Method and system for detecting local high-density points in ribbon ion beam

Abstract

The invention discloses a method and system for detecting local high-density points in a ribbon ion beam, and the method comprises the steps: S1, carrying out the scanning measurement of the ribbon ion beam through a two-dimensional Faraday cup array in a mobile Faraday device, and collecting the beam intensity data of each measurement point; s2, generating a two-dimensional flow intensity distribution cloud picture of the strip-shaped ion beam based on the beam intensity data; s3, calculating the beam height and the vertical center position of the strip-shaped ion beam in the vertical direction according to the beam intensity data; according to the beam height and the vertical center position, area data between the upper limit and the lower limit of the beam height in the wafer range is extracted from the two-dimensional beam intensity distribution cloud picture, and a data matrix is formed; and S4, carrying out second derivative curvature analysis or local flow intensity proportion analysis on the data matrix, screening out local high-density points, and outputting the number and position information of the local high-density points. The system has the advantages of heat quantification, intelligent detection and the like.

Description

Technical Field

[0001] This invention mainly relates to the field of semiconductor equipment manufacturing technology, specifically to a method and system for detecting local high-density points in a ribbon ion beam. Background Technology

[0002] With the development of semiconductor integrated circuit manufacturing processes, higher demands are being placed on the performance of semiconductor manufacturing equipment. Ion beam implanters are core doping equipment in semiconductor device manufacturing. The ultimate goal of ion implantation is to achieve a precise and uniform doping concentration on the silicon wafer. Beam current density directly determines the implantation dose; uneven density means different implantation doses in different areas. As wafer sizes enter the 300mm era, advanced device manufacturing processes are continuously advancing towards 5nm and even 3nm. To ensure the consistency of device performance across the entire wafer, higher requirements must be placed on the uniformity of the ion implantation doping process.

[0003] Excessively high local beam current density (i.e., "hot spots") leads to overdoping in the corresponding areas, severely affecting the consistency of device performance and becoming one of the key factors restricting the yield of advanced processes. Currently, the industry generally relies on manual observation of the color distribution of beam current maps to qualitatively judge uniformity. This method has significant drawbacks such as low efficiency, high subjectivity, and inability to quantitatively identify and locate "hot spots," making it difficult to meet the demands of real-time, quantitative, and intelligent process monitoring in advanced manufacturing. Therefore, developing a quantitative method that can automatically and accurately detect and locate local high-density points in ribbon ion beams has become an urgent technical problem to be solved in the industry. Summary of the Invention

[0004] To address the technical problems existing in the prior art, the present invention provides a quantitative and automated method and system for detecting local high-density points in a strip ion beam.

[0005] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows: A method for detecting localized high-density points in a ribbon ion beam, comprising the following steps: S1. The ribbon ion beam is scanned and measured by moving the two-dimensional Faraday cup array in the Faraday device, and the beam intensity data at each measurement point is collected; S2. Generate a two-dimensional current distribution cloud map of the ribbon ion beam based on the beam intensity data; S3. Calculate the beam height and vertical center position of the ribbon ion beam in the vertical direction based on the beam intensity data; based on the beam height and vertical center position, extract the regional data within the wafer range and between the upper and lower limits of the beam height from the two-dimensional current intensity distribution cloud map to form a data matrix; S4. Perform second-derivative curvature analysis or local flow intensity ratio analysis on the data matrix to filter out local high-density points and output the number and location information of the local high-density points.

[0006] Preferably, in step S4, the specific process of performing second-derivative curvature analysis on the data matrix to screen out locally high-density points is as follows: S401. Obtain the data matrix M(x,y), where x and y are the matrix row and column indices, and the matrix element values ​​are the normalized beam intensity values ​​I; S402. Calculate the surface curvature value C of each valid data point (x,y) in the data matrix in two-dimensional space; S403. Traverse the calculated curvature matrix and identify all points whose curvature value C exceeds a preset threshold as candidate hotspots; S404. Select candidate hotspots that exceed the preset intensity threshold as the final effective local high-density points.

[0007] Preferably, in step S403, the corresponding preset intensity threshold is μ+N*σ, where μ and σ are the average flow intensity and standard deviation of all data in the data matrix, respectively; N is an adjustable parameter.

[0008] Preferably, before step S401, conditional filtering is also included: if the average current intensity μ of the region is lower than a preset low threshold, the overall signal is considered too weak and hot spot detection is meaningless. In this case, curvature analysis is not performed or no hot spot is directly determined. If the calculated beam height is less than the preset minimum process requirement value, it indicates that the beam itself is very narrow and its natural focusing will produce a high curvature point. In this case, the judgment condition is relaxed or a beam height abnormality alarm is directly output.

[0009] Preferably, in step S4, the specific process of performing local flow intensity ratio analysis on the data matrix to screen out local high-density points is as follows: S41. In the data matrix, identify the point with the largest current intensity value as the center point; S42. Using the center point as the core, obtain all data points within the surrounding preset neighborhood to form an analysis sub-region; S43. Calculate the first proportion of the current intensity value at the center point to the total current intensity value of the analysis sub-region, and the second proportion of the average current intensity value at the other points in the analysis sub-region excluding the center point to the total current intensity value of the analysis sub-region. S44. Calculate the ratio R of the first ratio to the second ratio; S45. Compare the ratio R with a preset ratio threshold; if the ratio R exceeds the preset ratio threshold, then identify the center point as the local high-density point.

[0010] Preferably, in step S42, if the center point is located at the boundary of the data matrix, the analysis sub-region is adaptively translated to ensure its integrity.

[0011] Preferably, in step S3, the specific process of calculating the beam height and vertical center position of the ribbon ion beam in the vertical direction based on the beam intensity data is as follows: integrate or fit all vertical direction current intensity data at each horizontal position to obtain the vertical beam profile at that position; and calculate the beam height and vertical center of the ribbon ion beam by analyzing all vertical profiles within the entire scanning width range.

[0012] The present invention also discloses a device for detecting local high-density points in a ribbon ion beam, comprising: A mobile Faraday device, which contains a two-dimensional Faraday cup array; The signal acquisition module is used to acquire the beam intensity signal of each cup in real time; The data processing module is used to execute the steps of the detection method for local high-density points in a strip ion beam as described above, so as to output detection results; the detection results include the number and location information of local high-density points; The output module is used to display the detection results or output control signals.

[0013] Preferably, the two-dimensional Faraday cup array includes two columns of graphite cups arranged alternately in the vertical direction, each graphite cup being independently connected to a signal acquisition channel to synchronously acquire beam signals; the two-dimensional Faraday cup array includes 24 graphite cups, arranged in two horizontal columns at intervals and staggered in the vertical direction, with the center of each cup fixed in the vertical direction.

[0014] The present invention also discloses a detection system for local high-density points in a ribbon ion beam, comprising a memory and a processor connected to each other, wherein the memory stores a computer program, and the computer program executes the steps of the method described above when run by the processor.

[0015] Compared with the prior art, the advantages of the present invention are as follows: This invention, through an innovative combination of algorithms and hardware, achieves quantitative and automated detection of localized high-density points ("hot spots") in the ion implantation process. This method overcomes the limitations of traditional methods that rely on manual observation of two-dimensional contour maps for qualitative judgment. It can accurately identify the number of hot spots and precisely locate their spatial distribution, thus providing a reliable basis for process monitoring. By detecting and issuing early warnings of beam uniformity anomalies in real time, equipment parameters can be adjusted promptly, effectively avoiding inconsistent device performance caused by localized over-implantation, and significantly improving the overall uniformity and product yield of the ion implantation process. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the structure of the mobile connection device of the present invention in an embodiment.

[0017] Figure 2 This is a schematic diagram of the structure of the two-dimensional Faraday cup array of the present invention in an embodiment.

[0018] Figure 3 This is a schematic diagram of the two-dimensional current intensity distribution cloud map of the present invention in Embodiment 1.

[0019] Figure 4 This is a schematic diagram of a test example in this invention; where (a) is the original image; (b) is the filtered image; (c) is a schematic diagram after curvature analysis processing; and (d) is the output report.

[0020] Figure 5 This is a flowchart of the detection method of the present invention in an embodiment.

[0021] Figure 6 This is a schematic diagram of the analysis sub-region in this invention.

[0022] Figure 7 This is a schematic diagram of the two-dimensional current intensity distribution cloud map of the present invention in Embodiment 2; wherein (a) is a two-dimensional current intensity distribution cloud map; and (b) is a partial view.

[0023] Figure 8 This is a schematic diagram of a test example in this invention; where (a) represents the test parameters and (b) represents the output report.

[0024] Legend: 1. Moving Faraday cup device; 2. Two-dimensional Faraday cup array. Detailed Implementation

[0025] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0026] The method for detecting local high-density points in a ribbon ion beam provided in this embodiment of the invention relies on, for example, the following. Figure 1 The hardware system shown includes a core moving Faraday cup device 1. Driven by a scanning motor, this device can pass through the cross-section of the strip ion beam at a uniform speed in the horizontal direction (i.e., the width direction of the ion beam), thereby completing the scanning measurement of the entire beam profile. The moving Faraday cup device 1 integrates a two-dimensional Faraday cup array 2. (As shown...) Figure 2 As shown, the two-dimensional Faraday cup array 2 consists of 24 independent graphite cups. These cups are arranged horizontally in two columns with a certain distance between them, and are staggered in the vertical direction (i.e., the beam height direction). The center positions of the 24 cups are fixed in the vertical direction, and the 24 cups are independent of each other and do not interfere with each other.

[0027] This staggered design achieves higher spatial sampling density in the vertical direction without significantly increasing the device size. Each graphite cup is connected to an independent channel of the multi-channel data acquisition card via an independent signal wire. Subsequent circuit processing yields the beam current magnitude at each point, providing the hardware foundation and data support for subsequent two-dimensional contour mapping and calculation of beam height and vertical center.

[0028] Example 1 Based on the above hardware system, the method for detecting local high-density points in a strip ion beam according to embodiments of the present invention is as follows: Figure 5 As shown, it includes the following steps: S1. When the ion beam bombards the graphite cup, the graphite material conducts electricity, converting the intensity (current intensity) of the ion beam into a weak current signal. These current signals are synchronously acquired, amplified, and converted from analog to digital at a high sampling rate (e.g., tens of thousands of times per second), ultimately obtaining a beam intensity data sequence that corresponds one-to-one with 24 spatial points and varies over time (corresponding to the horizontal scanning position).

[0029] S2. By spatially mapping and interpolating all data points acquired from the 24 channels in a single scan (each channel can acquire thousands of data points), a continuous two-dimensional current intensity distribution cloud map reflecting the beam density distribution across the entire scanning area can be generated, such as... Figure 3 As shown, the generated two-dimensional current intensity distribution cloud map visually displays the beam density using color coding, typically employing a gradient color scheme from blue (low density) to red (high density), with deeper reds representing higher beam density. This cloud map can qualitatively reveal the overall beam profile, uniformity, and potentially existing anomalously high-density regions (i.e., "hot spots").

[0030] S3. To achieve quantitative detection, the system needs to extract key parameters from the data. First, the current intensity data in all vertical directions (Y coordinates) at each horizontal position (X coordinate) are integrated or fitted (e.g., using Gaussian fitting) to obtain the vertical beam profile at that position. By analyzing all vertical profiles across the entire scan width, two key vertical parameters of the ribbon ion beam can be accurately calculated: beam height and vertical center. Beam height is typically defined as the vertical dimension corresponding to a current intensity value decreasing to a certain percentage (e.g., 50%) of the peak value; vertical center is the centroid or peak position of the beam profile in the vertical direction.

[0031] After determining the beam height and vertical center, the system focuses its analysis on the region most relevant to the actual process. For example, for a 300mm (12-inch) wafer, the width of the actual process ranges approximately ±150mm. In the vertical direction, the region of interest is typically defined as a rectangular area extending vertically and vertically by a certain range (e.g., ±1 times the beam height). From the complete two-dimensional data matrix, all data points falling within this rectangular area are extracted to form a target data matrix for hotspot detection.

[0032] S4. Hotspot detection based on second derivative curvature analysis, the specific steps are as follows: S401. Data Matrix Preparation: Target data matrix M(x,y), where x,y are the matrix row and column indices (corresponding spatial coordinates), and the matrix element values ​​are the normalized beam intensity values ​​I.

[0033] S402. Curvature Calculation: Calculate the surface curvature (Curvature) of each valid data point (x,y) in the data matrix in two-dimensional space. Curvature reflects the degree of change in current intensity near that point. For discrete two-dimensional data, the curvature value C(x,y) can be approximated by calculating the second-order partial derivative of the neighborhood (e.g., a 3x3 or 5x5 window) of that point. The sign and magnitude of the curvature C characterize whether the point is in a locally convex (potentially a peak), locally concave, or gently sloping region.

[0034] S403. Candidate Hotspot Identification: Traverse the calculated curvature matrix and identify all points where the curvature value C(x,y) exceeds a preset threshold. These points correspond to locations where the flow intensity undergoes abrupt changes in both the horizontal and vertical directions, and are marked as candidate hotspots. The preset threshold can be set based on experience or statistical methods, for example, as a multiple of the average value of all curvature values.

[0035] S404. Threshold Determination and Precise Location: To avoid misjudgments caused by noise or minor fluctuations, candidate hotspots are screened using an intensity threshold. The average flow intensity μ and standard deviation σ of all data within the target data matrix are calculated. The final intensity determination threshold is set to μ + N*σ, where N is an adjustable parameter, preferably N = 3 (i.e., the average value plus 3 times the standard deviation). Only candidate hotspots whose original flow intensity value I(x,y) simultaneously satisfies I(x,y) > μ + 3σ are ultimately confirmed as effective local high-density points (“hotspots”).

[0036] To avoid misjudgment of critical states, a conditional filtering approach is proposed, and conditional filtering logic is introduced as follows: Condition A (Low flow intensity scenario): If the average flow intensity μ in the region is below a very low threshold (e.g., close to the background noise level), the overall signal is considered too weak, and hotspot detection is meaningless. In this case, the above curvature analysis can be omitted or no hotspot can be determined directly.

[0037] Condition B (Narrow Beam Scenario): If the calculated beam height is less than a preset minimum process requirement value, it indicates that the beam itself is very narrow and its natural focusing may produce a high curvature point. In this case, you can choose to relax the judgment condition or directly output a beam height abnormality alarm instead of performing hot spot filtering.

[0038] If conditions A or B are not met, the curvature and strength dual determination process of steps S2-S4 will be executed normally.

[0039] S5. Generate a detection report, specifically including the total number of detected hotspots, the precise coordinates (X, Y) of each hotspot, and the relative current intensity of each hotspot (e.g., the magnitude exceeding μ+3σ). Figure 4 As shown in (c), two local high-density points were successfully identified, with coordinates (13, 9) and (16, 9) in the two-dimensional data matrix, respectively. The precise actual coordinates (Rolldate) and severity (Strength) are shown below. Figure 4 As shown in (d). Additionally, Figure 4 (a) is the original grayscale image, (b) is the filtered image, and (c) is a schematic diagram after curvature analysis. The high-brightness areas clearly indicate the calculated hotspot locations, intuitively demonstrating the effectiveness of the algorithm.

[0040] Based on this report, operators or the host computer control system can determine the current uniformity of the ion beam. If hot spots are detected, timely intervention can be implemented to adjust relevant ion source parameters and eliminate abnormally high-density areas. This avoids inconsistent device performance or yield loss caused by localized over-implantation, enabling proactive monitoring and optimization of the ion implantation process.

[0041] This invention, through an innovative combination of algorithms and hardware, achieves quantitative and automated detection of localized high-density points ("hot spots") in the ion implantation process. This method overcomes the limitations of traditional methods that rely on manual observation of two-dimensional contour maps for qualitative judgment. It can accurately identify the number of hot spots and precisely locate their spatial distribution, thus providing a reliable basis for process monitoring. By detecting and issuing early warnings of beam uniformity anomalies in real time, equipment parameters can be adjusted promptly, effectively avoiding inconsistent device performance caused by localized over-implantation, and significantly improving the overall uniformity and product yield of the ion implantation process.

[0042] Example 2 The only difference between this embodiment and Embodiment 1 is step S4, which performs local flow intensity ratio analysis on the data matrix to screen out local high-density points. The specific process is as follows: S41. In the data matrix, identify the point with the largest current intensity value as the center point; S42. Using the center point as the core, acquire all data points in the surrounding preset neighborhood (3*3 or 4*4), which together constitute an analysis sub-region, such as... Figure 6 As shown; if the center point is located on the boundary of the data matrix, the analysis sub-region is adaptively shifted to ensure its integrity; S43. Calculate the first proportion of the current intensity value at the center point to the total current intensity value of the sub-region under analysis, and the second proportion of the average current intensity value at the other points in the sub-region under analysis to the total current intensity value of the sub-region under analysis; S44. Calculate the ratio R (Ratio) between the first ratio and the second ratio. S45. Compare the ratio R with the preset ratio threshold; if the ratio R exceeds the preset ratio threshold, the center point is identified as a local high-density point.

[0043] like Figure 7 As shown in (a), in a test case, a two-dimensional beam profile of a strip-shaped beam can be obtained by performing certain transition processing on the collected data. This can intuitively display the beam density distribution, with the redder and darker the color, the greater the beam density at that location. Figure 7 As can be seen in (b), the injection result map has a special image, and the uniformity is severely affected.

[0044] like Figure 8 As shown in (a), this is a schematic diagram of the results of the first proportion, the second proportion, and the ratio; Figure 8 In (b), the final judgment result obtained using the above example is shown, which shows the presence of "hot spots".

[0045] This invention also provides a detection apparatus for implementing the detection method for local high-density points in a ribbon ion beam as described above, comprising: A mobile Faraday device, which contains a two-dimensional array of Faraday cups; such as Figure 1 As shown, the two-dimensional Faraday cup array includes two columns of graphite cups arranged alternately in the vertical direction. Each graphite cup is independently connected to the signal acquisition channel to synchronously acquire beam signals. The two-dimensional Faraday cup array includes 24 graphite cups, arranged in two horizontally spaced columns and staggered vertically, with the center of each cup fixed in the vertical direction. The signal acquisition module is used to acquire the beam intensity signal of each cup in real time; The data processing module is used to execute the steps of the detection method for local high-density points in a strip ion beam as described above, so as to output detection results; the detection results include the number and location information of local high-density points; The output module is used to display the detection results or output control signals.

[0046] The present invention further provides a computer-readable storage medium having a computer program stored thereon, the computer program executing the steps of the method described above when run by a processor.

[0047] This invention also provides a detection system for local high-density points in a ribbon ion beam, including a memory and a processor connected to each other. The memory stores a computer program, which, when run by the processor, executes the steps of the method described above.

[0048] The apparatus, medium, and system of the present invention, corresponding to the methods described above, also have the advantages described above.

[0049] The present invention can implement all or part of the processes in the methods of the above embodiments, or it can be implemented by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium includes: any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. The memory is used to store computer programs and / or modules. The processor implements various functions by running or executing the computer programs and / or modules stored in the memory, and by calling data stored in the memory. The memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0050] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for detecting localized high-density points in a ribbon ion beam, characterized in that, Including the following steps: S1. The ribbon ion beam is scanned and measured by moving the two-dimensional Faraday cup array in the Faraday device, and the beam intensity data at each measurement point is collected; S2. Generate a two-dimensional current distribution cloud map of the ribbon ion beam based on the beam intensity data; S3. Calculate the beam height and vertical center position of the ribbon ion beam in the vertical direction based on the beam intensity data; based on the beam height and vertical center position, extract the regional data within the wafer range and between the upper and lower limits of the beam height from the two-dimensional current intensity distribution cloud map to form a data matrix; S4. Perform second-derivative curvature analysis or local flow intensity ratio analysis on the data matrix to filter out local high-density points and output the number and location information of the local high-density points.

2. The method for detecting local high-density points in a ribbon ion beam according to claim 1, characterized in that, In step S4, the specific process of performing second-order derivative curvature analysis on the data matrix to screen out locally high-density points is as follows: S401. Obtain the data matrix M(x,y), where x and y are the matrix row and column indices, and the matrix element values ​​are the normalized beam intensity values ​​I; S402. Calculate the surface curvature value C of each valid data point (x,y) in the data matrix in two-dimensional space; S403. Traverse the calculated curvature matrix and identify all points whose curvature value C exceeds a preset threshold as candidate hotspots; S404. Select candidate hotspots that exceed the preset intensity threshold as the final effective local high-density points.

3. The method for detecting local high-density points in a ribbon ion beam according to claim 2, characterized in that, In step S403, the corresponding preset intensity threshold is μ+N*σ, where μ and σ are the average flow intensity and standard deviation of all data in the data matrix, respectively; N is an adjustable parameter.

4. The method for detecting local high-density points in a ribbon ion beam according to claim 3, characterized in that, Before step S401, conditional filtering is also included: if the average current intensity μ of the region is lower than a preset low threshold, the overall signal is considered too weak and hot spot detection is meaningless. In this case, curvature analysis is not performed or no hot spot is directly determined. If the calculated beam height is less than the preset minimum process requirement value, it indicates that the beam itself is very narrow and its natural focusing will produce a high curvature point. In this case, the judgment condition is relaxed or a beam height abnormality alarm is directly output.

5. The method for detecting local high-density points in a ribbon ion beam according to claim 1, characterized in that, In step S4, the specific process of performing local flow intensity ratio analysis on the data matrix and screening out local high-density points is as follows: S41. In the data matrix, identify the point with the largest current intensity value as the center point; S42. Using the center point as the core, obtain all data points within the surrounding preset neighborhood to form an analysis sub-region; S43. Calculate the first proportion of the current intensity value at the center point to the total current intensity value of the analysis sub-region, and the second proportion of the average current intensity value at the other points in the analysis sub-region excluding the center point to the total current intensity value of the analysis sub-region. S44. Calculate the ratio R of the first ratio to the second ratio; S45. Compare the ratio R with a preset ratio threshold; if the ratio R exceeds the preset ratio threshold, then identify the center point as the local high-density point.

6. The method for detecting local high-density points in a ribbon ion beam according to claim 5, characterized in that, In step S42, if the center point is located at the boundary of the data matrix, the analysis sub-region is adaptively translated to ensure its integrity.

7. The method for detecting local high-density points in a ribbon ion beam according to any one of claims 1-6, characterized in that, In step S3, the specific process of calculating the beam height and vertical center position of the ribbon ion beam in the vertical direction based on the beam intensity data is as follows: Integrate or fit all vertical direction current intensity data at each horizontal position to obtain the vertical beam profile at that position; Analyze all vertical profiles within the entire scanning width range to calculate the beam height and vertical center of the ribbon ion beam.

8. A device for detecting localized high-density points in a ribbon ion beam, characterized in that, include: A mobile Faraday device, which contains a two-dimensional Faraday cup array; The signal acquisition module is used to acquire the beam intensity signal of each cup in real time; The data processing module is used to execute the steps of the detection method for local high-density points in a strip ion beam as described in any one of claims 1-7, so as to output detection results; the detection results include the number and location information of the local high-density points; The output module is used to display the detection results or output control signals.

9. The detection device according to claim 8, characterized in that, The two-dimensional Faraday cup array includes two columns of graphite cups arranged alternately in the vertical direction. Each graphite cup is independently connected to the signal acquisition channel to synchronously acquire beam signals. The two-dimensional Faraday cup array includes 24 graphite cups, arranged in two horizontal columns at intervals and staggered in the vertical direction. The center of each cup is fixed in the vertical direction.

10. A detection system for localized high-density points in a ribbon ion beam, comprising a memory and a processor interconnected, wherein the memory stores a computer program, characterized in that... The computer program, when run by a processor, performs the steps of the method as described in any one of claims 1-7.

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