Beam control optimization method for low-energy large-beam ion implanter

Abstract

The invention relates to the technical field of ion implantation, and discloses a beam control optimization method for a low-energy large-beam ion implanter. According to the method, beam current and energy monitoring values are continuously collected through a sensor system, and beam state characterization indexes are generated through real-time processing; comparing the index with a preset range, and triggering an abnormal signal when the index exceeds the range; in an abnormal state, a beam position data set is collected in a fixed period, a position influence evaluation value is calculated, and if the evaluation value reaches or exceeds a threshold value, the state is marked as a high influence state; in a high influence state, extracting a time change trend quantity and a mode similarity quantity of a beam parameter, and calculating a correlation strength coefficient; and obtaining an adjustment factor according to the high influence state, multiplying the current beam current reference value by the adjustment factor to obtain an optimized beam position value, and executing position adjustment. The method achieves the dynamic monitoring and precise adjustment of the beam state, and improves the adaptability and stability of beam control.

Description

Technical Field

[0001] This invention relates to the field of ion implantation technology, specifically to a method for optimizing beam current control in a low-energy, high-current ion implanter. Background Technology

[0002] In the semiconductor manufacturing field, low-energy high-current ion implanters are key equipment for achieving surface modification and doping of materials, and their beam current control accuracy directly affects chip performance and manufacturing yield. Currently, most beam current control methods are based on closed-loop adjustment of preset parameters and rely on fixed thresholds to trigger correction actions, making it difficult to cope with dynamic changes under complex operating conditions.

[0003] In actual operation, the beam is susceptible to interference from multiple factors such as vacuum environment fluctuations, ion source stability decay, and target surface charge accumulation, leading to nonlinear drift in beam current and energy. Traditional control methods exhibit lag in response to such dynamic disturbances, often initiating adjustments only after beam parameters exceed allowable ranges. By this time, the beam position has already shifted significantly, potentially causing localized overdoping or uneven doping of the target.

[0004] Existing methods lack quantitative analysis in assessing the impact of beam position changes, relying solely on simple position deviations to determine the necessity of adjustment, while ignoring the correlation between the temporal variation trend of beam parameters and historical patterns. When the beam is in a critical fluctuation state, over-adjustment introduces new system disturbances, while under-adjustment fails to effectively suppress deviations, leading to decreased beam stability and increased equipment maintenance costs and production downtime.

[0005] Under low-energy, high-current conditions, the uniformity of the beam spatial distribution has a significant impact on the implantation effect. Traditional control methods focus on the independent adjustment of a single parameter and fail to establish a synergistic optimization mechanism between beam current, energy, and position. This makes it difficult to maintain positional accuracy while ensuring beam intensity, thus restricting the application of ion implanters in high-precision manufacturing scenarios. Summary of the Invention

[0006] The purpose of this invention is to provide a method for optimizing beam control in low-energy, high-current ion implanters, in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a method for optimizing beam current control in a low-energy, high-current ion implanter, the method comprising:

[0008] The beam operating parameters, including beam current monitoring values ​​and beam energy monitoring values, are continuously collected by a sensor system installed on the ion implanter.

[0009] The beam operating parameters are processed in real time to generate beam state characterization indicators;

[0010] The beam state characterization index is compared and analyzed with the preset beam state range. If the beam state characterization index exceeds the preset beam state range, a beam abnormality signal is triggered.

[0011] When the beam anomaly signal is activated, the monitoring process of beam position data is initiated. Within a fixed monitoring period, beam position dataset is collected, and the beam position impact assessment value is calculated based on the beam position dataset.

[0012] The beam position influence assessment value is compared with the beam position influence threshold. If the beam position influence assessment value reaches or exceeds the beam position influence threshold, it is marked as a high beam position influence state.

[0013] Under the high-influence state marker of the beam position, the temporal variation trend and mode similarity of the beam parameters are extracted, and the temporal variation trend is divided by the mode similarity to obtain the correlation strength coefficient.

[0014] Based on the high-influence status marker of the beam position, an adjustment factor is obtained. The current beam current reference value is multiplied by the adjustment factor to obtain the optimized beam position value, and the beam position adjustment operation is performed.

[0015] Preferably, the method for generating the beam state characterization index includes: dividing the implantation area of ​​the ion implanter into multiple beam monitoring sub-regions and obtaining the beam current monitoring value in each beam monitoring sub-region;

[0016] Calculate the variance of the beam current monitoring values ​​for all beam monitoring sub-regions;

[0017] The variance value is correlated with the preset beam current reference value to obtain the beam state characterization index.

[0018] The beam state characterization index is used to transmit to the beam position monitoring process after the beam anomaly signal is activated.

[0019] Preferably, the calculation method for the beam position influence assessment value includes: obtaining the beam energy fluctuation contribution and the beam position distribution contribution;

[0020] The beam energy fluctuation contribution is added to the beam position distribution contribution to synthesize the beam position influence assessment value;

[0021] The beam position influence assessment value is transmitted to the high influence status marker of the beam position after it exceeds the threshold.

[0022] Preferably, the method for obtaining the contribution of beam energy fluctuation includes: identifying peak points and valley points in the beam energy data within the monitoring period, and calculating the energy change slope between adjacent peak points and valley points;

[0023] Simultaneously, peak and valley points in the beam current data are identified, and the slope of current change between adjacent peak and valley points is calculated.

[0024] Cluster analysis was performed on the slope of energy change and the slope of current change to identify time periods with similar changes, which were then marked as the influencing periods.

[0025] The duration of the affected period is statistically analyzed, and the variance of the duration percentage is calculated with the total duration of the monitoring period to obtain the contribution of beam energy fluctuation.

[0026] The beam energy fluctuation contribution is used to synthesize the beam position influence assessment value.

[0027] Preferably, the calculation method for the energy change slope and the current change slope includes: dividing the monitoring period into multiple time nodes, establishing a time series coordinate system, with the horizontal axis representing the time node sequence and the vertical axis representing the beam energy monitoring value, and drawing the beam energy change trajectory based on the beam energy monitoring value;

[0028] The energy peaks and valleys are extracted from the beam energy change trajectory. The slope of each energy peak and its adjacent energy valley is calculated to obtain the energy change slope.

[0029] At the same time, another time series coordinate system is established, with the horizontal axis representing the time node sequence and the vertical axis representing the beam current monitoring value. The beam current change trajectory is plotted based on the beam current monitoring value.

[0030] Extract the current peaks and troughs from the beam current change trajectory, and perform slope calculations on each current peak and its adjacent current trough to obtain the current change slope.

[0031] The energy change slope and current change slope are used for subsequent cluster analysis.

[0032] Preferably, the method for obtaining the contribution of the beam position distribution includes: calculating the deviation between the measured position value and the standard position value of each beam monitoring sub-region to obtain the sub-region position deviation value;

[0033] Compare the sub-region position deviation value with the sub-region position deviation threshold. If the sub-region position deviation value is greater than or equal to the sub-region position deviation threshold, then mark the sub-region as a positional abnormal sub-region.

[0034] The measured current value of each beam monitoring sub-region is obtained and the standard current value is compared with the deviation to calculate the sub-region current deviation value;

[0035] Compare the sub-region current deviation value with the sub-region current deviation threshold. If the sub-region current deviation value is greater than or equal to the sub-region current deviation threshold, then mark the sub-region as a current abnormal sub-region.

[0036] Identify the overlapping areas of the location anomaly sub-region and the current anomaly sub-region, mark them as abnormal overlapping sub-regions, and calculate the total area of ​​all abnormal overlapping sub-regions;

[0037] The contribution of the beam position distribution is obtained by proportionally calculating the total area of ​​the abnormally overlapping sub-regions to the total area of ​​the injection region.

[0038] The contribution of the beam position distribution is used to synthesize the beam position influence assessment value.

[0039] Preferably, the method for obtaining the sub-region position deviation value includes: using image monitoring technology to scan the beam position of the ion implanter and generating a beam position distribution map;

[0040] The beam position distribution map is divided into multiple monitoring sub-regions, and the average position value of each monitoring sub-region is measured as the measured position value.

[0041] The difference between the measured position value and the preset standard position value is calculated to obtain the sub-region position deviation value;

[0042] The method for obtaining the sub-region current deviation value includes: dividing the injection area space into multiple monitoring sub-regions, measuring the average current value of each monitoring sub-region, and using it as the measured current value;

[0043] The difference between the measured current value and the preset standard current value is calculated to obtain the sub-region current deviation value;

[0044] The sub-region position deviation value and the sub-region current deviation value are used to identify abnormally overlapping sub-regions.

[0045] Preferably, the method for obtaining the pattern similarity includes: extracting the energy change slope and the current change slope from the period of influence, calculating the correlation coefficient between the energy change slope and the current change slope for each period of influence, and obtaining the slope correlation value;

[0046] The slope correlation values ​​of all influencing periods are summarized and averaged to obtain the pattern similarity value; the pattern similarity value is used in combination with the time change trend value to calculate the correlation strength coefficient.

[0047] Preferably, the method for obtaining the time change trend includes: marking it as the change value based on the change slope corresponding to the period of influence;

[0048] The deviations between all impact change values ​​and the standard impact change values ​​within the monitoring period are calculated to obtain the impact change deviation set.

[0049] Standard deviation analysis was performed on the set of deviations affecting the changes to obtain the time-varying trend.

[0050] The time-varying trend is used in conjunction with the pattern similarity to calculate the correlation strength coefficient.

[0051] Preferably, the adjustment factor is obtained by summing all correlation strength coefficients, taking the average value, and then obtaining the adjustment factor.

[0052] The adjustment factor is used to multiply the current beam current reference value to achieve beam position adjustment.

[0053] Compared with the prior art, the beneficial effects of the present invention are:

[0054] This beam control optimization method continuously collects beam operating parameters through a sensor system, enabling real-time monitoring of the beam state. It breaks through the limitations of traditional control that relies on fixed-period sampling, and can promptly capture subtle changes in beam parameters, providing a dynamic data basis for subsequent adjustments.

[0055] Real-time processing of beam operating parameters and generation of state characterization indicators transform abstract parameter fluctuations into quantifiable evaluation criteria, making beam state assessment more objective and accurate. Through comparative analysis with preset ranges, a trigger signal can be generated at the initial stage of beam anomalies, avoiding the amplification of deviations caused by delayed responses in traditional methods and reducing the adverse effects of beam anomalies on target material processing.

[0056] After the beam anomaly signal is activated, position datasets are collected at fixed intervals and impact assessment values ​​are calculated, enabling quantitative analysis of the impact of beam position changes. This overcomes the limitations of traditional methods that rely solely on position deviations for judgment. By comparing the assessment values ​​with thresholds to mark high-impact states, it is possible to accurately identify operating conditions requiring intervention, avoiding unnecessary adjustments and reducing system disturbances.

[0057] By extracting the temporal variation trend and mode similarity of beam parameters and calculating the correlation strength coefficient, a correlation analysis between beam dynamic changes and historical stable modes was established, providing a multi-dimensional reference for the formulation of control strategies. This trend- and mode-based analysis method makes control actions more closely aligned with the actual variation patterns of the beam, improving the adaptability of control.

[0058] By obtaining adjustment factors based on high-influence states and optimizing beam position values, synergistic linkage between beam parameters and position adjustment is achieved, overcoming the limitations of independent parameter adjustment in traditional methods. By combining current reference values ​​with adjustment factors, beam position adjustment is achieved while maintaining beam intensity stability, which helps maintain the uniformity of beam spatial distribution under low-energy, high-current conditions and improves the consistency of the ion implantation process.

[0059] This method, through multi-stage collaborative design, forms a complete closed loop from parameter monitoring and status assessment to dynamic adjustment, making beam control more intelligent and precise. It can adapt to various interferences under complex working conditions, reduce equipment downtime and material loss caused by beam instability, extend the effective operating time of the equipment, and improve the reliability and economy of the ion implantation process. Attached Figure Description

[0060] Figure 1 This is a schematic diagram illustrating the working principle of the low-energy high-current ion implanter beam current control optimization method described in this invention.

[0061] Figure 2 A flowchart for generating beam state characterization indicators;

[0062] Figure 3 Flowchart for obtaining the contribution of beam energy fluctuations;

[0063] Figure 4 This is a flowchart for obtaining pattern similarity metrics. Detailed Implementation

[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] Please see Figure 1 This invention provides a method for optimizing beam current control in a low-energy, high-current ion implanter, the method comprising:

[0066] Beam operating parameters, including beam current and beam energy, are continuously acquired by a sensor system installed on the ion implanter. These parameters are processed in real time to generate beam state characterization indicators. The beam state characterization indicators are compared with a preset beam state range. If the indicators exceed the preset range, a beam anomaly signal is triggered. When the anomaly signal is active, beam position data monitoring is initiated. Within a fixed monitoring period, beam position datasets are collected, and beam position impact assessment values ​​are calculated based on these datasets. These assessment values ​​are compared with a beam position impact threshold. If the assessment value reaches or exceeds the threshold, the beam position is marked as having a high impact. Under this high-impact condition, the temporal trend and pattern similarity of the beam parameters are extracted. The temporal trend is divided by the pattern similarity to obtain the correlation strength coefficient. Based on the high-influence status marker of the beam position, the adjustment factor is obtained. The current beam current reference value is multiplied by the adjustment factor to obtain the optimized beam position value, and the beam position adjustment operation is performed.

[0067] Example 1: See Figure 2 The generation of beam state characterization indicators begins with the division of the injection area of ​​the ion implanter. Based on the physical structural characteristics of the injection area and the typical distribution pattern of the beam within it, the entire injection area is divided into multiple beam monitoring sub-regions. These sub-regions are spatially connected, with no overlap, and completely cover every corner of the injection area, ensuring that the beam can be effectively monitored at any location within the injection area. The size and shape of each sub-region are not fixed but are adjusted according to actual injection requirements and equipment structure to adapt to different working scenarios.

[0068] After the sub-regions are divided, the beam current in each monitoring sub-region is continuously monitored using a sensor system installed on the ion implanter. The sensor system collects current data for each sub-region at set time intervals. For each sub-region, the collected current data are averaged to obtain the beam current monitoring value for that sub-region. This averaging process can reduce the impact of instantaneous current fluctuations on the monitoring results to a certain extent, making the obtained monitoring values ​​more reflective of the actual beam current situation in the sub-region.

[0069] After acquiring the beam current monitoring values ​​for all beam monitoring sub-regions, the variance of these values ​​is calculated. The variance is calculated by comparing the difference between the current monitoring value of each sub-region and the average of the current monitoring values ​​for all sub-regions, squaring these differences, summing them, and finally dividing by the number of sub-regions. This variance clearly reflects the dispersion of beam current among the beam monitoring sub-regions; a larger variance indicates a more significant difference in beam current between different sub-regions; conversely, a smaller variance indicates a relatively uniform distribution of beam current across the sub-regions.

[0070] The calculated variance value is correlated with a preset beam current reference value. This preset beam current reference value is determined based on the ion implanter's model, operating parameters, and past operating experience; it represents a reference standard for the variance of the beam current monitoring value under normal operating conditions. The correlation operation involves multiplying the variance value by a preset weighting coefficient and then adding it to the preset beam current reference value to obtain the beam state characterization index. This preset weighting coefficient is also pre-set based on the specific equipment and actual operating requirements; its function is to adjust the proportion of the variance value in the beam state characterization index to more accurately reflect the actual beam state. After the beam state characterization index is generated, when a beam anomaly signal is activated, this index is transmitted to the beam position monitoring process, providing initial analytical basis for subsequent beam position monitoring.

[0071] Calculating the beam position impact assessment value requires first obtaining the contribution of beam energy fluctuations and the contribution of beam position distribution. The contribution of beam energy fluctuations is obtained by analyzing the fluctuation characteristics of beam energy data within the monitoring period. During monitoring, changes in beam energy are continuously recorded. By observing the fluctuations in energy data and analyzing their amplitude, frequency, and other characteristics, the potential impact of energy fluctuations on the beam position can be determined, ultimately yielding the contribution of beam energy fluctuations.

[0072] The beam position distribution contribution is derived by analyzing the position distribution deviation of each beam monitoring sub-region. For each beam monitoring sub-region, the actual monitored position is compared with the preset standard position, the position deviation is calculated, and then the impact of the beam position distribution on the overall beam state is evaluated based on the magnitude and distribution of these deviations, thus obtaining the beam position distribution contribution.

[0073] After obtaining the contributions of beam energy fluctuation and beam position distribution, the two are added together to synthesize the beam position impact assessment value. This addition uses a simple arithmetic method, meaning the contributions of beam energy fluctuation and beam position distribution have the same weight in the calculation; simply adding the two values ​​yields the beam position impact assessment value. This assessment value comprehensively reflects the overall impact of beam energy fluctuation and position distribution deviation on the beam position. When the beam position impact assessment value exceeds a preset threshold, this information is transmitted to the high-impact state marker for the beam position, thereby determining whether the beam is in a high-impact state requiring close attention and adjustment.

[0074] Example 2: See Figure 3 The acquisition of the contribution of beam energy fluctuations begins with the continuous recording of beam energy and current data during the monitoring period. Throughout the monitoring process, instantaneous values ​​of beam energy and current need to be continuously collected, and these data will serve as the basis for subsequent analysis. First, the beam energy data is processed to identify peak and trough points. A peak point refers to the energy value recorded at a given moment, which is higher than the energy values ​​of the adjacent preceding and following moments; a trough point is the energy value at a given moment, which is lower than the energy values ​​of the adjacent preceding and following moments. Similarly, the beam current data undergoes the same processing to identify current peak and trough points, using the same criteria as for energy peak and trough points.

[0075] After identifying adjacent peak and valley points, the slopes of energy and current changes between them are calculated. The energy change slope is calculated by dividing the energy difference between adjacent peak and valley points by the time difference between these two points. The current change slope is calculated similarly, by dividing the current difference between adjacent current peak and valley points by the corresponding time difference. These slope values ​​reflect the rate and trend of change in beam energy and current over a specific time period.

[0076] Cluster analysis was performed on the slopes of energy and current changes. During cluster analysis, slope data with similar trends were grouped into the same category. This method identifies time periods with similar energy and current changes and marks these time periods as influencing periods. Identifying these influencing periods helps to focus on intervals where both energy and current change significantly, and these intervals often have a more pronounced effect on beam position.

[0077] The total duration of all affected time periods is calculated, and then the proportion of this total duration to the total duration of the entire monitoring period is calculated, i.e., the duration percentage. Next, the variance of the duration percentage and the total monitoring period duration is calculated using the following formula:

[0078]

[0079] in, This indicates the contribution of beam energy fluctuations. Indicates the percentage of time spent. This represents the average percentage of time spent on the program. This represents the total duration of the monitoring period. The beam energy fluctuation contribution calculated in this way will be used to subsequently synthesize the beam position impact assessment value.

[0080] The specific calculation of the energy change slope and current change slope requires first dividing the monitoring period into time nodes. The interval of the time nodes is set based on the sensor's sampling frequency to ensure that each time node corresponds to a set of valid monitoring data, thereby guaranteeing the continuity and integrity of the data. A time series coordinate system is established, with the horizontal axis representing the time node sequence, arranged sequentially in chronological order; the vertical axis represents the beam energy monitoring value. Based on the energy monitoring value corresponding to each time node, a beam energy change trajectory is plotted. This trajectory is a continuous curve passing through the energy values ​​of each time node, which can intuitively show the change of beam energy within the monitoring period.

[0081] Energy peaks and troughs are extracted from the beam energy change trajectory. The extraction criteria are that, among three adjacent time points, the energy value of the middle node is higher or lower than the energy values ​​of the two adjacent nodes. For each energy peak, the slope of its adjacent energy trough is calculated by dividing (peak energy value - trough energy value) by the time interval between the two points, thus obtaining the energy change slope.

[0082] Another time-series coordinate system is established, with the horizontal axis representing the time node sequence and the vertical axis representing the beam current monitoring values. Based on the current monitoring values ​​at each time node, the beam current variation trajectory is plotted. Using the same criteria as for extracting energy peaks and troughs, current peaks and troughs are extracted from the current variation trajectory. Then, the slope of each current peak and its adjacent current trough is calculated: (peak current value - trough current value) divided by the time interval between the two points, yielding the current variation slope. These energy variation slopes and current variation slopes will serve as the basis for subsequent cluster analysis, supporting the accurate identification of influencing periods.

[0083] Example 3: The process of obtaining the contribution of beam position distribution begins with the calculation of the position deviation of each beam monitoring sub-region. Each beam monitoring sub-region has its corresponding measured position value, which is collected in real time by a position sensor installed on the ion implanter, covering the specific coordinate information of the sub-region in the spatial coordinate system. At the same time, each sub-region also has a preset standard position value. These standard values ​​are determined based on the equipment design drawings, the target position requirements of the implantation process, and the benchmark data accumulated over a long period of operation, representing the ideal position of the beam in the sub-region under normal operating conditions. The deviation between the measured position value and the standard position value of each sub-region is calculated to obtain the sub-region position deviation value. The calculation method is the square root of the sum of the squares of the differences between the two along each axis in the spatial coordinate system, thus comprehensively reflecting the degree of position deviation in three-dimensional space.

[0084] After obtaining the position deviation values ​​of all sub-regions, each value is compared with the sub-region position deviation threshold. The setting of the sub-region position deviation threshold needs to consider the tolerance range of the target material, the implantation accuracy level, and the process characteristics of different materials. Different types of ion implantation tasks may correspond to different threshold standards. When the position deviation value of a certain sub-region reaches or exceeds the sub-region position deviation threshold, the beam position of that sub-region is determined to be outside the acceptable range, and it is marked as a positionally abnormal sub-region.

[0085] After marking the abnormal sub-regions, the analysis of current deviations in each sub-region is initiated. The measured current value of each beam monitoring sub-region is collected by a ring-shaped current sensor deployed at the sub-region's edge. This sensor captures the average current intensity of the beam within the sub-region. The corresponding standard current value is pre-set based on the sub-region's functional positioning in the implantation sequence, the target doping requirements, and the equipment's rated output parameters. This represents the current level that the sub-region should maintain during normal implantation. The deviation between the measured current value and the standard current value for each sub-region is calculated as the absolute difference between the two, thus visually reflecting the current fluctuation range.

[0086] The current deviation value of each sub-region is compared with the sub-region current deviation threshold, which is determined based on the beam stability requirements, the current withstand capability of the target material, and the injection uniformity index. When the current deviation value of a certain sub-region reaches or exceeds the sub-region current deviation threshold, it indicates that the current fluctuation in that sub-region has affected the injection effect, and it is marked as a current abnormal sub-region.

[0087] After marking the sub-regions with abnormal position and abnormal current respectively, the overlapping portions of the two types of abnormal sub-regions are identified by spatial coordinate comparison. The overlapping portions are sub-regions that are simultaneously marked as having both abnormal position and abnormal current; these sub-regions are grouped together and marked as abnormal overlapping sub-regions. The existence of these sub-regions means that the beam current in this region deviates from the normal state in both position and intensity, and needs to be included in the scope of key analysis.

[0088] Calculate the total area of ​​all overlapping sub-regions. The area of ​​each beam monitoring sub-region is determined during the sub-regioning process based on the geometry of the injection region and the number of sub-regions. For example, the area of ​​a rectangular sub-region is the product of its length and width, while the area of ​​a sector-shaped sub-region is calculated based on its central angle and radius. The areas of all overlapping sub-regions are summed to obtain the total area of ​​the overlapping sub-regions, which reflects the size of the region where both location and current anomalies exist simultaneously.

[0089] The beam position distribution contribution is calculated using the following formula:

[0090]

[0091] in, This indicates the contribution of the beam position distribution. This represents the total area of ​​the abnormally overlapping sub-regions. This represents the total area of ​​the injected region.

[0092] This formula transforms the scale of spatial anomalies into a quantifiable indicator by calculating the percentage of the total area of ​​the anomalous overlapping sub-regions to the total area of ​​the injection region. A larger value for the beam position distribution contribution indicates a higher proportion of the anomalous region within the entire injection region, and a more significant degree of anomalous beam position distribution. This indicator, along with the beam energy fluctuation contribution, contributes to the synthesis of the beam position influence assessment value, providing a key parameter for subsequent beam state determination.

[0093] Example 4: See Figure 4 The acquisition of sub-region position deviation values ​​relies on image monitoring technology to scan the beam position. In practice, a fluorescent screen is placed at the end of the implantation region of the ion implanter. When the beam bombards the fluorescent screen, a spot corresponding to the beam position is generated. A charge-coupled device (CCD) camera continuously captures images of the fluorescent screen, converting the optical signal of the spot into an electrical signal, thereby generating a grayscale image containing beam position information, i.e., a beam position distribution map. Different grayscale values ​​in the image represent differences in beam intensity, while the geometric center of the spot corresponds to the actual position of the beam.

[0094] The beam position distribution map is divided into multiple monitoring sub-regions according to a preset grid. The grid size is determined based on the size of the injection area and the required monitoring accuracy, ensuring that each sub-region covers a sufficient beam area while also reflecting subtle positional differences. For each monitoring sub-region, the coordinate information of all pixels is extracted using image analysis software, and the arithmetic mean of these coordinates is calculated to obtain the average position value of that sub-region, which is used as the measured position value. Each sub-region has a preset standard position value, determined based on the injection process design parameters, representing the ideal position of the beam in that sub-region. The deviation between the measured position value and the standard position value is calculated by calculating the coordinate differences in the horizontal and vertical directions, and then using geometric calculations to obtain the comprehensive deviation, i.e., the sub-region position deviation value.

[0095] To obtain the sub-region current deviation value, the injection area must first be divided into sub-regions, with the division method completely consistent with the sub-region division of the beam position distribution map to ensure the correspondence between position and current data. A miniature current sensor is installed at the edge of each monitoring sub-region, with the sensor's sensing surface perpendicular to the beam path, enabling real-time acquisition of the beam current intensity within that sub-region. The instantaneous current values ​​acquired by the sensors during the monitoring period are averaged to obtain the average current value of that sub-region, which is used as the measured current value. Each sub-region also has a preset standard current value, determined based on the injection dose requirements and beam density distribution, reflecting the current level that the sub-region should maintain under normal operating conditions. The absolute difference between the measured current value and the standard current value is calculated to obtain the sub-region current deviation value.

[0096] Obtaining pattern similarity requires extracting the energy change slope and current change slope from the previously identified influencing periods. Each influencing period corresponds to a set of synchronously collected energy change slope and current change slope data, reflecting the rate of change of beam energy and current within that period. Correlation analysis is performed on each set of slope data to determine the consistency of their changing trends. During the analysis, the correlation between the two sets of data is calculated using statistical methods. If the current change slope also increases as the energy change slope increases, or both decrease simultaneously, it indicates that the two have similar changing trends and a high degree of correlation; conversely, the correlation is low.

[0097] The correlation data for all affected time periods are aggregated and averaged to obtain the pattern similarity. Below is an example of the slope correlation data for affected time periods within a specific monitoring period.

[0098] Table 1: Example of slope correlation data for the period of influence within a certain monitoring period.

[0099] Affected period number energy change slope Current change slope Slope correlation value 1 0.8 0.7 0.85 2 -0.6 -0.5 0.82 3 0.5 0.4 0.78 4 -0.7 -0.6 0.80 5 0.6 0.5 0.79

[0100] The average of the slope correlation values ​​in the table, i.e., (0.85+0.82+0.78+0.80+0.79)÷5, yields the mode similarity value for that monitoring period. The magnitude of the mode similarity value reflects the overall similarity between beam energy changes and current changes during the affected period, providing basic data for subsequent calculation of the correlation strength coefficient.

[0101] The calculation of sub-region position deviation and current deviation values ​​provides a quantitative basis for identifying abnormal sub-regions, while the calculation of mode similarity reflects the correlation characteristics between energy and current from a dynamic perspective. These data collectively participate in subsequent stages of beam control optimization, promoting the precise implementation of beam position adjustments. The accuracy of position and current data is ensured by combining image analysis with sensor monitoring; the objectivity of mode similarity is guaranteed by statistical analysis of slope data. This ensures that the entire implementation process both closely reflects the actual operating state of the equipment and meets the process requirements for data accuracy.

[0102] Example 5: Obtaining the temporal variation trend begins with processing the slope of the variation within the affected period. The affected period is the time segment where energy and current variations exhibit similar characteristics, as previously identified. Each affected period corresponds to a set of energy variation slopes and current variation slopes. The arithmetic mean of these two slope values ​​is taken, and the result is labeled as the influence variation value. This operation integrates the variation trends of both energy and current into a comprehensive index to more concisely reflect the overall variation characteristics of the beam parameters within that period.

[0103] After determining all impact change values, they need to be compared with standard impact change values. Standard impact change values ​​are the average slope of change over the impact period, obtained through long-term monitoring and data accumulation under normal equipment operating conditions. They represent the typical change level of the beam parameters during stable operation. The difference between each impact change value and the standard impact change value is calculated; these differences collectively constitute the impact change deviation set. Each value in the deviation set reflects the degree of deviation between the actual change and the standard state during the corresponding impact period.

[0104] Performing standard deviation analysis on the set of deviations is a crucial step in obtaining a measure of the time-varying trend. Standard deviation analysis measures the overall volatility of these deviations by calculating the dispersion of all values ​​in the set. Specifically, it first calculates the average of all values ​​in the set, then calculates the difference between each deviation value and this average, squares these differences, sums them, divides by the number of deviation values, and finally takes the square root to obtain the result. This result is the measure of the time-varying trend, reflecting the overall fluctuation trend of the affected values ​​over time. The larger the value, the more inconsistent the changes in each affected period are with the standard state, and the more drastic the fluctuations.

[0105] The moderating factor is obtained based on the correlation strength coefficient. The correlation strength coefficient is obtained by dividing the temporal variation trend of each affected period by the pattern similarity, thus combining the variation trend of beam parameters and pattern similarity within that period. The correlation strength coefficients of all affected periods are summed, and then the sum is divided by the number of correlation strength coefficients; the average value obtained is the moderating factor. This averaging process smooths out the potential impact of extreme data in individual periods, allowing the moderating factor to more objectively reflect the overall correlation strength level.

[0106] The application of the adjustment factor is reflected in the specific adjustment of the beam position. The current reference beam current value is the current setpoint when the equipment was in a stable operating state before the beam anomaly signal was activated; it represents the current benchmark during normal operation. Multiplying the current reference beam current value by the adjustment factor yields the optimized beam position value. This value integrates the current reference current level and the adjustment coefficient derived from historical data, providing specific target parameters for beam position adjustment.

[0107] The beam position adjustment is performed based on the optimized beam position value, primarily by adjusting the deflection electrode voltage of the ion implanter. The deflection electrode is a key component controlling the beam trajectory; changes in its voltage alter the electric field strength between the electrodes, thus affecting the beam deflection direction and degree. The control system calculates the required voltage adjustment based on the difference between the optimized beam position value and the current actual beam position, and then sends a control signal to the power supply module of the deflection electrode to achieve precise voltage regulation. This closed-loop adjustment process can correct the beam trajectory in real time, gradually bringing the beam position closer to the optimized value, thereby improving beam stability and implantation accuracy.

[0108] From parameter extraction during the influencing period to the calculation of trend values ​​and adjustment factors, and finally to voltage regulation, a complete feedback control chain is formed. Each step is based on actual monitoring data, which is transformed into executable control parameters through statistical analysis and mathematical calculations, ensuring the scientific rigor and targeted nature of beam position adjustments. This method can adapt to dynamic changes in beam parameters, respond promptly to anomalies, and maintain optimal operation of the ion implanter in complex working environments.

[0109] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0110] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing beam current control in a low-energy, high-current ion implanter, characterized in that, The method includes: The beam operating parameters, including beam current monitoring values ​​and beam energy monitoring values, are continuously collected by a sensor system installed on the ion implanter. The beam operating parameters are processed in real time to generate beam state characterization indicators; The beam state characterization index is compared and analyzed with the preset beam state range. If the beam state characterization index exceeds the preset beam state range, a beam abnormality signal is triggered. When the beam anomaly signal is activated, the monitoring process of beam position data is initiated. Within a fixed monitoring period, beam position dataset is collected, and the beam position impact assessment value is calculated based on the beam position dataset. The beam position influence assessment value is compared with the beam position influence threshold. If the beam position influence assessment value reaches or exceeds the beam position influence threshold, it is marked as a high beam position influence state. Under the high-influence state marker of the beam position, the temporal variation trend and mode similarity of the beam parameters are extracted, and the temporal variation trend is divided by the mode similarity to obtain the correlation strength coefficient. Based on the high-influence status marker of the beam position, an adjustment factor is obtained. The current beam current reference value is multiplied by the adjustment factor to obtain the optimized beam position value, and the beam position adjustment operation is performed.

2. The method for optimizing beam current control in a low-energy, high-current ion implanter as described in claim 1, characterized in that, The methods for generating the beam state characterization index include: The implantation area of ​​the ion implanter is divided into multiple beam monitoring sub-regions, and the beam current monitoring value in each beam monitoring sub-region is obtained; Calculate the variance of the beam current monitoring values ​​for all beam monitoring sub-regions; The variance value is correlated with the preset beam current reference value to obtain the beam state characterization index. The beam state characterization index is used to transmit to the beam position monitoring process after the beam anomaly signal is activated.

3. The method for optimizing beam current control in a low-energy, high-current ion implanter as described in claim 2, characterized in that, The calculation method for the beam position influence assessment value includes: Obtain the contribution of beam energy fluctuation and beam position distribution; The beam energy fluctuation contribution is added to the beam position distribution contribution to synthesize the beam position influence assessment value; The beam position influence assessment value is transmitted to the high influence status marker of the beam position after it exceeds the threshold.

4. The method for optimizing beam current control in a low-energy, high-current ion implanter as described in claim 3, characterized in that, The methods for obtaining the beam energy fluctuation contribution include: During the monitoring period, identify the peak and valley points in the beam energy data, and calculate the slope of energy change between adjacent peak and valley points. Simultaneously, peak and valley points in the beam current data are identified, and the slope of current change between adjacent peak and valley points is calculated. Cluster analysis was performed on the slope of energy change and the slope of current change to identify time periods with similar changes, which were then marked as the influencing periods. The duration of the affected period is statistically analyzed, and the variance of the duration percentage is calculated with the total duration of the monitoring period to obtain the contribution of beam energy fluctuation. The beam energy fluctuation contribution is used to synthesize the beam position influence assessment value.

5. The method for optimizing beam current control in a low-energy, high-current ion implanter as described in claim 4, characterized in that, The calculation methods for the energy change slope and the current change slope include: The monitoring period is divided into multiple time nodes, and a time series coordinate system is established. The horizontal axis represents the time node sequence, and the vertical axis represents the beam energy monitoring value. The beam energy change trajectory is plotted based on the beam energy monitoring value. The energy peaks and valleys are extracted from the beam energy change trajectory. The slope of each energy peak and its adjacent energy valley is calculated to obtain the energy change slope. At the same time, another time series coordinate system is established, with the horizontal axis representing the time node sequence and the vertical axis representing the beam current monitoring value. The beam current change trajectory is plotted based on the beam current monitoring value. Extract the current peaks and troughs from the beam current change trajectory, and perform slope calculations on each current peak and its adjacent current trough to obtain the current change slope. The energy change slope and current change slope are used for subsequent cluster analysis.

6. The method for optimizing beam current control in a low-energy, high-current ion implanter as described in claim 5, characterized in that, The methods for obtaining the beam position distribution contribution include: The deviation between the measured position value and the standard position value of each beam monitoring sub-region is calculated to obtain the sub-region position deviation value; Compare the sub-region position deviation value with the sub-region position deviation threshold. If the sub-region position deviation value is greater than or equal to the sub-region position deviation threshold, then mark the sub-region as a positional abnormal sub-region. The measured current value of each beam monitoring sub-region is obtained and the standard current value is compared with the deviation to calculate the sub-region current deviation value; Compare the sub-region current deviation value with the sub-region current deviation threshold. If the sub-region current deviation value is greater than or equal to the sub-region current deviation threshold, then mark the sub-region as a current abnormal sub-region. Identify the overlapping areas of the location anomaly sub-region and the current anomaly sub-region, mark them as abnormal overlapping sub-regions, and calculate the total area of ​​all abnormal overlapping sub-regions; The contribution of the beam position distribution is obtained by proportionally calculating the total area of ​​the abnormally overlapping sub-regions to the total area of ​​the injection region. The contribution of the beam position distribution is used to synthesize the beam position influence assessment value.

7. The method for optimizing beam current control in a low-energy, high-current ion implanter as described in claim 6, characterized in that, The methods for obtaining the sub-region position deviation value include: Image monitoring technology is used to scan the beam position of the ion implanter and generate a beam position distribution map; The beam position distribution map is divided into multiple monitoring sub-regions, and the average position value of each monitoring sub-region is measured as the measured position value. The difference between the measured position value and the preset standard position value is calculated to obtain the sub-region position deviation value; The method for obtaining the sub-region current deviation value includes: dividing the injection area space into multiple monitoring sub-regions, measuring the average current value of each monitoring sub-region, and using it as the measured current value; The difference between the measured current value and the preset standard current value is calculated to obtain the sub-region current deviation value; The sub-region position deviation value and the sub-region current deviation value are used to identify abnormally overlapping sub-regions.

8. The method for optimizing beam current control in a low-energy, high-current ion implanter as described in claim 7, characterized in that, The methods for obtaining the pattern similarity include: Extract the slope of energy change and the slope of current change from the period of influence, and calculate the correlation coefficient between the slope of energy change and the slope of current change for each period of influence to obtain the slope correlation value; The slope correlation values ​​of all influencing periods are summarized and averaged to obtain the pattern similarity value; the pattern similarity value is used in combination with the time change trend value to calculate the correlation strength coefficient.

9. The method for optimizing beam current control in a low-energy, high-current ion implanter as described in claim 7, characterized in that, The methods for obtaining the time-varying trend include: Based on the slope of change corresponding to the period of influence, the value of change is marked as the value of influence. The deviations between all impact change values ​​and the standard impact change values ​​within the monitoring period are calculated to obtain the impact change deviation set. Standard deviation analysis was performed on the set of deviations affecting the changes to obtain the time-varying trend. The time-varying trend is used in conjunction with the pattern similarity to calculate the correlation strength coefficient.

10. The method for optimizing beam current control in a low-energy, high-current ion implanter as described in claim 1, characterized in that, The methods for obtaining the regulation factor include: Sum all correlation strength coefficients, calculate the sum and take the mean to obtain the adjustment factor; The adjustment factor is used to multiply the current beam current reference value to achieve beam position adjustment.

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