Ion beam energy self-adaptive regulation and control method and system for FIB equipment

Abstract

The invention provides an ion beam energy adaptive regulation and control method and system for FIB equipment, and the method comprises the steps: obtaining an energy distribution parameter of an ion beam in a focusing region, and determining a target energy adjustment range according to the energy distribution parameter and a preset ion beam energy reference model, generating a multi-stage energy adjustment instruction based on the target energy adjustment range, controlling an output parameter of an ion beam energy adjuster through the multi-stage energy adjustment instruction, enabling the current energy distribution of the ion beam to enter the target energy adjustment range, and detecting the energy fluctuation data of the adjusted ion beam on the surface of the sample in real time, and according to the energy fluctuation data and a preset adaptive compensation algorithm, adjusting feedback control parameters of the ion beam energy regulator, so that the energy distribution of the ion beam is maintained within the target energy regulation range. By means of the method, better machining surface quality and process consistency of industrial machining can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical fields of data processing and intelligent control, and particularly to an ion beam energy adaptive regulation method and system for a FIB device. Background Art

[0002] Ion beam energy regulation is the core technology for a focused ion beam (FIB) device to achieve nanoscale processing accuracy. Existing methods usually rely on static threshold control of energy parameters (such as beam current intensity or magnetic field intensity), and linearly feedback regulate the ion beam energy through a fixed adjustment model. However, such methods are prone to a decrease in energy distribution uniformity and focal point drift, especially during long-term high-precision processing tasks, which are likely to cause an increase in the surface roughness of the processed surface and structural distortion. At the same time, the existing technology cannot dynamically optimize the adjustment strategy according to the non-linear relationship between the energy attenuation gradient and the diffusion rate, resulting in adjustment lag and overshoot oscillation, and frequent interruption of tasks for manual calibration when environmental parameters change suddenly or hardware performance fluctuates, seriously restricting the processing efficiency and process consistency. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide an ion beam energy adaptive regulation method and system for a FIB device. The technical solution of the present invention is realized as follows:

[0004] On the one hand, embodiments of the present invention provide an ion beam energy adaptive regulation method for a FIB device, including the following steps: obtaining energy distribution parameters of the ion beam in the focusing area, where the energy distribution parameters include ion beam current intensity, energy attenuation gradient, and spatial distribution characteristics; determining a target energy regulation range according to the energy distribution parameters and a preset ion beam energy reference model, where the target energy regulation range includes an allowable peak energy deviation threshold and a minimum energy gradient change rate; generating multi-level energy regulation instructions based on the target energy regulation range, and controlling the output parameters of the ion beam energy regulator through the multi-level energy regulation instructions to make the current energy distribution of the ion beam enter the target energy regulation range; detecting in real time the energy fluctuation data of the regulated ion beam on the sample surface, where the energy fluctuation data includes instantaneous energy offset and focal point energy diffusion coefficient; adjusting the feedback control parameters of the ion beam energy regulator according to the energy fluctuation data and a preset adaptive compensation algorithm to maintain the energy distribution of the ion beam within the target energy regulation range.

[0005] On the other hand, embodiments of the present invention provide a computer system, including a memory and a processor, where the memory stores a computer program that can run on the processor, and when the processor executes the program, the steps in the above method are implemented.

[0006] The ion beam energy adaptive regulation method for FIB equipment provided by the present invention obtains the energy distribution parameters of the ion beam in the focusing area in real time and dynamically determines the target energy regulation range based on the ion beam energy reference model, generates multi-level energy regulation instructions to control the output parameters of the regulator, and detects the energy fluctuation data after regulation in real time and dynamically adjusts the feedback control parameters through the adaptive compensation algorithm, so that the ion beam current intensity, energy attenuation gradient and spatial distribution characteristics form a multi-dimensional collaborative regulation mechanism, which can effectively suppress the random fluctuation and systematic drift of the ion beam energy in the dynamic working environment, and significantly improve the focusing accuracy and energy distribution uniformity; by using the product of the energy attenuation gradient and the diffusion radius as the calculation basis for the focusing accuracy compensation amount, the coupling control of the spatial distribution characteristics and the time-sequence attenuation characteristics is realized, so that the magnetic field gradient compensation instruction and the pulse time-sequence regulation signal are strictly synchronized, and the time-sequence conflict problem of multi-parameter regulation in the traditional method is solved; and based on the adaptive compensation algorithm of the non-linear feedback control cycle compression mechanism and the exponential decay rule, the regulation step size and control frequency can be dynamically adjusted according to the error intensity, avoiding overshoot oscillation while converging quickly, and ensuring the system stability in the high-precision processing scenario; in addition, through the intelligent matching of the environmental parameter snapshots and regulation efficiency fingerprints in the historical regulation database, the rapid reuse of parameter configuration under complex working conditions is realized, significantly shortening the regulation time of repetitive tasks, and enhancing the adaptability of the equipment to different vacuum environments and ion source attenuation states, so as to achieve better processing surface quality and process consistency in applications such as etching and three-dimensional reconstruction.

[0007] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and do not limit the technical solution of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The drawings herein are incorporated into the specification and form a part of this specification, which illustrate embodiments consistent with the present invention and, together with the specification, are used to explain the technical solution of the present invention.

[0009] Figure 1 It is a schematic flow chart of the implementation of an ion beam energy adaptive regulation method for FIB equipment provided by an embodiment of the present invention.

[0010] Figure 2 It is a schematic diagram of the hardware entity of a computer system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0011] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be further elaborated in detail below in conjunction with the drawings and embodiments. The described embodiments should not be regarded as limitations of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0012] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict. The terms "first / second / third" involved are only used to distinguish similar objects and do not represent a specific order for the objects. It is understood that "first / second / third" may be interchanged with a specific order or sequence when allowed, so that the embodiments of the present invention described herein can be implemented in an order other than that illustrated or described herein.

[0013] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used herein are only for the purpose of describing the present invention and are not intended to limit the present invention.

[0014] An embodiment of the present invention provides an ion beam energy adaptive regulation method for a FIB device, and this method can be executed by a processor of a computer system. Among them, the computer system may refer to a computer system embedded in the FIB device or connected to the FIB device.

[0015] Figure 1 It is a schematic flowchart of the implementation of an ion beam energy adaptive regulation method for a FIB device provided by an embodiment of the present invention, as Figure 1 shown, and this method includes:

[0016] Step S100: Obtain the energy distribution parameters of the ion beam in the focusing area, and the energy distribution parameters include the ion beam current intensity, the energy attenuation gradient, and the spatial distribution characteristics.

[0017] In this step, the ion beam current intensity refers to the number of ions passing through a certain cross-section per unit time, which reflects the energy carrying capacity of the ion beam. The greater the ion beam current intensity, the more energy is transferred in the same time. The energy attenuation gradient refers to the attenuation rate of the ion beam energy during propagation with respect to distance or time, which reflects the stability and distribution uniformity of the ion beam energy. The magnitude of the energy attenuation gradient will affect the action effect of the ion beam in the focusing area. The spatial distribution characteristics describe the distribution of the ion beam energy in the three-dimensional space of the focusing area, including the central position where the energy is concentrated and the range of energy diffusion, which is convenient for accurately controlling the action position and range of the ion beam.

[0018] In order to obtain these energy distribution parameters, it specifically includes the following steps:

[0019] Step S110: Synchronously collect the original energy signals of the ion beam at different angles in the focusing area through a multi-axis energy detection array arranged in a ring. Each detection unit of the multi-axis energy detection array corresponds to a preset radial detection angle, and there is partial overlap in the signal acquisition areas of adjacent detection units.

[0020] The multi-axis energy detection array is a device composed of multiple detection units arranged in a ring, which can detect the ion beam in the focusing area from different angles. Each detection unit has its preset radial detection angle, so they can collect signals for the ion beam in a specific direction. There is partial overlap in the signal acquisition areas of adjacent detection units, aiming to ensure that the energy signals in the focusing area can be comprehensively and accurately collected, avoiding signal acquisition blind spots. For example, when the energy distribution of the ion beam in the focusing area is uneven, the overlapping acquisition areas can ensure that the energy signals at each position can be detected.

[0021] Step S120: Perform time-domain denoising processing on the original energy signals to generate a denoised energy waveform cluster after eliminating the high-frequency electromagnetic interference components. The denoised energy waveform cluster contains the waveform data corresponding to each detection unit and spatial position tags.

[0022] The original energy signals may be affected by high-frequency electromagnetic interference during the acquisition process, and these interferences will make the signals inaccurate, so time-domain denoising processing is required. Time-domain denoising processing is a method of filtering signals in the time domain, which can identify and eliminate high-frequency electromagnetic interference components, thereby obtaining a purer energy signal. The denoised signal will generate a denoised energy waveform cluster, where each waveform data corresponds to a detection unit, and it also contains the spatial position tag of this detection unit. These tags are used to determine the specific position of each waveform data in the focusing area. For example, by performing a fast Fourier transform on the collected original energy signals, converting them to the frequency domain, then removing the interference components in the high-frequency part, and then converting the signals back to the time domain through an inverse Fourier transform, the denoised energy waveform cluster can be obtained.

[0023] Step S130: Select the reference waveform with the largest energy integral value among all detection units.

[0024] The energy integral value refers to the cumulative value of the energy signal over a period of time, which reflects the total energy of the ion beam received by this detection unit. Selecting the reference waveform with the largest energy integral value is because this waveform can represent the part with the most concentrated energy in the focusing area, and subsequent parameter calculations will be based on this reference waveform. For example, perform integral calculations on the energy signals of each detection unit, compare the magnitudes of each integral value, and select the waveform with the largest integral value as the reference waveform.

[0025] Step S140: Intercept the energy maintenance interval in the steady state phase of the reference waveform, and calculate the average value of the waveform peaks within the energy maintenance interval as the ion beam current intensity.

[0026] The steady state phase of the reference waveform refers to the state where the waveform remains relatively stable over a period of time. During this phase, the energy output of the ion beam is relatively stable. Intercepting the energy maintenance interval is to perform parameter calculations within this stable phase to obtain a more accurate ion beam current intensity. By calculating the average value of the waveform peaks within the energy maintenance interval, a value that can represent the ion beam current intensity can be obtained. For example, in the reference waveform, determine a time period when the waveform is relatively stable as the energy maintenance interval, sum up the waveform peaks within this interval, and then divide by the number of peaks to obtain the average value, which is the ion beam current intensity.

[0027] Step S150: Divide the decay phase of the reference waveform into multiple sampling windows with equal time intervals.

[0028] The decay phase of the reference waveform refers to the phase when the energy of the ion beam starts to gradually decrease. Dividing the sampling windows with equal time intervals is to conduct a detailed analysis of the energy change during the decay phase. By calculating the energy change rate within different sampling windows, information about the energy decay gradient can be obtained. For example, according to the preset time interval, divide the decay phase of the reference waveform into multiple sampling windows of equal length, and each window contains multiple energy data points.

[0029] Step S160: Calculate the decline rate of the energy peak within each sampling window, and obtain the weighted average value of the decline rates of all sampling windows as the energy decay gradient.

[0030] The decline rate of the energy peak within each sampling window reflects the attenuation of the ion beam energy during that time period. By calculating the weighted average value of the decline rates of all sampling windows, a comprehensive energy decay gradient can be obtained, which can more accurately describe the change trend of the ion beam energy during the entire decay phase. For example, for each sampling window, calculate the difference between the energy peaks at the start and end of the window, and then divide by the time length of the window to obtain the decline rate of the energy peak within that window. Then, assign different weights according to the importance or representativeness of each sampling window, and perform a weighted average calculation on all decline rates to obtain the energy decay gradient.

[0031] Step S170: Perform spatial interpolation calculations on the spatial position labels of each detection unit and the corresponding waveform energy integral values to generate a three-dimensional energy density surface covering the focused area.

[0032] Spatial interpolation calculation is a method of estimating the information of unknown points based on the information of known points. In this step, the spatial position tags of each detection unit and the energy integral values of the corresponding waveforms are used as known points, and the energy density at other positions within the focusing region is calculated through a spatial interpolation algorithm, thereby generating a three-dimensional energy density surface covering the entire focusing region. This surface can intuitively display the spatial distribution of the ion beam energy within the focusing region. For example, using the Kriging interpolation algorithm, based on the spatial positions and energy integral values of each detection unit, the energy density at other positions within the focusing region is estimated, and finally a three-dimensional energy density surface is generated. The Kriging interpolation algorithm is a spatial interpolation method based on statistics. When generating the three-dimensional energy density surface, the spatial positions of each detection unit and the corresponding energy integral values are used as basic data. Analyze the spatial correlation of these known data points, that is, determine the relationship between the spatial distance between data points and the difference in energy integral values. Based on this correlation, a semi-variogram model is constructed, which can describe the variation law of energy in space. Then, for the unknown positions within the focusing region where the energy density needs to be estimated, the Kriging algorithm uses a weighted combination of known data points for estimation. The weights are determined based on the previously constructed semi-variogram model. The data points that are closer to the unknown point and have stronger correlation have larger weights. By estimating the energy density at all unknown positions within the focusing region and finally integrating these estimated values, a three-dimensional energy density surface covering the entire focusing region can be generated, intuitively presenting the spatial distribution of the ion beam energy.

[0033] Step S180: Identify the spatial coordinates of the energy density extreme points in the three-dimensional energy density surface, perform a density gradient scan from the extreme points to the surrounding area, and determine the boundary surface range where the energy density drops to a preset critical value.

[0034] The energy density extreme points refer to the points with the maximum or minimum energy density in the three-dimensional energy density surface, and these points represent the key positions where energy is concentrated or dispersed in the focusing region. Performing a density gradient scan from the extreme points to the surrounding area is to determine the rate and range of the energy density decrease starting from the extreme points. When the energy density drops to the preset critical value, the corresponding surface range is the boundary surface range to be determined. This boundary surface range can clarify the effective action range of the ion beam energy in the focusing region. For example, by performing a derivative operation on the three-dimensional energy density surface, the extreme points of the energy density are found, and then a point-by-point scan is performed from this point to the surrounding area to calculate the energy density gradient. When the energy density drops to the preset critical value, the corresponding points are recorded, and connecting these points forms the boundary surface range.

[0035] Step S190: Define the spatial coordinates of the extreme points as the central position of the spatial distribution characteristics, and define the maximum radial extension distance of the boundary surface range as the diffusion radius of the spatial distribution characteristics.

[0036] The spatial coordinates of the extreme point represent the position where the ion beam energy is most concentrated in the focusing region. Therefore, it is defined as the central position of the spatial distribution characteristics. The maximum radial extension distance of the boundary surface range reflects the degree of diffusion of the ion beam energy in space, which is defined as the diffusion radius of the spatial distribution characteristics. Through the central position and the diffusion radius, the spatial distribution characteristics of the ion beam energy in the focusing region can be described simply and accurately. For example, in a three-dimensional coordinate system, record the coordinates of the extreme point of the energy density as the central position, measure the radial distances of the boundary surface range in each direction, and take the maximum value as the diffusion radius.

[0037] Step S1100: Combine the ion beam intensity, the energy attenuation gradient, and the spatial distribution characteristics including the central position and the diffusion radius into energy distribution parameters.

[0038] Through the previous steps, the ion beam intensity, the energy attenuation gradient, and the spatial distribution characteristics are obtained respectively. Combining them together forms the complete energy distribution parameters. These parameters can comprehensively describe the energy distribution of the ion beam in the focusing region and provide an important basis for subsequent energy adjustment.

[0039] Step S200: Determine the target energy adjustment range according to the energy distribution parameters and the preset ion beam energy reference model. The target energy adjustment range includes the allowable peak energy deviation threshold and the minimum energy gradient change rate.

[0040] In this step, the preset ion beam energy reference model is a pre-established model that contains the energy distribution characteristics and related parameters of the ion beam under ideal conditions, such as the reference current intensity, the gradient history curve, etc. By comparing and analyzing the obtained energy distribution parameters with this reference model, the difference between the current ion beam energy and the ideal state can be determined, so as to determine the target energy adjustment range. The allowable peak energy deviation threshold refers to the maximum range that the peak energy of the ion beam is allowed to deviate from the reference value in the reference model. It is used to constrain the amplitude change range of the ion source current adjustment instruction to ensure that the peak energy of the ion beam is within an acceptable error range. The minimum energy gradient change rate refers to the minimum change rate allowed for the energy attenuation gradient of the ion beam. It is used to define the stepping accuracy threshold for the adjustment of the focusing lens magnetic field to ensure that the energy attenuation process of the ion beam meets the expectations.

[0041] The specific steps to determine the target energy adjustment range are as follows:

[0042] Step S210: Compare the deviation between the ion beam intensity and the reference current intensity recorded in the ion beam energy reference model to generate a current intensity deviation rate, and select the initial peak energy deviation threshold from the preset dynamic adjustment rule table according to the current intensity deviation rate.

[0043] The beam current deviation rate refers to the deviation ratio between the ion beam current intensity and the reference beam current intensity, which reflects the degree of difference between the current ion beam current intensity and the ideal state. The preset dynamic adjustment rule table is a pre-established table that corresponds different initial peak energy deviation thresholds according to different beam current deviation rates. By matching the beam current deviation rate with the dynamic adjustment rule table, an appropriate initial peak energy deviation threshold can be selected. For example, calculate the difference between the ion beam current intensity and the reference beam current intensity, and then divide it by the reference beam current intensity to obtain the beam current deviation rate. Then, look up the initial peak energy deviation threshold corresponding to this deviation rate in the dynamic adjustment rule table.

[0044] Step S220: Based on the energy attenuation gradient and the gradient history curve stored in the ion beam energy reference model, calculate the gradient deviation degree, which is the maximum difference between the current gradient and the historical gradient curve within the same time window.

[0045] The gradient history curve records the changes in the ion beam energy attenuation gradient over a past period of time. By comparing the current energy attenuation gradient with the gradient history curve within the same time window and calculating the maximum difference between them, the gradient deviation degree is obtained. The gradient deviation degree reflects the degree of difference between the current energy attenuation gradient and the historical situation, which can help judge the stability of the current ion beam energy attenuation. For example, within the same time window, compare the values of the current energy attenuation gradient with the values at the corresponding time points on the gradient history curve, and find the maximum difference as the gradient deviation degree.

[0046] Step S230: Non-linearly correct the initial peak energy deviation threshold according to the gradient deviation degree. If the gradient deviation degree exceeds the preset gradient stability interval, reduce the initial peak energy deviation threshold according to the square ratio of the gradient deviation degree to generate the corrected peak energy deviation threshold.

[0047] The preset gradient stability interval is a pre-set range. When the gradient deviation degree is within this range, it indicates that the ion beam energy attenuation gradient is relatively stable; when the gradient deviation degree exceeds this range, it indicates that the energy attenuation gradient is unstable and the initial peak energy deviation threshold needs to be corrected. Reducing the initial peak energy deviation threshold according to the square ratio of the gradient deviation degree is to more strictly constrain the peak energy of the ion beam to improve the stability of the ion beam energy. For example, if the gradient deviation degree is x and the preset gradient stability interval is [a, b], when x > b, multiply the initial peak energy deviation threshold by (1 / x 2 ), to obtain the corrected peak energy deviation threshold.

[0048] Step S240: Extract the diffusion radius in the spatial distribution features, input it into a pre-configured focusing accuracy compensator, output an accuracy compensation coefficient that is negatively correlated with the diffusion radius, and scale the preset minimum energy gradient change rate in the ion beam energy reference model according to the accuracy compensation coefficient.

[0049] The pre-configured focusing accuracy compensator is a pre-set module that outputs a corresponding accuracy compensation coefficient according to the input diffusion radius.

[0050] The focusing accuracy compensator is essentially a functional module based on algorithms and models. Its core purpose is to adjust the parameters related to focusing accuracy according to the spatial distribution characteristics of the ion beam energy. From its input perspective, it receives the diffusion radius in the spatial distribution features. The diffusion radius is an important indicator describing the degree of diffusion of the ion beam energy in space. The larger the diffusion radius, the more dispersed the ion beam energy in space, and the lower the focusing accuracy; conversely, the smaller the diffusion radius, the more concentrated the ion beam energy, and the higher the focusing accuracy. The focusing accuracy compensator processes the input diffusion radius using the algorithms and models preset within itself. These algorithms and models are established based on a large amount of experimental data and theoretical analysis, and they reflect the internal relationship between the diffusion radius and the focusing accuracy. The output of the focusing accuracy compensator is an accuracy compensation coefficient that is negatively correlated with the diffusion radius. That is to say, when the diffusion radius increases, the accuracy compensation coefficient will decrease accordingly; when the diffusion radius decreases, the accuracy compensation coefficient will increase. This negative correlation is to achieve effective compensation for the focusing accuracy. For example, when the diffusion radius is large, it indicates that the focusing effect of the ion beam is poor. At this time, a smaller accuracy compensation coefficient is required to adjust the subsequent adjustment parameters to enhance the adjustment strength for the focusing accuracy; when the diffusion radius is small, the focusing effect of the ion beam is good. At this time, a larger accuracy compensation coefficient can be used to appropriately relax the adjustment requirements for the focusing accuracy.

[0051] Taking a specific implementation of the focusing accuracy compensator as an example, it adopts, for example, the algorithm of polynomial fitting. According to a large amount of experimental data, it is found that there is approximately a relationship of k = a - b*r 2 between the diffusion radius r and the accuracy compensation coefficient k (where a and b are constants obtained by fitting the experimental data). After inputting the diffusion radius r, the focusing accuracy compensator calculates the corresponding accuracy compensation coefficient k according to this polynomial formula.

[0052] After obtaining the precision compensation coefficient, it is used to scale the preset minimum energy gradient change rate in the ion beam energy reference model. The preset minimum energy gradient change rate is an important parameter for ion beam energy adjustment, which stipulates the minimum change rate allowed for the energy attenuation gradient. By scaling it with the precision compensation coefficient, the adjustment precision of the energy gradient can be dynamically adjusted according to the actual focusing situation of the ion beam. For example, if the precision compensation coefficient is 0.8 and the preset minimum energy gradient change rate is G0, then the scaled minimum energy gradient change rate becomes 0.8 * G0. In this way, during the subsequent energy adjustment process, the change of the energy attenuation gradient can be more accurately controlled according to the focusing state of the ion beam, thereby improving the focusing precision of the ion beam and the accuracy of energy adjustment.

[0053] Step S250: Combine the corrected peak energy deviation threshold and the scaled minimum energy gradient change rate into a target energy adjustment range, where the peak energy deviation threshold is used to constrain the amplitude change range of the ion source current adjustment instruction, and the minimum energy gradient change rate is used to define the step precision threshold for the focusing lens magnetic field adjustment.

[0054] Through the previous steps, the corrected peak energy deviation threshold and the scaled minimum energy gradient change rate are obtained respectively, and combining them together forms the target energy adjustment range. This range clarifies the goals and constraints of ion beam energy adjustment, providing a basis for the generation of subsequent energy adjustment instructions.

[0055] Step S300: Generate multi-level energy adjustment instructions based on the target energy adjustment range, and control the output parameters of the ion beam energy regulator through the multi-level energy adjustment instructions to make the current energy distribution of the ion beam enter the target energy adjustment range.

[0056] The multi-level energy adjustment instructions are a series of instructions used to adjust the ion beam energy, which are executed in sequence and priority to gradually adjust the energy distribution of the ion beam to make it reach the target energy adjustment range. The ion beam energy regulator is a device used to control the ion beam energy, which adjusts its output parameters such as ion source current, pulse timing, focusing lens magnetic field, etc. according to the multi-level energy adjustment instructions, so as to achieve precise adjustment of the ion beam energy.

[0057] The specific steps for generating the multi-level energy adjustment instructions are as follows:

[0058] Step S310: Decompose the target energy adjustment range into multiple energy adjustment stages, each energy adjustment stage corresponding to a different energy adjustment priority, where: In the first adjustment stage, the ion beam current intensity is preferentially adjusted to a preset reference intensity range; in the second adjustment stage, the ion beam pulse width is adjusted according to the energy attenuation gradient so that the energy attenuation rate matches the minimum energy gradient change rate; in the third adjustment stage, based on the diffusion radius of the spatial energy distribution matrix, the magnetic field intensity of the focusing lens is adjusted so that the position coordinates of the maximum energy density region coincide with the preset focusing center point.

[0059] The preset reference intensity range is a pre-set range. The goal of the first adjustment stage is to adjust the ion beam current intensity to this range to ensure that the energy carrying capacity of the ion beam meets the requirements. The second adjustment stage controls the energy attenuation rate by adjusting the ion beam pulse width to match the minimum energy gradient change rate, thus ensuring the stability of the ion beam energy attenuation process. The third adjustment stage adjusts the magnetic field intensity of the focusing lens according to the diffusion radius of the spatial energy distribution matrix so that the position of the maximum energy density region coincides with the preset focusing center point, improving the focusing accuracy of the ion beam. For example, in the first adjustment stage, by increasing or decreasing the current of the ion source, the ion beam current intensity gradually approaches the reference intensity range; in the second adjustment stage, according to the magnitude of the energy attenuation gradient, the width of the ion beam pulse is adjusted so that the energy attenuation rate meets the requirements; in the third adjustment stage, according to the magnitude of the diffusion radius, the excitation current of the focusing lens is adjusted to change the magnetic field intensity so that the maximum energy density region moves to the preset focusing center point.

[0060] Step S320: Allocate corresponding time windows for each energy adjustment stage, and generate corresponding adjustment instruction sequences within each time window. The adjustment instruction sequences include:

[0061] Ion source current adjustment instruction, used to control the output current of the ion emitter;

[0062] Pulse timing adjustment instruction, used to modify the rise time and duration of the ion beam pulse;

[0063] Magnetic field gradient control instruction, used to change the excitation current of the focusing lens to adjust the magnetic field distribution pattern.

[0064] Time windows are assigned to each energy regulation stage to ensure that there is sufficient time for the regulation of each stage and that they can be executed sequentially in order. Corresponding regulation instruction sequences are generated within each time window, and the output parameters of the ion beam energy regulator are controlled through these instructions. The ion source current regulation instruction adjusts the ion beam current intensity by controlling the output current of the ion emitter; the pulse timing adjustment instruction controls the energy decay rate by modifying the rise time and duration of the ion beam pulse; the magnetic field gradient control instruction adjusts the magnetic field distribution pattern by changing the excitation current of the focusing lens, thereby achieving the adjustment of the ion beam focusing position.

[0065] The steps of specifically assigning time windows to each energy regulation stage and generating a regulation instruction sequence are as follows:

[0066] Step S321: Determine the priority order of each energy regulation stage according to the regulation urgency weights of the peak energy deviation threshold and the minimum energy gradient change rate in the target energy regulation range. The regulation urgency weight is calculated by multiplying the influence coefficient of the peak energy deviation threshold on the system stability by the sensitivity coefficient of the minimum energy gradient change rate to the focusing accuracy.

[0067] The regulation urgency weight reflects the importance of each regulation parameter to the system performance and the urgency of the regulation. By calculating the product of the influence coefficient of the peak energy deviation threshold on the system stability and the sensitivity coefficient of the minimum energy gradient change rate to the focusing accuracy, the regulation urgency weight is obtained. The priority order of each energy regulation stage is determined according to the regulation urgency weight, and the stage with a higher priority is regulated first. For example, if the peak energy deviation threshold has a greater impact on the system stability and the minimum energy gradient change rate also has a greater impact on the focusing accuracy, then the regulation stages corresponding to these two parameters have a higher priority.

[0068] The peak energy deviation threshold refers to the maximum range within which the peak energy of the ion beam is allowed to deviate from the reference value in the reference model, which is related to the stability of the ion beam energy. In the ion beam energy regulation system, if the peak energy of the ion beam deviates too much from the reference value, it will have a significant impact on the stability of the entire system. For example, when the peak energy is too high, it may cause the ion beam to act too strongly on the sample, resulting in problems such as sample damage and decreased processing accuracy; while when the peak energy is too low, the ion beam may not achieve the expected processing or analysis effect, affecting the normal operation of the system. To quantify this degree of influence, an influence coefficient is introduced. It can be obtained through experimental analysis and reflects how much the system stability will be affected when the peak energy deviation threshold changes by one unit. Specifically, by monitoring and analyzing various performance indicators of the system (such as the stability of the ion beam current, the consistency of processing accuracy, etc.) under different peak energy deviation threshold conditions, and then calculating the influence coefficient based on the changes of these indicators. For example, through a series of experiments, it is found that when the peak energy deviation threshold increases by 1%, the consistency of the processing accuracy of the system decreases by 0.5%, then the influence coefficient can be determined according to this relationship.

[0069] The minimum energy gradient change rate refers to the minimum change rate allowed for the energy attenuation gradient of the ion beam, which mainly affects the focusing accuracy of the ion beam. In the processing of semiconductor chips, precise focusing can ensure that the ion beam acts accurately on the target position, improving the processing accuracy and quality. The magnitude of the minimum energy gradient change rate directly affects the attenuation of the ion beam energy during propagation, and thus affects the focusing effect. When the minimum energy gradient change rate is too large or too small, it may cause problems such as the shift of the ion beam focus point and uneven energy distribution, reducing the focusing accuracy. The sensitivity coefficient is an index used to measure how much a small change in the minimum energy gradient change rate affects the focusing accuracy. Similarly, this sensitivity coefficient is also determined through experiments and data analysis. Under different minimum energy gradient change rate conditions, parameters such as the position of the ion beam focus point and the energy distribution can be measured, and the variation law of these parameters with the minimum energy gradient change rate can be analyzed, thereby calculating the sensitivity coefficient. For example, it is found through experiments that when the minimum energy gradient change rate increases by 0.1, the offset of the focus point increases by 0.2 micrometers, and the corresponding sensitivity coefficient can be determined according to such a relationship.

[0070] Multiply these two coefficients to obtain the adjustment urgency weight. This calculation method is because both system stability and focusing accuracy are indispensable important factors in ion beam energy adjustment, and they are interrelated and interact with each other. By integrating their influence degrees, the importance and adjustment urgency of each adjustment parameter can be evaluated more comprehensively. The greater the adjustment urgency weight, the more significant the influence of the adjustment parameter on the overall performance of the system, and the higher the priority of the corresponding energy adjustment stage. For example, if the influence coefficient of the peak energy deviation threshold on system stability is 0.6 and the sensitivity coefficient of the minimum energy gradient change rate to focusing accuracy is 0.8, then the adjustment urgency weight is 0.6×0.8 = 0.48. When determining the priority of the energy adjustment stage, adjust the parameters with larger adjustment urgency weights first according to this weight value to ensure that the system can quickly and effectively reach the target energy adjustment range while ensuring the system stability and focusing accuracy.

[0071] Step S322: Allocate an initial time window for the first adjustment stage based on the priority order. The duration of the initial time window is inversely proportional to the difference between the peak energy deviation threshold and the current ion beam current intensity, and allocate a dynamic time window for the second adjustment stage. The start time of the dynamic time window is determined by the sum of the end time of the initial time window and a preset buffer interval.

[0072] The duration of the initial time window is determined according to the difference between the peak energy deviation threshold and the current ion beam current intensity. The larger the difference, the greater the gap between the ion beam current intensity and the target value, and the more time is required for adjustment. Therefore, the duration of the initial time window is longer; conversely, the smaller the difference, the shorter the duration of the initial time window. The start time of the dynamic time window starts after a preset buffer interval after the end of the initial time window, which can avoid interference between different adjustment stages. For example, if the peak energy deviation threshold is A, the current ion beam current intensity is B, and the difference is |A - B|, then the duration T1 of the initial time window can be expressed as T1 = k / |A - B|, where k is a constant. The preset buffer interval is t, then the start time of the dynamic time window is the end time of the initial time window plus t.

[0073] Step S323: Calculate the time window length of the third adjustment stage according to the diffusion radius of the spatial distribution characteristics and the maximum adjustment rate of the focusing lens. The time window length is proportional to the square root of the diffusion radius and starts immediately after the end of the dynamic time window.

[0074] The larger the diffusion radius of the spatial distribution characteristics, the greater the degree of diffusion of the ion beam energy in space, and more time is required to adjust the magnetic field strength of the focusing lens to move the maximum energy density region to the preset focusing center point. The maximum adjustment rate of the focusing lens limits the speed of magnetic field strength adjustment. Therefore, the length of the time window in the third adjustment stage is proportional to the square root of the diffusion radius. For example, if the diffusion radius is r and the maximum adjustment rate of the focusing lens is v, then the length of the time window T3 in the third adjustment stage can be expressed as T3 = m * √r / v, where m is a constant. The start time of the third adjustment stage immediately follows the end time of the dynamic time window to ensure the continuity of the adjustment process.

[0075] Step S324: Generate an ion source current adjustment instruction sequence within the initial time window of the first adjustment stage. The step size of the ion source current adjustment instruction sequence is dynamically adjusted by the difference between the peak energy deviation threshold and the current ion beam intensity, and the interval time between adjacent instructions is inversely proportional to the change rate of the difference.

[0076] The step size of the ion source current adjustment instruction sequence is dynamically adjusted according to the difference between the peak energy deviation threshold and the current ion beam intensity. The larger the difference, the larger the step size, so as to approach the target value faster; the smaller the difference, the smaller the step size to improve the adjustment accuracy. The interval time between adjacent instructions is inversely proportional to the change rate of the difference. The larger the change rate of the difference, the faster the change of the ion beam intensity, and more frequent adjustment instructions need to be sent, so the interval time between adjacent instructions is shorter; conversely, the smaller the change rate of the difference, the longer the interval time between adjacent instructions. For example, if the peak energy deviation threshold is A, the current ion beam intensity is B, the difference is |A - B|, and the change rate of the difference is Δ|A - B| / Δt, then the step size ΔI of the ion source current adjustment instruction sequence can be expressed as ΔI = n * |A - B|, where n is a constant, and the interval time ΔT between adjacent instructions can be expressed as ΔT = p / (Δ|A - B| / Δt), where p is a constant.

[0077] Step S325: Generate a pulse timing adjustment instruction sequence within the dynamic time window of the second adjustment stage. The trigger interval of the pulse timing adjustment instructions therein is determined by the ratio of the energy attenuation gradient to the change rate of the minimum energy gradient, and the duration of each pulse timing adjustment instruction is proportional to the length of the dynamic time window.

[0078] The triggering interval of the pulse timing adjustment instruction is determined according to the ratio of the energy attenuation gradient to the minimum energy gradient change rate. The larger the ratio, the greater the difference between the current energy attenuation gradient and the target value, and the more frequently the pulse timing needs to be adjusted. Therefore, the triggering interval is shorter; conversely, the smaller the ratio, the longer the triggering interval. The duration of each pulse timing adjustment instruction is proportional to the length of the dynamic time window. The longer the dynamic time window, the more time there is for pulse timing adjustment. Therefore, the duration of each instruction is also longer. For example, if the energy attenuation gradient is G and the minimum energy gradient change rate is G0, then the triggering interval ΔT2 of the pulse timing adjustment instruction can be expressed as ΔT2 = q / (G / G0), where q is a constant. The length of the dynamic time window is T2, then the duration Δt of each pulse timing adjustment instruction can be expressed as Δt = r*T2, where r is a constant.

[0079] Step S326: Generate a magnetic field gradient control instruction group within the time window of the third adjustment stage. The amplitudes of the control signals in the magnetic field gradient control instruction group are jointly determined by the diffusion radius and the magnetic field response sensitivity of the focusing lens, and the alternating frequency of the control signals is proportional to the reciprocal of the time window length.

[0080] The amplitudes of the control signals in the magnetic field gradient control instruction group are determined according to the diffusion radius and the magnetic field response sensitivity of the focusing lens. The larger the diffusion radius, the greater the change in magnetic field strength required to adjust the focusing position. Therefore, the amplitude of the control signal is larger; the higher the magnetic field response sensitivity of the focusing lens, the greater the change in magnetic field strength under the same control signal. Therefore, the amplitude of the control signal can be relatively smaller. The alternating frequency of the control signals is proportional to the reciprocal of the time window length. The shorter the time window, the faster the magnetic field strength needs to be adjusted. Therefore, the alternating frequency of the control signals is higher; conversely, the longer the time window, the lower the alternating frequency of the control signals. For example, if the diffusion radius is r and the magnetic field response sensitivity of the focusing lens is s, then the amplitude ΔB of each control signal in the magnetic field gradient control instruction group can be expressed as ΔB = s*r*k1, where k1 is a constant. The length of the time window in the third adjustment stage is T3, then the alternating frequency f of the control signals can be expressed as f = k2 / T3, where k2 is a constant.

[0081] Step S327: Monitor the fluctuation amplitude of the ion beam current intensity after the execution of the instructions in each time window in real time. If the fluctuation amplitude exceeds a preset proportion of the peak energy deviation threshold, shorten the current time window and insert a transition buffer instruction. The duration of the transition buffer instruction is inversely proportional to the square root of the excess value of the fluctuation amplitude.

[0082] Real-time monitoring of the fluctuation amplitude of the ion beam current intensity is to ensure the stability of the adjustment process. If the fluctuation amplitude exceeds a preset ratio of the peak energy deviation threshold, it indicates that an abnormality may have occurred in the adjustment process, and measures need to be taken for adjustment. Shortening the current time window can speed up the adjustment, and inserting a transition buffer instruction can make the adjustment process smoother. The duration of the transition buffer instruction is inversely proportional to the square root of the fluctuation amplitude excess value. The larger the fluctuation amplitude excess value, the more serious the abnormal situation, and a shorter transition buffer time is required to restore stability. For example, if the peak energy deviation threshold is A, the preset ratio is α, and the ion beam current intensity fluctuation amplitude is ΔI, when ΔI > α*A, shorten the current time window and insert a transition buffer instruction. The duration of the transition buffer instruction Δt can be expressed as Δt = m / √(ΔI - α*A), where m is a constant.

[0083] Step S328: After the instructions in all time windows are executed, recalibrate the minimum energy gradient change rate based on the change amount of the diffusion radius of the adjusted spatial distribution characteristics. If the calibrated change rate exceeds the allowable error of the target energy adjustment range, trigger the time window reallocation process.

[0084] The change amount of the diffusion radius of the adjusted spatial distribution characteristics reflects the influence of the adjustment process on the focusing situation of the ion beam. Recalibrating the minimum energy gradient change rate based on this change amount can make the energy adjustment more accurately adapt to the actual situation of the ion beam. If the calibrated change rate exceeds the allowable error of the target energy adjustment range, it indicates that the current time window allocation may be unreasonable, and the time window reallocation process needs to be triggered to ensure that the adjustment process can reach the target energy adjustment range. For example, if the diffusion radius before adjustment is r1, the diffusion radius after adjustment is r2, and the change amount of the diffusion radius is Δr = |r2 - r1|, recalculate the minimum energy gradient change rate G' according to Δr. If |G' - G0| > ε, where G0 is the minimum energy gradient change rate in the target energy adjustment range and ε is the allowable error, trigger the time window reallocation process.

[0085] Step S330: During the execution of the multi-level energy adjustment instruction, real-time monitor the completion status of each adjustment stage. If it is detected that the adjustment error of a certain stage exceeds the stage allowable error threshold, pause the subsequent stage and start the error compensation sub-process.

[0086] Real-time monitoring of the completion status of each adjustment stage is to ensure the accuracy and stability of the adjustment process. The stage allowable error threshold is the allowable error range preset for each adjustment stage. If the adjustment error of a certain stage exceeds this threshold, it means that there may be a problem with the adjustment of this stage, and it is necessary to suspend the adjustment of the subsequent stage and start the error compensation sub-process to correct the error. For example, in the first adjustment stage, the stage allowable error threshold is set to β. If the adjustment error of the ion beam current intensity exceeds β, that is, |I-I0|>β, where I is the current ion beam current intensity and I0 is the target value of the preset reference intensity interval, the adjustment of the subsequent stage is suspended and the error compensation sub-process is started.

[0087] Step S400: Real-time detection of energy fluctuation data of the adjusted ion beam on the sample surface, the energy fluctuation data including instantaneous energy offset and focus point energy diffusion coefficient.

[0088] The instantaneous energy offset refers to the difference between the energy of the ion beam on the sample surface and the expected energy at a certain moment, which reflects the instantaneous stability of the ion beam energy. The focus energy diffusion coefficient is an indicator that measures the degree of diffusion of the ion beam energy around the focus point, which reflects the focusing accuracy of the ion beam. By real-time detection of these energy fluctuation data, the energy distribution of the ion beam on the sample surface can be understood in a timely manner, providing a basis for subsequent adaptive compensation.

[0089] The specific steps for detecting energy fluctuation data are as follows:

[0090] Step S410: Arrange an array energy sensor on the sample surface, wherein the array energy sensor is composed of a plurality of micro sensor units distributed in a grid shape, and each micro sensor unit covers a preset detection area.

[0091] An array energy sensor is a device used to detect ion beam energy. It consists of multiple micro sensor units that are distributed in a grid on the sample surface and can fully and accurately detect the energy distribution of the ion beam on the sample surface. Each micro sensor unit covers a preset detection area, which ensures energy detection at each position on the sample surface. For example, micro sensor units are arranged at intervals on the sample surface to form a two-dimensional grid structure, and each unit is responsible for detecting the ion beam energy in its area.

[0092] Step S420: synchronously collect the output signals of all micro sensor units to generate an energy distribution thermogram.

[0093] Synchronously collecting the output signals of all micro-sensor units can ensure that the acquired energy data is at the same moment, thus accurately reflecting the energy distribution of the ion beam on the sample surface. The energy distribution heat map is a graph that uses colors to represent the energy distribution, and it can visually display the high and low distribution of the ion beam energy on the sample surface. For example, by using the data acquisition system to simultaneously collect the output signals of all micro-sensor units, converting these signals into corresponding energy values, and then displaying them on the heat map with different colors according to the magnitude of the energy values, the areas with higher energy are shown in darker colors, and the areas with lower energy are shown in lighter colors.

[0094] Step S430: Perform edge detection on the energy distribution heat map to identify the position and shape characteristics of the energy anomaly regions.

[0095] Edge detection is an image processing technique that can identify the regions with large energy changes in the energy distribution heat map, that is, the energy anomaly regions. Through edge detection, the positions and shape characteristics of these anomaly regions can be determined, providing a basis for subsequent analysis. For example, using the Canny edge detection algorithm to process the energy distribution heat map, finding the edges of the energy changes, thereby determining the boundaries of the energy anomaly regions and further obtaining their position and shape characteristics.

[0096] Step S440: Extract the instantaneous energy offset from the energy anomaly regions, and the instantaneous energy offset is the difference between the maximum and minimum energy values within the current detection period.

[0097] In the energy anomaly regions, the energy fluctuations are relatively obvious. By extracting the difference between the maximum and minimum energy values within the current detection period in this region, the instantaneous energy offset can be obtained. This value reflects the instantaneous change of the ion beam energy in this region. For example, within a detection period, record the maximum energy value Emax and the minimum energy value Emin in the energy anomaly region, and the instantaneous energy offset ΔE = Emax - Emin.

[0098] Step S450: Calculate the variance value of the energy distribution heat map and normalize the variance value to the focal point energy diffusion coefficient.

[0099] The variance value of the energy distribution heat map reflects the degree of dispersion of the energy distribution on the sample surface. The larger the variance value, the more dispersed the energy distribution and the greater the energy diffusion degree of the focal point. Normalizing the variance value to the focal point energy diffusion coefficient can make this coefficient comparable. For example, first calculate the variance σ of the energy values of all pixel points in the energy distribution heat map 2 , and then convert the variance value into the focal point energy diffusion coefficient C through a normalization algorithm. For example, C = σ 2 / (Emax - Emin).

[0100] Step S460: Match the shape characteristics of the energy anomaly region with a preset defect pattern library. If a known energy diffusion pattern is matched, generate a corresponding diffusion type identifier and associate the diffusion type identifier with the energy fluctuation data.

[0101] The preset defect pattern library stores various known energy diffusion patterns and their corresponding shape characteristics. By matching the shape characteristics of the energy anomaly region with the defect pattern library, the current energy diffusion pattern can be identified. If the match is successful, generate a corresponding diffusion type identifier and associate it with the energy fluctuation data, so that the fluctuation of the ion beam energy can be analyzed more accurately. For example, the defect pattern library stores energy diffusion patterns of different shapes such as circular, elliptical, and rectangular and their corresponding identifiers. When the shape of the energy anomaly region matches a certain pattern, extract the identifier of the pattern and associate it with the energy fluctuation data.

[0102] Step S500: According to the energy fluctuation data and a preset adaptive compensation algorithm, adjust the feedback control parameters of the ion beam energy regulator so that the energy distribution of the ion beam is maintained within the target energy regulation range.

[0103] The preset adaptive compensation algorithm is a pre-designed algorithm that can automatically adjust the feedback control parameters of the ion beam energy regulator according to the energy fluctuation data to achieve adaptive regulation of the ion beam energy. The feedback control parameters include the ion source current adjustment step, the feedback period, the magnetic field response coefficient, etc. By adjusting these parameters, the energy distribution of the ion beam can be more stably maintained within the target energy regulation range.

[0104] The process of the adaptive compensation algorithm adjusting the feedback control parameters is as follows:

[0105] Step S510: Compare the instantaneous energy offset with the peak energy deviation threshold in the target energy regulation range through the adaptive compensation algorithm. When the absolute value of the offset exceeds the preset offset threshold, generate an error polarity identifier and an amplitude weight. The error polarity identifier indicates the energy offset direction, and the amplitude weight is the ratio of the absolute value of the offset to the offset threshold.

[0106] The preset offset threshold is a preset allowable offset range. When the absolute value of the instantaneous energy offset exceeds this threshold, it indicates that there is a large deviation in the ion beam energy and compensation is required. The error polarity flag is used to indicate the direction of the energy offset, such as whether it is a positive offset or a negative offset. The amplitude weight reflects the relative relationship between the magnitude of the offset and the offset threshold, and it will be used for subsequent parameter adjustment calculations. For example, if the instantaneous energy offset is ΔE, the peak energy deviation threshold in the target energy adjustment range is A, and the preset offset threshold is γ, when |ΔE|>γ, if ΔE>0, the error polarity flag is positive, and the amplitude weight w = |ΔE| / γ; if ΔE<0, the error polarity flag is negative, and the amplitude weight is also w = |ΔE| / γ.

[0107] Step S520: Determine the ion source current adjustment direction based on the error polarity flag. At the same time, calculate the current adjustment step size according to the amplitude weight. The current adjustment step size is proportional to the square root of the amplitude weight, and shorten the feedback control period based on the reciprocal of the amplitude weight, so that the current adjustment rate in the feedback control parameters is non-linearly related to the error intensity.

[0108] The error polarity flag indicates the direction of the energy offset. According to this flag, the adjustment direction of the ion source current can be determined. For example, when the energy has a positive offset, the ion source current needs to be decreased; when the energy has a negative offset, the ion source current needs to be increased. The current adjustment step size is calculated according to the square root of the amplitude weight. The larger the amplitude weight, the larger the offset, and a larger adjustment step size is required to quickly correct the error. Shorten the feedback control period based on the reciprocal of the amplitude weight, so that adjustments can be made more frequently when the error is large, thereby making the current adjustment rate non-linearly related to the error intensity and improving the adjustment efficiency. For example, if the error polarity flag is positive and the ion source current needs to be decreased, the current adjustment step size ΔI = k*√w, where k is a constant, and the feedback control period T = T0 / w, where T0 is the initial feedback control period.

[0109] Step S530: Input the focal point energy diffusion coefficient into the diffusion trend prediction model in the adaptive compensation algorithm, and output the diffusion out-of-control index. When the diffusion out-of-control index exceeds the minimum energy gradient change rate constraint of the target energy adjustment range, trigger the magnetic field gradient compensation mechanism.

[0110] The diffusion trend prediction model is a sub-model in the adaptive compensation algorithm. It can predict the trend of ion beam energy diffusion based on the energy diffusion coefficient of the focal point and output the diffusion out-of-control index. The diffusion out-of-control index reflects the severity of the ion beam energy diffusion. When it exceeds the minimum energy gradient change rate constraint of the target energy adjustment range, it indicates that the energy diffusion has exceeded the acceptable range and the magnetic field gradient compensation mechanism needs to be triggered for adjustment. For example, the diffusion trend prediction model can be a machine learning-based model. It establishes the relationship between the diffusion coefficient and the diffusion out-of-control index by learning the historical energy diffusion coefficient data of the focal point. When the current energy diffusion coefficient of the focal point is input, the corresponding diffusion out-of-control index is output.

[0111] Step S540: In the magnetic field gradient compensation mechanism, match the magnetic field waveform correction strategy based on the diffusion type identifier, generate a gradient control instruction including the magnetic field strength increment and the phase delay amount, and scale the amplitudes of the magnetic field strength increment and the phase delay amount by the magnetic field response coefficient in the feedback control parameters.

[0112] The magnetic field waveform correction strategy is a series of strategies for adjusting the magnetic field waveform formulated in advance according to different diffusion type identifiers. After matching the corresponding strategy based on the diffusion type identifier, a gradient control instruction including the magnetic field strength increment and the phase delay amount is generated. These instructions are used to adjust the magnetic field distribution of the focusing lens. By scaling the amplitudes of the magnetic field strength increment and the phase delay amount by the magnetic field response coefficient in the feedback control parameters, precise adjustment can be made according to the actual response of the system. For example, when the diffusion type identifier is of a certain type, the corresponding magnetic field waveform correction strategy is matched, and the magnetic field strength increment in the generated gradient control instruction is ΔB, and the phase delay amount is After scaling by the magnetic field response coefficient s, the actual magnetic field strength increment is s*ΔB, and the phase delay amount is

[0113] Step S550: Real-time collect the adjusted instantaneous energy offset and the energy diffusion coefficient of the focal point, and calculate the composite convergence index. The composite convergence index is composed of the product of the offset decrease rate and the diffusion coefficient fluctuation attenuation rate.

[0114] Collect the instantaneous energy offset and the energy diffusion coefficient at the focus point after adjustment in real time to evaluate the adjustment effect. The composite convergence index is a comprehensive index, which is composed of the product of the offset decline rate and the diffusion coefficient fluctuation attenuation rate, reflecting the convergence of the ion beam energy in terms of stability and focusing accuracy. The faster the offset decline rate and the faster the diffusion coefficient fluctuation attenuation rate, the better the adjustment effect and the larger the composite convergence index. For example, within a period of time, record the change of the instantaneous energy offset, and calculate the offset decline rate v1 = (ΔE1 - ΔE2) / Δt, where ΔE1 and ΔE2 are the instantaneous energy offsets at the beginning and end of the time period respectively, and Δt is the length of the time period. Similarly, calculate the fluctuation attenuation rate v2 = (C1 - C2) / Δt of the energy diffusion coefficient at the focus point, where C1 and C2 are the energy diffusion coefficients at the focus point at the beginning and end of the time period respectively. The composite convergence index C = v1 * v2.

[0115] Step S560: When the composite convergence index is lower than the preset index threshold, iteratively update the current adjustment step size and the magnetic field response coefficient. The step size shrinks according to the exponential decay rule, and the magnetic field response coefficient is linearly adjusted according to the decline rate of the diffusion out-of-control index.

[0116] The preset index threshold is a pre-set standard for judging whether the adjustment effect is good. When the composite convergence index is lower than this threshold, it means that the adjustment effect is not yet ideal and the feedback control parameters need to be further adjusted. The current adjustment step size shrinks according to the exponential decay rule. As the number of iterations increases, the step size gradually becomes smaller to improve the adjustment accuracy. The magnetic field response coefficient is linearly adjusted according to the decline rate of the diffusion out-of-control index. When the diffusion out-of-control index drops rapidly, the magnetic field response coefficient increases accordingly to speed up the magnetic field adjustment; when the diffusion out-of-control index drops slowly, the magnetic field response coefficient decreases accordingly to avoid over-adjustment. For example, assume the initial current adjustment step size is ΔI0, the number of iterations is n, and the exponential decay rule is ΔIn = ΔI0 * e (-λn) -λn, where λ is the attenuation coefficient. The decline rate of the diffusion out-of-control index is v3, and the adjustment formula for the magnetic field response coefficient is s = s0 + k * v3, where s0 is the initial magnetic field response coefficient and k is the adjustment coefficient.

[0117] Step S570: When the instantaneous energy offset is within the peak energy deviation threshold and the diffusion out-of-control index is lower than the minimum energy gradient change rate constraint in N consecutive iterations, freeze the current adjustment step size, feedback period, and magnetic field response coefficient in the feedback control parameters, and generate a locking signal, N > 1.

[0118] When the instantaneous energy offset within consecutive N iterations is within the peak energy deviation threshold and the diffusion runaway index is lower than the minimum energy gradient change rate constraint, it indicates that the energy distribution of the ion beam has been stabilized within the target energy regulation range. At this time, freeze the current adjustment step, feedback period, and magnetic field response coefficient in the feedback control parameters, stop further adjustment, and generate a locking signal to maintain the current stable state. For example, set N = 5. When the absolute value of the instantaneous energy offset is less than the peak energy deviation threshold in consecutive 5 iterations and the diffusion runaway index is less than the minimum energy gradient change rate constraint, fix the current adjustment step, feedback period, and magnetic field response coefficient, no longer make adjustments, and generate a locking signal.

[0119] For example, in the adaptive compensation algorithm, dynamic error perception and quantization can be achieved through sliding window difference calculation. Continuously sample the instantaneous energy offset using a fixed time window (such as 10 ms), calculate the mean and standard deviation of the offset within the window, and generate a dynamic error baseline. The real-time difference between the current offset and the baseline value is used to trigger the compensation threshold. Decompose the energy offset into direction polarity (positive / negative) and absolute amplitude, extract the direction using the Sign Function, and quantify the amplitude weight using the absolute value function. The error signal is processed through a sliding window to reduce noise interference, and the polarity-amplitude decomposition provides discrete control inputs for subsequent compensation. The feedback parameter non-linear mapping technology can adopt a piecewise proportional-integral (PI) regulator. Divide the adjustment interval according to the error amplitude weight. For example, for the low amplitude region (<30% threshold): adopt linear proportional adjustment, and the step size is proportional to the amplitude weight; for the high amplitude region (≥30% threshold): introduce an integral accumulation term, and the step size increases according to the square root of the amplitude to suppress overshoot. The feedback period dynamic compression can shorten the control period based on the reciprocal function of the error amplitude, where k is the compression coefficient and |E| is the normalized error amplitude. The non-linear PI strategy realizes rapid error convergence, and the dynamic period compression improves the response speed to high-frequency disturbances. The diffusion trend prediction model can be implemented as a time series autoregressive (AR) model. Establish a first-order autoregressive equation for the sequence of the focal point energy diffusion coefficient: D t = αD t-1 + βΔP + ∈, where D tis the current diffusion coefficient, ΔP is the change in ambient air pressure, α and β are fitting parameters, and ∈ is the residual. Residual trend detection is used to calculate the moving average of consecutive prediction residuals. If the residual mean exceeds 3 times the historical standard deviation, diffusion runaway is determined. The AR model captures the time dependence of the diffusion coefficient, and residual analysis enables real-time alarm for abnormal trends. The magnetic field gradient compensation waveform synthesis technology can adopt phase-synchronized pulse modulation to generate a sine modulation waveform according to the diffusion runaway direction. The waveform frequency is synchronized with the ion beam pulse frequency, and the phase difference is dynamically adjusted according to the change rate of the diffusion coefficient. Based on the magnetic field strength envelope control, trapezoidal wave envelope modulation is adopted. The rising edge slope is determined by the diffusion runaway index, and the peak intensity has an exponential relationship with the error amplitude (B peak = B0e λ|E| ), where λ is the attenuation factor. Among them, phase synchronization avoids magnetic field-pulse interference, and envelope modulation realizes the non-linear matching of intensity and the degree of diffusion deterioration.

[0120] Step S600: After adjusting the feedback control parameters, start the energy distribution sampling period for a preset number of times. In each sampling period, continuously collect multiple groups of energy fluctuation data.

[0121] The energy distribution sampling period for a preset number of times is to further verify the stability of ion beam energy regulation. By continuously collecting multiple groups of energy fluctuation data in each sampling period, a more comprehensive understanding of the energy distribution of the ion beam can be obtained through the analysis of these data. For example, if the preset number is set to 10 and each sampling period is 10 seconds, and one group of energy fluctuation data is collected per second in each sampling period, then a total of 100 groups of energy fluctuation data will be collected in 10 sampling periods.

[0122] Step S700: Calculate the standard deviation of each group of energy fluctuation data, and use the median of all standard deviations as the current energy stability index.

[0123] The standard deviation is a statistic that measures the degree of data dispersion. Calculating the standard deviation of each group of energy fluctuation data can reflect the fluctuation of that group of data. Using the median of all standard deviations as the current energy stability index, the median can avoid the influence of individual abnormal data and more accurately reflect the overall stability of the ion beam energy. For example, for the 100 groups of energy fluctuation data collected, calculate the standard deviation of each group of data respectively, then sort these 100 standard deviations from small to large, and take the value at the middle position as the current energy stability index.

[0124] Step S800: If the current energy stability index is lower than the preset stability threshold, it is determined that the ion beam energy regulation is completed, and the current parameter configuration of the ion beam energy regulator is locked.

[0125] The preset stability threshold is a pre-set criterion for judging the stability of the ion beam energy. When the current energy stability index is lower than this threshold, it indicates that the ion beam energy has reached a stable state, and it is determined that the ion beam energy adjustment is completed. Lock the current parameter configuration of the ion beam energy regulator to ensure that the ion beam energy can remain stable in subsequent operations. For example, the preset stability threshold is δ. When the current energy stability index is less than δ, it is considered that the ion beam energy adjustment is completed, and the current parameter configuration of the ion beam energy regulator, such as the ion source current step size, magnetic field response coefficient, pulse synchronization delay amount, etc., is locked.

[0126] Step S801: Record the instantaneous value of the vacuum chamber pressure, the stable value of the ion source temperature, and the cooling system circulation rate at the locking moment in real time, generate an environmental parameter snapshot, and bind the environmental parameter snapshot with the ion source current step size, magnetic field response coefficient, and pulse synchronization delay amount in the current parameter configuration and store them in the historical adjustment database.

[0127] Environmental parameters such as the vacuum chamber pressure, ion source temperature, and cooling system circulation rate will affect the ion beam energy adjustment. Record these environmental parameters at the locking moment in real time, generate an environmental parameter snapshot, and bind it with the current parameter configuration and store it in the historical adjustment database, so as to refer to these historical data in subsequent adjustments to improve the efficiency and accuracy of the adjustment. For example, at the locking moment, record the vacuum chamber pressure as P, the ion source temperature as T, and the cooling system circulation rate as v, combine these parameters into an environmental parameter snapshot (P, T, v), and store them in the historical adjustment database together with the current ion source current step size ΔI, magnetic field response coefficient s, pulse synchronization delay amount t, etc.

[0128] Step S802: According to the energy distribution comparison chart before and after the adjustment in the energy adjustment report, extract the peak displacement amount of the energy density and the edge energy decay rate, generate an adjustment efficiency fingerprint, and the adjustment efficiency fingerprint includes the peak displacement direction identifier and the decay rate change ratio.

[0129] The energy distribution comparison chart before and after the adjustment in the energy adjustment report can intuitively show the change of the ion beam energy before and after the adjustment. By extracting the peak displacement amount of the energy density and the edge energy decay rate, an adjustment efficiency fingerprint is generated, which can reflect the effect of this energy adjustment. The peak displacement direction identifier indicates the moving direction of the energy density peak before and after the adjustment, and the decay rate change ratio reflects the change of the edge energy decay rate before and after the adjustment. For example, before the adjustment, the position of the energy density peak is (x1, y1, z1), and after the adjustment, it is (x2, y2, z2), and the peak displacement amount of the energy density is √((x2 - x1) 2 +(y2 - y1) 2 +(z2 - z1) 2) The peak displacement direction identifier can be determined by calculating the direction of the displacement vector. The energy attenuation rate of the front edge before adjustment is r1, and after adjustment is r2. The change ratio of the attenuation rate is r2 / r1. The peak displacement direction identifier and the change ratio of the attenuation rate are combined into an adjustment efficiency fingerprint.

[0130] Step S803: Store the adjustment efficiency fingerprint in association with the environmental parameter snapshot, and add an environmental matching degree label to each record in the historical adjustment database. The label is generated by weighted calculation of the air pressure deviation degree in the vacuum chamber, the temperature fluctuation range of the ion source, and the cooling rate consistency coefficient.

[0131] Storing the adjustment efficiency fingerprint in association with the environmental parameter snapshot can establish the connection between the environmental parameters and the adjustment effect. Adding an environmental matching degree label to each record in the historical adjustment database, which is generated by weighted calculation of the air pressure deviation degree in the vacuum chamber, the temperature fluctuation range of the ion source, and the cooling rate consistency coefficient, can reflect the similarity of the adjustment effect under different environmental conditions. For example, for each record in the historical adjustment database, calculate the deviation degree between its vacuum chamber air pressure and the current air pressure, the temperature fluctuation range during the adjustment of the ion source, and the consistency coefficient between the cooling rate and the current rate. Assign different weights to these parameters and obtain the environmental matching degree label through weighted calculation.

[0132] Step S804: When the initial environmental parameters of a new ion beam processing task are detected, calculate the similarity between its environmental matching degree label and those of each record in the historical adjustment database. If there is a record with a similarity exceeding the preset matching threshold, directly load the ion source current step size, magnetic field response coefficient, and pulse synchronization delay amount in this record as the initial parameter configuration.

[0133] When there is a new ion beam processing task, detect its initial environmental parameters. By calculating the similarity between its environmental matching degree label and those of each record in the historical adjustment database, historical records similar to the current environmental conditions can be found. If there is a record with a similarity exceeding the preset matching threshold, it means that there has been a successful adjustment experience under similar environmental conditions. Directly loading the ion source current step size, magnetic field response coefficient, and pulse synchronization delay amount in this record as the initial parameter configuration can save the adjustment time and improve the adjustment efficiency. For example, use methods such as cosine similarity to calculate the similarity between the initial environmental parameters of the new task and the environmental matching degree labels of the historical records. The preset matching threshold is θ. When the similarity is greater than θ, load the parameter configuration of this record.

[0134] After loading the historical parameter configuration, skip the steps of generating multi-level energy adjustment instructions, and directly enter the step of real-time detecting the energy fluctuation data of the adjusted ion beam on the sample surface, and synchronously adjust the signal acquisition timing of the array energy sensor according to the loaded pulse synchronization delay amount.

[0135] After loading the historical parameter configuration, since these parameters have been verified in a similar environment, the steps of generating multi-level energy adjustment instructions can be skipped, and the process can directly enter the step of real-time detecting energy fluctuation data to speed up the adjustment process. Synchronously adjust the signal acquisition timing of the array energy sensor according to the loaded pulse synchronization delay amount to ensure that the acquired energy data is synchronized with the pulse signal of the ion beam and improve the accuracy of the data. For example, if the loaded pulse synchronization delay amount is t, delay the signal acquisition timing of the array energy sensor by t time to synchronize it with the pulse signal of the ion beam.

[0136] Step S900: If the current energy stability index is higher than the stability threshold, re-execute the adaptive compensation algorithm and increase the compensation intensity of the magnetic field gradient compensation mechanism in each iteration until the maximum number of iterations is reached or the current energy stability index meets the standard.

[0137] If the current energy stability index is higher than the stability threshold, it indicates that the energy of the ion beam is not yet stable enough, and the above-mentioned adaptive compensation algorithm needs to be re-executed for adjustment. Increasing the compensation intensity of the magnetic field gradient compensation mechanism in each iteration can speed up the adjustment speed and make the energy of the ion beam reach a stable state faster. Stop the iteration when the maximum number of iterations is reached or the current energy stability index meets the standard. For example, set the maximum number of iterations to M, and increase the compensation intensity of the magnetic field gradient compensation mechanism by a fixed ratio in each iteration. Stop the iteration when the number of iterations reaches M or the current energy stability index is less than the stability threshold.

[0138] Step S1000: Continuously collect the voltage fluctuation curve of the ion source high-voltage power supply and the vacuum chamber leakage rate monitoring signal during the ion beam energy adjustment process. The voltage fluctuation curve includes the instantaneous voltage drop depth and the recovery time, and the leakage rate monitoring signal includes the air pressure rise rate per unit time.

[0139] During the ion beam energy adjustment process, the voltage fluctuation of the ion source high-voltage power supply and the leakage of the vacuum chamber will affect the energy of the ion beam. Continuously collecting the voltage fluctuation curve and the leakage rate monitoring signal can timely detect these abnormal situations and provide a basis for subsequent processing. The instantaneous voltage drop depth and the recovery time reflect the severity and recovery of the voltage fluctuation, and the air pressure rise rate per unit time reflects the leakage degree of the vacuum chamber. For example, use a voltage sensor to collect the voltage signal of the ion source high-voltage power supply in real time, draw the voltage fluctuation curve, and record the instantaneous voltage drop depth and the recovery time. Use a pressure sensor to monitor the air pressure change in the vacuum chamber and calculate the air pressure rise rate per unit time.

[0140] Step S1100: When it is detected that the depth of the instantaneous voltage dip exceeds the preset voltage tolerance, immediately send a beam current truncation instruction to the ion emitter and activate the overvoltage protection circuit of the high-voltage power supply. The execution delay time of the truncation instruction is inversely proportional to the absolute value of the voltage dip depth.

[0141] The preset voltage tolerance is a preset allowable voltage dip range. When the depth of the instantaneous voltage dip exceeds this tolerance, it indicates that the voltage fluctuation has seriously affected the normal emission of the ion beam, and immediate measures need to be taken. Sending a beam current truncation instruction to the ion emitter can stop the emission of the ion beam and avoid damage to the equipment. Activating the overvoltage protection circuit of the high-voltage power supply can protect the safety of the power supply equipment. The execution delay time of the truncation instruction is inversely proportional to the absolute value of the voltage dip depth. The greater the voltage dip depth, the more urgent the situation is, and the faster the truncation instruction needs to be executed. For example, the preset voltage tolerance is U0. When the depth of the instantaneous voltage dip |ΔU|>U0, send a beam current truncation instruction to the ion emitter. The execution delay time t of the truncation instruction is t = k / |ΔU|, where k is a constant, and at the same time, activate the overvoltage protection circuit of the high-voltage power supply.

[0142] Step S1200: When it is detected that the air pressure rising rate exceeds the safety threshold, close the electromagnetic valves of all ion transmission channels and start the standby vacuum pump group to operate at the preset maximum power until the leakage rate monitoring signal drops back within the safe range.

[0143] The safety threshold is a preset allowable air pressure rising rate range. When it is detected that the air pressure rising rate exceeds this threshold, it indicates that there is a leakage problem in the vacuum chamber and it needs to be dealt with in time. Closing the electromagnetic valves of all ion transmission channels can prevent the ion beam from being affected by the leaked gas. Starting the standby vacuum pump group to operate at the preset maximum power can accelerate the pumping speed and reduce the air pressure in the vacuum chamber until the leakage rate monitoring signal drops back within the safe range. For example, the preset safety threshold is v0. When the air pressure rising rate v>v0, close the electromagnetic valves of all ion transmission channels, start the standby vacuum pump group to operate at the maximum power P, and monitor the leakage rate monitoring signal in real time. When the air pressure rising rate v<v0, stop the operation of the standby vacuum pump group.

[0144] Step S1300: After completing the voltage protection or leakage control operation, re-initialize the parameter configuration of the ion beam energy regulator. The parameter configuration includes the progress identifier of the adjustment instruction sequence being executed at the interruption moment, the remaining duration of the uncompleted feedback control cycle, and the waveform break point position of the magnetic field gradient control instruction.

[0145] After completing the voltage protection or leakage control operation, since the adjustment process is interrupted, it is necessary to re-initialize the parameter configuration of the ion beam energy regulator to ensure that the adjustment process can continue. The progress identifier records the execution progress of the adjustment instruction sequence being executed at the interruption moment, the remaining duration of the unfinished feedback control cycle records the remaining time of the feedback control cycle, and the waveform break point position of the magnetic field gradient control instruction records the waveform position of the magnetic field gradient control instruction at the interruption moment. For example, at the interruption moment, the adjustment instruction sequence has been executed to the nth step, and the progress identifier is n; there is still t1 time left unfinished in the feedback control cycle; the waveform of the magnetic field gradient control instruction is interrupted at the mth data point, and the waveform break point position is m.

[0146] Step S1400: Recalibrate the reference sampling moment of the array energy sensor according to the progress identifier and the waveform break point position. The calibration offset of the reference sampling moment is determined by the difference between the interruption duration and the ion beam current recovery stabilization time.

[0147] Recalibrating the reference sampling moment of the array energy sensor according to the progress identifier and the waveform break point position is to ensure that the collected energy data is synchronized with the actual state of the ion beam. The calibration offset of the reference sampling moment is determined by the difference between the interruption duration and the ion beam current recovery stabilization time, so that accurate energy data can be collected after the ion beam current recovers and stabilizes. For example, the interruption duration is t2, the ion beam current recovery stabilization time is t3, and the calibration offset Δt = t2 - t3. The reference sampling moment of the array energy sensor is shifted backward by Δt time.

[0148] Step S1500: Based on the calibrated sampling moment, continue to execute the multi-level energy adjustment instruction before the interruption, and temporarily adjust the ion source current step size to a preset ratio of the original step size within the first complete feedback control cycle to accelerate system stabilization.

[0149] Continuing to execute the multi-level energy adjustment instruction before the interruption based on the calibrated sampling moment enables the adjustment process to continue. Temporarily adjusting the ion source current step size to a preset ratio of the original step size within the first complete feedback control cycle can speed up the adjustment speed of the ion beam energy and enable the system to reach a stable state faster. For example, the original ion source current step size is ΔI0, and the preset ratio is α. The ion source current step size is adjusted to α * ΔI0 within the first complete feedback control cycle.

[0150] Step S1600: Trigger a full energy range scan instruction at a preset calibration time point, and control the ion beam energy regulator to output a continuous energy beam current from the minimum allowable energy to the maximum allowable energy in a linearly increasing mode.

[0151] The preset calibration time point is a pre-set time for energy calibration. At this time point, a full energy range scan instruction is triggered to control the ion beam energy regulator to output a continuous energy beam current from the minimum allowable energy to the maximum allowable energy in a linearly increasing mode. This can comprehensively detect and calibrate the performance of the ion beam over the entire energy range. For example, if the preset calibration time point is 2:00 am every day, after triggering the full energy range scan instruction, the ion beam energy regulator starts from the minimum allowable energy Emin and gradually increases the energy output in a linearly increasing manner until it reaches the maximum allowable energy Emax.

[0152] Step S1700: Synchronously collect the actual energy distribution parameters at different energy levels through an array-type energy sensor. The actual energy distribution parameters include the mean beam current intensity, spatial distribution symmetry, and focal point drift amount corresponding to each energy level.

[0153] Synchronously collecting the actual energy distribution parameters at different energy levels through an array-type energy sensor can accurately understand the performance of the ion beam at different energy levels. The mean beam current intensity reflects the average energy-carrying capacity of the ion beam at this energy level, the spatial distribution symmetry reflects the symmetry of the ion beam energy distribution in space, and the focal point drift amount reflects the change in the focal point position of the ion beam at different energy levels. For example, during the process of gradually increasing the ion beam energy from Emin to Emax, at every preset energy interval, the beam current intensity data at this energy level is collected through an array-type energy sensor, and its mean value is calculated; the symmetry of the energy distribution is analyzed to obtain the spatial distribution symmetry; the change in the focal point position is recorded to obtain the focal point drift amount.

[0154] Step S1800: Compare each item of the actual energy distribution parameters with the expected parameters in the theoretical distribution model to generate an energy calibration offset set. The offset set includes a beam current intensity deviation vector, a symmetry error scalar, and a focal point drift direction identifier.

[0155] The theoretical distribution model is a pre-established model that contains the expected parameters of the ion beam at different energy levels. By comparing the actual energy distribution parameters item by item with the expected parameters in the theoretical distribution model, the differences between them are found, and a set of energy calibration offset values is generated. The beam intensity deviation vector reflects the deviation between the mean beam intensity and the expected value, the symmetry error scalar reflects the error between the spatial distribution symmetry and the expected value, and the focus drift direction identifier indicates the direction of focus drift. For example, for a certain energy level E, the expected mean beam intensity in the theoretical distribution model is I0, the actually measured mean beam intensity is I, and the beam intensity deviation vector is I - I0; the expected spatial distribution symmetry in the theoretical distribution model is S0, the actually obtained spatial distribution symmetry is S, and the symmetry error scalar is S - S0; the direction of focus drift is recorded to obtain the focus drift direction identifier.

[0156] Step S1900: Modify the reference beam intensity mapping table in the ion beam energy reference model according to the beam intensity deviation vector, and the modification amount is the integral average value of the deviation vector in each energy interval.

[0157] The beam intensity deviation vector reflects the difference between the actual beam intensity and the expected value. By modifying the reference beam intensity mapping table in the ion beam energy reference model according to this deviation vector, the reference model can more accurately reflect the actual performance of the ion beam. The modification amount is the integral average value of the deviation vector in each energy interval, so that the deviation conditions at different energy levels can be comprehensively considered. For example, integrate the beam intensity deviation vector in the energy interval from Emin to Emax, and then divide by the length of the energy interval to obtain the integral average value ΔI. Add ΔI to the reference beam intensity corresponding to each energy level in the reference beam intensity mapping table to complete the modification.

[0158] Step S2000: Adjust the feedback gain coefficient of the adaptive compensation algorithm based on the symmetry error scalar. The adjustment direction is opposite to the sign of the error scalar, and the adjustment amplitude is non-linearly positively correlated with the absolute value of the error scalar.

[0159] The symmetry error scalar reflects the error between the spatial distribution symmetry and the expected value. Based on this error scalar, adjusting the feedback gain coefficient of the adaptive compensation algorithm can improve the effect of adaptive compensation. The adjustment direction is opposite to the sign of the error scalar. When the symmetry error is positive, it indicates that the actual symmetry is greater than the expected value, and the feedback gain coefficient needs to be decreased; when the symmetry error is negative, it indicates that the actual symmetry is smaller than the expected value, and the feedback gain coefficient needs to be increased. The adjustment amplitude is non-linearly positively correlated with the absolute value of the error scalar. The larger the error, the larger the adjustment amplitude. For example, if the symmetry error scalar is ΔS and the initial feedback gain coefficient of the adaptive compensation algorithm is K0, the adjusted feedback gain coefficient K = K0 - k * f(|ΔS|), where k is a constant and f(|ΔS|) is a function that is non-linearly positively correlated with |ΔS|.

[0160] Step S2100: When the focus drift direction identifier points to the same quadrant continuously for X times (X ≥ 3), trigger the hardware self-check process of the ion optical system, check the mechanical positioning accuracy of the focusing lens and the impedance value of the electromagnetic coil, and update the waveform amplitude compensation coefficient of the magnetic field gradient control instruction according to the change in the impedance value.

[0161] The focus drift direction identifier is used to indicate the direction of the ion beam focus drift in space, usually represented by quadrants. In a three-dimensional Cartesian coordinate system, the four quadrants represent different direction regions. When the focus drift direction identifier points to the same quadrant continuously for X times, this phenomenon is not accidental and may indicate a problem with the hardware of the ion optical system. Because under normal circumstances, the focus drift should be random or fluctuate within a small range, and continuously pointing to the same quadrant for X times indicates a systematic deviation.

[0162] After triggering the hardware self - check process of the ion optical system, first, it is necessary to check the mechanical positioning accuracy of the focusing lens. The focusing lens plays a crucial role in the ion optical system. It focuses the ion beam by generating a set magnetic field, enabling the ion beam to accurately reach the target position. Deviations in mechanical positioning accuracy may cause changes in the magnetic field distribution, thereby affecting the focusing effect of the ion beam. To check the mechanical positioning accuracy of the focusing lens, high - precision laser measurement equipment can be used to evaluate its positioning accuracy by measuring the deviation between the actual position and the theoretical position of the focusing lens. For example, a laser interferometer is used to measure the position deviations of the focusing lens in the three coordinate axes. When the deviation exceeds the preset accuracy threshold, it indicates that there may be a problem with the mechanical positioning of the focusing lens and adjustments or repairs are required. At the same time, check the impedance value of the electromagnetic coil. The electromagnetic coil is a key component for generating the magnetic field, and changes in its impedance value will directly affect the strength and distribution of the magnetic field. As the usage time increases, the impedance value of the electromagnetic coil may change due to temperature changes, aging, etc. A professional impedance measurement instrument, such as a multimeter, can be used to check the impedance value of the electromagnetic coil. Compare the actual measured impedance value with the initial design value and calculate the change in the impedance value. For example, if the initial designed impedance value of the electromagnetic coil is R0 and the actual measured impedance value is R1, then the change in impedance value ΔR = |R1 - R0|.

[0163] Update the waveform amplitude compensation coefficient of the magnetic field gradient control command according to the change in impedance value. The magnetic field gradient control command is used to adjust the magnetic field distribution of the focusing lens to achieve the focusing and energy adjustment of the ion beam. Changes in the impedance value will cause changes in the magnetic field response characteristics, so it is necessary to compensate the waveform amplitude of the magnetic field gradient control command. The update of the waveform amplitude compensation coefficient can be carried out in a linear or non - linear manner, depending on the relationship between the change in impedance value and the magnetic field response characteristics. For example, if the change in impedance value has a linear relationship with the change in magnetic field strength, a simple linear formula can be used to update the waveform amplitude compensation coefficient: K' = K+α*ΔR, where K is the initial waveform amplitude compensation coefficient, K' is the updated coefficient, and α is a proportional constant related to the system characteristics.

[0164] After completing the hardware self - check and updating the waveform amplitude compensation coefficient, it is necessary to recalibrate and test the ion beam energy adjustment system. By sending a series of test signals, observe the focusing effect and energy distribution of the ion beam to verify whether the hardware problems have been solved and whether the update of the waveform amplitude compensation coefficient is effective. If the test results are still not satisfactory, it may be necessary to further check and adjust the hardware components or re - optimize the update algorithm of the waveform amplitude compensation coefficient.

[0165] Figure 2 A schematic diagram of the hardware entity of a computer system provided by an embodiment of the present invention is shown in Figure 2As shown, the hardware entities of the computer system 1000 include: a processor 1001 and a memory 1002. Among them, the memory 1002 stores a computer program that can run on the processor 1001, and when the processor 1001 executes the program, it implements the steps in the method of any of the above embodiments.

[0166] As described above, only the embodiments of the present invention are provided, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention.

Claims

1. An ion beam energy adaptive regulation method for a FIB device, characterized in that, Including the following steps: Obtain the energy distribution parameters of the ion beam in the focusing area, where the energy distribution parameters include the ion beam current intensity, the energy attenuation gradient, and the spatial distribution characteristics; Determine the target energy adjustment range according to the energy distribution parameters and a preset ion beam energy reference model, where the target energy adjustment range includes the allowable peak energy deviation threshold and the minimum energy gradient change rate; Generate multi-level energy adjustment commands based on the target energy adjustment range, and control the output parameters of the ion beam energy regulator through the multi-level energy adjustment commands, so that the current energy distribution of the ion beam enters the target energy adjustment range; Real-time detect the energy fluctuation data of the adjusted ion beam on the sample surface, where the energy fluctuation data includes the instantaneous energy offset and the focal point energy diffusion coefficient; Adjust the feedback control parameters of the ion beam energy regulator according to the energy fluctuation data and a preset adaptive compensation algorithm, so that the energy distribution of the ion beam is maintained within the target energy adjustment range.

2. The method according to claim 1, characterized in that The obtaining of the energy distribution parameters of the ion beam in the focusing area includes the following steps: Synchronously collect the original energy signals of the ion beam current at different angles in the focusing area through a multi-axis energy detection array arranged in a ring. Each detection unit of the multi-axis energy detection array corresponds to a preset radial detection angle, and there is partial overlap in the signal acquisition areas of adjacent detection units; Perform time-domain denoising processing on the original energy signals to generate a denoised energy waveform cluster after eliminating high-frequency electromagnetic interference components. The denoised energy waveform cluster contains the waveform data and spatial position labels corresponding to each detection unit; Select the reference waveform with the largest energy integral value among all detection units; Intercept the energy maintenance interval in the steady state stage of the reference waveform, and calculate the average value of the waveform peaks in the energy maintenance interval as the ion beam current intensity; Divide multiple equal-time-interval sampling windows in the attenuation stage of the reference waveform; Calculate the decline rate of the energy peak in each sampling window, and obtain the weighted average value of the decline rates of all sampling windows as the energy attenuation gradient; Perform spatial interpolation calculation on the spatial position labels of each detection unit and the corresponding waveform energy integral values to generate a three-dimensional energy density surface covering the focusing area; Identify the spatial coordinates of the energy density extreme points in the three-dimensional energy density surface, and perform density gradient scanning from the extreme points to the surrounding areas to determine the boundary surface range where the energy density drops to a preset critical value; Define the spatial coordinates of the extreme points as the central position of the spatial distribution characteristics, and define the maximum radial extension distance of the boundary surface range as the diffusion radius of the spatial distribution characteristics; Combine the ion beam current intensity, the energy attenuation gradient, and the spatial distribution characteristics including the central position and the diffusion radius as the energy distribution parameters.

3. The method according to claim 2, wherein The determining of the target energy adjustment range according to the energy distribution parameters and a preset ion beam energy reference model includes the following steps: Compare the intensity of the ion beam current with the reference beam intensity recorded in the ion beam energy reference model to generate a current intensity deviation rate, and select an initial peak energy deviation threshold from a preset dynamic adjustment rule table according to the current intensity deviation rate; Based on the energy attenuation gradient and the gradient history curve stored in the ion beam energy reference model, calculate the gradient deviation degree, which is the maximum difference between the current gradient and the historical gradient curve within the same time window; Non-linearly correct the initial peak energy deviation threshold according to the gradient deviation degree. If the gradient deviation degree exceeds the preset gradient stability interval, reduce the initial peak energy deviation threshold in proportion to the square of the gradient deviation degree to generate a corrected peak energy deviation threshold; Extract the diffusion radius in the spatial distribution feature, input it into a pre-configured focusing accuracy compensator, output an accuracy compensation coefficient that is negatively correlated with the diffusion radius, and scale the preset minimum energy gradient change rate in the ion beam energy reference model according to the accuracy compensation coefficient; Combine the corrected peak energy deviation threshold and the scaled minimum energy gradient change rate into the target energy adjustment range, where the peak energy deviation threshold is used to constrain the amplitude change range of the ion source current adjustment instruction, and the minimum energy gradient change rate is used to define the step accuracy threshold for adjusting the magnetic field of the focusing lens.

4. The method according to claim 3, wherein Generating multi-level energy adjustment instructions based on the target energy adjustment range includes: Decompose the target energy adjustment range into multiple energy adjustment stages, each energy adjustment stage corresponding to a different energy adjustment priority, where: in the first adjustment stage, adjust the ion beam current intensity to a preset reference intensity range first; in the second adjustment stage, adjust the ion beam pulse width according to the energy attenuation gradient so that the energy attenuation rate matches the minimum energy gradient change rate; in the third adjustment stage, based on the diffusion radius of the spatial energy distribution matrix, adjust the magnetic field intensity of the focusing lens so that the position coordinates of the maximum energy density region coincide with the preset focusing center point; Allocate corresponding time windows for each energy adjustment stage, and generate corresponding adjustment instruction sequences within each time window. The adjustment instruction sequences include: Ion source current adjustment instructions for controlling the output current of the ion emitter; Pulse timing adjustment instructions for modifying the rise time and duration of the ion beam pulse; Magnetic field gradient control instructions for changing the excitation current of the focusing lens to adjust the magnetic field distribution pattern; During the execution of the multi-level energy adjustment instructions, real-time monitor the completion status of each adjustment stage. If it is detected that the adjustment error of a certain stage exceeds the stage allowable error threshold, pause the subsequent stages and start the error compensation sub-process.

5. The method according to claim 4, wherein Allocating corresponding time windows for each energy adjustment stage and generating corresponding adjustment instruction sequences within each time window includes: Determine the priority order of each energy adjustment stage according to the adjustment urgency weights of the peak energy deviation threshold and the minimum energy gradient change rate in the target energy adjustment range. The adjustment urgency weights are calculated by multiplying the influence coefficient of the peak energy deviation threshold on the system stability and the sensitivity coefficient of the minimum energy gradient change rate to the focusing accuracy. Allocate an initial time window for the first adjustment stage based on the priority order. The duration of the initial time window is inversely proportional to the difference between the peak energy deviation threshold and the current ion beam intensity, and allocate a dynamic time window for the second adjustment stage. The start time of the dynamic time window is determined by the sum of the end time of the initial time window and a preset buffer interval. Calculate the time window length of the third adjustment stage according to the diffusion radius of the spatial distribution characteristics and the maximum adjustment rate of the focusing lens. The time window length is proportional to the square root of the diffusion radius and starts immediately after the end time of the dynamic time window. Generate an ion source current adjustment instruction sequence within the initial time window of the first adjustment stage. The step amplitude of the ion source current adjustment instruction sequence is dynamically adjusted by the difference between the peak energy deviation threshold and the current ion beam intensity, and the interval time between adjacent instructions is inversely proportional to the change rate of the difference. Generate a pulse timing adjustment instruction sequence within the dynamic time window of the second adjustment stage. The trigger interval of the pulse timing adjustment instruction in it is determined by the ratio of the energy decay gradient to the minimum energy gradient change rate, and the duration of each pulse timing adjustment instruction is proportional to the length of the dynamic time window. Generate a magnetic field gradient control instruction group within the time window of the third adjustment stage. The amplitude of each control signal in the magnetic field gradient control instruction group is jointly determined by the diffusion radius and the magnetic field response sensitivity of the focusing lens, and the alternating frequency of the control signal is proportional to the reciprocal of the time window length. Real-time monitor the fluctuation amplitude of the ion beam intensity after the execution of the instructions within each time window. If the fluctuation amplitude exceeds a preset ratio of the peak energy deviation threshold, shorten the current time window and insert a transition buffer instruction. The duration of the transition buffer instruction is inversely proportional to the square root of the excess value of the fluctuation amplitude. After the execution of the instructions in all time windows is completed, recalibrate the minimum energy gradient change rate based on the change amount of the diffusion radius of the adjusted spatial distribution characteristics. If the calibrated change rate exceeds the allowable error of the target energy adjustment range, trigger the time window reallocation process.

6. The method according to claim 4, wherein The real-time detection of the energy fluctuation data of the adjusted ion beam on the sample surface includes: Arrange an array of energy sensors on the sample surface. The array of energy sensors consists of multiple micro-sensor units distributed in a grid pattern, and each micro-sensor unit covers a preset detection area. Synchronously collect the output signals of all micro-sensor units to generate an energy distribution heat map. Perform edge detection on the energy distribution heat map to identify the position and shape characteristics of the energy anomaly area. Extract the instantaneous energy offset from the energy anomaly region, where the instantaneous energy offset is the difference between the maximum and minimum energies within the current detection period; Calculate the variance value of the energy distribution heat map and normalize the variance value to the focal point energy diffusion coefficient; Match the shape characteristics of the energy anomaly region with a preset defect pattern library. If a known energy diffusion pattern is matched, generate a corresponding diffusion type identifier and associate the diffusion type identifier with the energy fluctuation data.

7. The method according to claim 6, wherein Adjust the feedback control parameters of the ion beam energy regulator according to the energy fluctuation data and a preset adaptive compensation algorithm, including: Compare the instantaneous energy offset with the peak energy deviation threshold in the target energy adjustment range through the adaptive compensation algorithm. When the absolute value of the offset exceeds the preset offset threshold, generate an error polarity flag and an amplitude weight. The error polarity flag indicates the energy offset direction, and the amplitude weight is the ratio of the absolute value of the offset to the offset threshold; Determine the ion source current adjustment direction based on the error polarity flag. At the same time, calculate the current adjustment step size according to the amplitude weight. The current adjustment step size is proportional to the square root of the amplitude weight, and shorten the feedback control period based on the reciprocal of the amplitude weight, so that the current adjustment rate in the feedback control parameters is non-linearly related to the error intensity; Input the focal point energy diffusion coefficient into the diffusion trend prediction model in the adaptive compensation algorithm, and output a diffusion out-of-control index. When the diffusion out-of-control index exceeds the minimum energy gradient change rate constraint of the target energy adjustment range, trigger the magnetic field gradient compensation mechanism; In the magnetic field gradient compensation mechanism, match the magnetic field waveform correction strategy based on the diffusion type identifier, generate a gradient control instruction including the magnetic field strength increment and the phase delay amount, and scale the amplitudes of the magnetic field strength increment and the phase delay amount through the magnetic field response coefficient in the feedback control parameters; Real-time collect the adjusted instantaneous energy offset and the focal point energy diffusion coefficient, and calculate a composite convergence index, which is composed of the product of the offset decrease rate and the diffusion coefficient fluctuation attenuation rate; When the composite convergence index is lower than the preset index threshold, iteratively update the current adjustment step size and the magnetic field response coefficient. The step size shrinks according to the exponential decay rule, and the magnetic field response coefficient is linearly adjusted according to the decrease rate of the diffusion out-of-control index; When the instantaneous energy offset is within the peak energy deviation threshold and the diffusion out-of-control index is lower than the minimum energy gradient change rate constraint in N consecutive iterations, freeze the current adjustment step size, feedback period, and magnetic field response coefficient in the feedback control parameters, and generate a lock signal, where N > 1.

8. The method according to claim 7, wherein The method further includes an energy stability verification step: After adjusting the feedback control parameters, start a preset number of energy distribution sampling periods, and continuously collect multiple groups of energy fluctuation data within each sampling period; Calculate the standard deviation of each group of energy fluctuation data, and use the median of all standard deviations as the current energy stability index; If the current energy stability index is lower than a preset stability threshold, it is determined that the ion beam energy adjustment is completed, and the current parameter configuration of the ion beam energy regulator is locked; If the current energy stability index is higher than the stability threshold, the adaptive compensation algorithm is re-executed, and the compensation intensity of the magnetic field gradient compensation mechanism is increased in each iteration until the maximum number of iterations is reached or the current energy stability index meets the standard.

9. The method according to claim 8, wherein After locking the current parameter configuration of the ion beam energy regulator, the following steps are executed: The instantaneous value of the vacuum chamber air pressure, the stable value of the ion source temperature, and the cooling system circulation rate at the locking moment are recorded in real time to generate an environmental parameter snapshot, and the environmental parameter snapshot is bound to the ion source current step size, magnetic field response coefficient, and pulse synchronization delay amount in the current parameter configuration and stored in the historical adjustment database; According to the energy distribution comparison chart before and after the adjustment in the energy adjustment report, the peak displacement amount of the energy density and the edge energy decay rate are extracted to generate an adjustment efficiency fingerprint, and the adjustment efficiency fingerprint includes a peak displacement direction identifier and a decay rate change ratio; The adjustment efficiency fingerprint is associated and stored with the environmental parameter snapshot, and an environmental matching degree label is added to each record in the historical adjustment database. The label is generated by weighted calculation of the vacuum chamber air pressure deviation degree, the ion source temperature fluctuation range, and the cooling rate consistency coefficient; When the initial environmental parameters of a new ion beam processing task are detected, the similarity between the initial environmental parameters and the environmental matching degree labels of each record in the historical adjustment database is calculated. If there is a record with a similarity exceeding a preset matching threshold, the ion source current step size, magnetic field response coefficient, and pulse synchronization delay amount in the record are directly loaded as the initial parameter configuration; After loading the historical parameter configuration, the steps of generating multi-level energy adjustment instructions are skipped, and the process directly enters the step of real-time detecting the energy fluctuation data of the ion beam on the sample surface after adjustment, and the signal acquisition timing of the array energy sensor is synchronously adjusted according to the loaded pulse synchronization delay amount.

10. A computer system, comprising a memory and a processor, the memory storing a computer program that can run on the processor, characterized in that, When the processor executes the program, it implements the steps in the method according to any one of claims 1 to 9.

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