseline noise energy spectra for each optimized address within a preset time period to calculate the average baseline noise value for each optimized address;

[0107] Multiple sets of digital signal values ​​for each optimized address are obtained within the preset time period to calculate the average signal value and signal value deviation for each optimized address.

[0108] Based on the average signal value, the average baseline noise value, and the signal value deviation of each optimized channel address, differential calculation is performed on the digital signal value of each optimized channel address to obtain the optimized digital signal value of each optimized channel address.

[0109] The optimized energy spectrum is generated based on the optimized digital signal values ​​of each optimized address.

[0110] On the other hand, embodiments of this application provide an energy spectrum detector, which is used to perform the energy spectrum analysis methods possible in any of the above embodiments.

[0111] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a computer-readable storage medium storing computer instructions for causing the computer to execute the energy spectrum analysis method as described in any of the above embodiments.

[0112] The computer-readable media in this application embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0113] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the energy spectrum analysis method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0114] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the methods, systems, energy spectrum detectors, and media are basically similar to the method embodiments, so the descriptions are relatively simple, and relevant parts can be referred to the descriptions of the method embodiments. The methods, systems, energy spectrum detectors, and media described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components indicated as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the solution in this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0115] The above description is merely one specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An energy dispersive spectroscopy (EDS) analysis method, characterized in that, When applied in an energy spectrum detector, the method includes: Obtain the characteristic rays excited by the sample under test and the elemental energy range of the sample under test; The characteristic ray is converted into a characteristic electrical signal, and the digital signal value corresponding to the characteristic electrical signal is dynamically allocated based on the energy range of the element to obtain multiple optimized addresses; The baseline noise energy spectrum of each optimized channel address is obtained, and statistical differential noise reduction processing is performed on the digital signal value of each optimized channel address based on the baseline noise energy spectrum to generate an optimized energy spectrum for the characteristic ray; the baseline noise energy spectrum is used to characterize the energy spectrum collected by the energy spectrum detector when the energy spectrum detector does not perform energy spectrum detection on the sample under test.

2. The method according to claim 1, characterized in that, The step of acquiring the baseline noise energy spectrum and performing statistical differential noise reduction processing on the digital signal values ​​at each of the optimized addresses based on the baseline noise energy spectrum to generate an optimized energy spectrum for the feature ray includes: Obtain multiple sets of baseline noise energy spectra for each optimized address within a preset time period to calculate the average baseline noise value for each optimized address; Multiple sets of digital signal values ​​for each optimized address are obtained within the preset time period to calculate the average signal value and signal value deviation for each optimized address. Based on the average signal value, the average baseline noise value, and the signal value deviation of each optimized channel address, differential calculation is performed on the digital signal value of each optimized channel address to obtain the optimized digital signal value of each optimized channel address. The optimized energy spectrum is generated based on the optimized digital signal values ​​of each optimized address.

3. The method according to claim 1, characterized in that, The energy spectrum detector includes: a charge-sensitive preamplifier, and the charge-sensitive preamplifier is provided with a preset controllable capacitor network; the preset controllable capacitor network is composed of multiple capacitor units and multiple corresponding switching units; the type of the switching unit is at least one of a single transistor and a transmission gate composed of complementary transistors. Before converting the characteristic ray into a characteristic electrical signal, the method further includes: Based on the energy range of the element, the switching units within the preset controllable capacitor network are controlled to turn off or merge, thereby adjusting the feedback capacitance value of the charge-sensitive preamplifier.

4. The method according to claim 1, characterized in that, The dynamic channel address allocation based on the element energy range for the digital signal value corresponding to the characteristic electrical signal yields multiple optimized channel addresses, including: The channel allocation density is determined based on the energy range of the elements; Dynamically allocate digital signal values ​​corresponding to the characteristic electrical signals according to the address allocation density to obtain the multiple optimized addresses.

5. The method according to claim 4, characterized in that, The energy spectrum detector includes a low-energy signal filtering circuit, which is used to filter out electrical signals in the characteristic electrical signals whose element energy range is greater than the preset first threshold when the element energy range is less than the preset first threshold.

6. The method according to claim 1, characterized in that, After converting the characteristic ray into a characteristic electrical signal, the method further includes: Based on dynamic baseline correction technology, background noise and baseline drift in the characteristic electrical signal are adaptively removed to obtain the denoised electrical signal; The energy spectrum of the denoised electrical signal is modeled using the Markov chain Monte Carlo method to estimate the posterior distribution of the peak parameters corresponding to the feature rays. The energy distribution of the characteristic rays is quantified based on a dynamic compensation algorithm based on pulse amplitude analysis and the posterior distribution of the peak parameters to optimize the response function of the energy spectrum detector.

7. An energy dispersive spectroscopy (EDS) analysis system, characterized in that, The system, used in an energy spectrum detector, includes: The acquisition module is used to acquire the characteristic rays excited by the sample under test and the elemental energy range of the sample under test; The channel address allocation module is used to convert the characteristic ray into a characteristic electrical signal, and to dynamically allocate the digital signal value corresponding to the characteristic electrical signal based on the element energy range to obtain multiple optimized channels. The statistical differential noise reduction module is used to acquire the baseline noise energy spectrum of each of the optimized channels, and to perform statistical differential noise reduction processing on the digital signal values ​​of each of the optimized channels based on the baseline noise energy spectrum to generate an optimized energy spectrum for the feature ray; the baseline noise energy spectrum is used to characterize the energy spectrum collected by the energy spectrum detector when the energy spectrum detector does not perform energy spectrum detection on the sample under test.

8. The system according to claim 7, characterized in that, The statistical difference noise reduction module is specifically used for: Obtain multiple sets of baseline noise energy spectra for each optimized address within a preset time period to calculate the average baseline noise value for each optimized address; Multiple sets of digital signal values ​​for each optimized address are obtained within the preset time period to calculate the average signal value and signal value deviation for each optimized address. Based on the average signal value, the average baseline noise value, and the signal value deviation of each optimized channel address, differential calculation is performed on the digital signal value of each optimized channel address to obtain the optimized digital signal value of each optimized channel address. The optimized energy spectrum is generated based on the optimized digital signal values ​​of each optimized address.

9. An energy spectrum detector, characterized in that, The energy spectrum detector is used to perform the energy spectrum analysis method according to any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the energy spectrum analysis method as described in any one of claims 1-6.

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