Real-time monitoring system and method for energy of ion implantation equipment

Abstract

The invention discloses an energy real-time monitoring system and method for ion implantation equipment. The system comprises a monitoring module which is arranged at a downstream position of an accelerator resonant cavity, and the monitoring module comprises an energy feedback controller which is used for collecting an energy feedback signal of an idle ion beam output by the accelerator resonant cavity according to a preset sampling frequency and obtaining a monitoring energy parameter in the process that ion implantation equipment carries out ion implantation with preset energy; the fault detection system is used for judging whether the monitoring energy parameters conform to a preset condition threshold range or not according to the monitoring range, the sampling frequency and the preset condition threshold range; the manufacturing execution system is used for providing relevant parameters for setting a monitoring range, a sampling frequency and a threshold range of a preset condition for the fault detection system; and the control module is used for executing a process pause operation when the monitored energy parameter exceeds the threshold range of the preset condition. According to the invention, the problems of low ion implantation energy monitoring efficiency and incapability of real-time monitoring in the prior art are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of semiconductor manufacturing, and particularly relates to an ion implantation equipment energy real-time monitoring system and method. BACKGROUND

[0002] Ion implantation technology is an important process step in semiconductor device manufacturing, and by precisely controlling ion implantation energy, the target depth of the device can be accurately controlled, thereby achieving control of the electrical parameters of the device. In the ion implantation process, after the ion beam current is processed by the extraction voltage, it is accelerated by the accelerator resonant cavity to reach the wafer surface to complete the implantation process.

[0003] The commonly used ion implantation energy monitoring method in the industry is to select a target process program, adjust the beam current of the program, and then analyze it using an energy spectrum analyzer at the machine end, and confirm whether the target energy requirement is met by checking the distribution of the energy spectrum.

[0004] However, the existing energy monitoring method needs to manually adjust the beam current of a single program and perform energy spectrum analysis, and the whole process is inefficient, and real-time monitoring of the implantation energy cannot be achieved. If the energy deviates, it cannot be discovered and adjusted in time, which may cause the electrical parameters of the device to deviate and affect product quality.

[0005] Therefore, a solution to the problems of the prior art is needed. SUMMARY

[0006] The present application aims to provide an ion implantation equipment energy real-time monitoring system and method to solve the deficiencies in the prior art, thereby improving monitoring efficiency, discovering energy deviation in time, avoiding deviation of the electrical parameters of the device, and ensuring product quality.

[0007] According to a first aspect of the present application, embodiments of the present application provide an ion implantation equipment energy real-time monitoring system, comprising: a monitoring module arranged at a downstream position of an accelerator resonant cavity of an ion implantation equipment, configured to collect an energy feedback signal of an idle ion beam in an ion implantation process of a preset energy of the ion implantation equipment according to a preset sampling frequency and obtain a monitoring energy parameter; a fault detection system connected with the monitoring module, configured to set a monitoring range, the sampling frequency and a threshold range of a preset condition, and configured to receive the monitoring energy parameter of the monitoring module, and judge whether the monitoring energy parameter meets the threshold range of the preset condition according to the monitoring range, the sampling frequency and the threshold range of the preset condition; a manufacturing execution system connected with the fault detection system, configured to provide the fault detection system with related parameters for setting the monitoring range, the sampling frequency and the threshold range of the preset condition; and a control module connected with the fault detection system, configured to perform a process pause operation on the ion implantation equipment when the monitoring energy parameter exceeds the threshold range of the preset condition.

[0008] According to a second aspect of the present application, embodiments of the present application provide an ion implantation equipment energy real-time monitoring method, comprising: setting a monitoring range, a sampling frequency and a threshold range of a preset condition; collecting an energy feedback signal of an idle ion beam according to the sampling frequency; generating a monitoring energy parameter based on the energy feedback signal; and performing real-time monitoring on the monitoring energy parameter according to the sampling frequency, and triggering a process pause signal when the monitoring energy parameter exceeds the threshold range of the preset condition.

[0009] According to a third aspect of the present application, embodiments of the present application provide an ion implantation equipment energy real-time monitoring method, comprising: setting a monitoring range, a sampling frequency and a threshold range of a preset condition; collecting an energy feedback signal of an idle ion beam and an accelerator voltage signal according to the preset sampling frequency, and obtaining a plurality of monitoring parameters therefrom, and establishing a multi-parameter linear fitting model based on the plurality of monitoring parameters; determining a fitting coefficient of each monitoring parameter, and generating a monitoring energy parameter through the multi-parameter linear fitting model based on the plurality of monitoring parameters and the corresponding fitting coefficients; and performing real-time monitoring on the monitoring energy parameter, and triggering a process pause signal when the monitoring energy parameter exceeds the threshold range of the preset condition.

[0010] This application provides a real-time energy monitoring system and method for ion implantation equipment. A monitoring module acquires the energy feedback signal of the ion beam and obtains the monitored energy parameters. A fault detection system receives the monitored energy parameters and monitors them according to a set sampling frequency. The entire process eliminates the need for manual program selection and beam adjustment, shortening the monitoring cycle and reducing human intervention compared to existing manual operation methods. When the monitored energy parameters exceed a preset threshold range, a process pause signal is triggered, preventing the ion implantation process from continuing under abnormal energy conditions. Existing technologies typically require energy dispersive spectroscopy analysis after program adjustment to detect energy shifts, resulting in a detection lag. Furthermore, this application generates monitored energy parameters by establishing a linear fitting model based on multiple monitoring parameters such as feedback voltage, feedback current, and accelerator voltage, improving the accuracy of ion implantation energy state monitoring. When an abnormal energy state is detected and the process is paused, energy state recovery is achieved through energy shift analysis, calibration verification, and parameter adjustment. Once the monitored energy parameters recover to the preset threshold range, a process recovery operation is performed. This application addresses the problems of low efficiency and delayed energy shift detection in existing ion implantation energy monitoring methods, thereby reducing the risk of device electrical parameter shifts caused by energy shifts and improving the stability of semiconductor manufacturing processes. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application.

[0012] Figure 1 This is a schematic diagram of energy analysis using a traditional energy spectrometer.

[0013] Figure 2 This is a schematic diagram showing the installation location of the energy feedback controller in this application.

[0014] Figure 3 This is a schematic diagram of the structure of a real-time energy monitoring system for an ion implantation device according to one embodiment of this application.

[0015] Figure 4 This is a schematic diagram of the structure of a real-time energy monitoring system for an ion implantation device according to one embodiment of this application.

[0016] Figure 5 This is a schematic diagram of the structure of a real-time energy monitoring system for an ion implantation device according to one embodiment of this application.

[0017] Figure 6 This is a schematic diagram of the signal transmission module of this application.

[0018] Figure 7This is a schematic diagram of the monitoring module in one embodiment of this application.

[0019] Figure 8 This is a schematic diagram of the monitoring module in another embodiment of this application.

[0020] Figure 9 This is a schematic diagram of the fault detection system of this application.

[0021] Figure 10 This is a schematic diagram of the multi-parameter fitting analysis module of this application.

[0022] Figure 11 This is a flowchart illustrating the real-time energy monitoring method for ion implantation equipment in an embodiment of this application.

[0023] Figure 12 This is a flowchart illustrating steps S101 to S103 of this application.

[0024] Figure 13 This is a schematic diagram of the relationship for fitting Formula 1.

[0025] Figure 14 This is an example diagram of the 1500 keV ion implantation current monitoring parameters for this application.

[0026] Figure 15 This is a flowchart illustrating a real-time energy monitoring method for an ion implantation device according to another embodiment of this application.

[0027] Figure 16 This is a schematic diagram of the relationship for fitting Formula 2.

[0028] Figure 17 This is an example of the multi-parameter fitting and monitoring parameters for 1000 keV ion implantation in this application.

[0029] Figure 18 This is a flowchart illustrating steps S901 to S903 of this application.

[0030] Figure 19 This is a flowchart illustrating steps S9021 to S9023 of this application.

[0031] Figure 20 This is a schematic diagram of the calibration and verification results of the ion concentration depth distribution curve. Detailed Implementation

[0032] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0033] In semiconductor device manufacturing, ion implantation is a crucial process step. By precisely controlling the ion implantation energy, the target depth of the device can be accurately controlled, thereby enabling the control of device electrical parameters (such as sheet resistance Rs, threshold voltage Vt, and off-state current Ioff). During ion implantation, the ion beam is treated with an extraction voltage and then accelerated through an accelerator resonant cavity to reach the wafer surface and complete the implantation process. The precise control of ion implantation energy directly affects the electrical performance of the device and the product quality.

[0034] Existing technologies employ a monitoring process based on single-program adjustment and energy spectrum analysis. This process includes: selecting the target high-energy implantation equipment program; fine-tuning the beam of the target program; analyzing the beam using an energy spectrometer at the equipment end; and checking whether the peak value of the Gaussian distribution of the energy spectrometer reaches the target energy. However, this ion implantation energy monitoring method has the following drawbacks: First, the entire monitoring process requires manual beam adjustment for each individual program, which is inefficient and cannot meet production requirements. Second, traditional methods can only detect specific programs at specific time points, failing to monitor the ion implantation process in real time. If the ion implantation energy deviates during production, the system cannot detect and respond promptly, potentially causing a large number of wafers to complete the implantation process under abnormal energy conditions, resulting in product defects.

[0035] like Figure 1 The traditional energy spectrum analysis method shown ( Figure 1 The environment shown is as follows: minimum energy value 440.96, maximum energy value 480.63, minimum beam current value 8, maximum beam current value 119.56 (the horizontal axis represents energy value, and the vertical axis represents beam current value). This method primarily relies on the Gaussian peak value of the energy spectrum for single-parameter judgment. Such monitoring methods are easily affected by equipment fluctuations, environmental changes, and beam stability, resulting in insufficient monitoring accuracy. When multiple parameters such as the extraction voltage of the ion implantation equipment and the voltage of the accelerator resonant cavity undergo slight changes simultaneously, existing energy spectrum analysis cannot fully reflect the true energy state, potentially leading to misjudgments or missed detections. Furthermore, traditional processes rely heavily on manual operation, increasing labor costs and potentially causing inconsistent monitoring results due to different operating methods. Moreover, when energy deviations are detected, the system cannot automatically initiate calibration procedures or pause the process, requiring manual intervention for subsequent processing, resulting in long response times and the potential to miss optimal adjustment opportunities.

[0036] To address the shortcomings of the existing technology, this application proposes a real-time energy monitoring system for ion implantation equipment. By working collaboratively with a monitoring module, a fault detection system (FDC), and a manufacturing execution system (MES), the system solves the problem of real-time energy offset monitoring and improves the control capability of the ion implantation process.

[0037] See Figures 2 to 4 This application provides an embodiment of an ion implantation equipment energy real-time monitoring system 1. To better understand the monitoring system 1 of this application, the basic workflow of the ion implantation equipment will be described below: In the ion implantation equipment, the ion source 101 generates electrons through filament heating, which collide with special gases (arsine, phosphine, borane) to generate ions (As+75 / P+31 / B+11). The ions are extracted from the ion source by the extraction voltage 102, deflected by the Lorentz force of the analysis magnetic field (AlysisMagnet) 103, and then enter the accelerator resonant cavity 200 for energy acceleration. Subsequently, the beam shape is checked by the scanning system 104, the beam angle is adjusted by the magnetic field deflection system 105, and the charge is neutralized by the electron surface neutralization system (PEFsystem) 106. Finally, the ion beam reaches the wafer surface to complete the ion implantation process. Through the coordinated work of these components, it is ensured that the ion beam reaches the target position with the correct energy, shape, and angle.

[0038] Based on the above process flow, the monitoring system 1 of this application may include a monitoring module 100, a fault detection system 400, a manufacturing execution system 500, and a control module 700, such as... Figure 3 As shown.

[0039] The monitoring module 100 is located downstream of the accelerator resonant cavity 200 of the ion implantation equipment. During ion implantation at a preset energy level, it collects the energy feedback signal of the idle ion beam at a preset sampling frequency and obtains monitored energy parameters. The energy feedback signal includes at least the signal output from the accelerator resonant cavity 200. In one embodiment, the energy feedback signal includes the signal output from the accelerator resonant cavity 200. In another embodiment, the energy feedback signal includes signals output from both the accelerator resonant cavity 200 and the extract voltage controller 1100.

[0040] The monitoring module 100 includes an energy feedback controller 101, which acquires the actual energy state information of the ion beam through signal acquisition, realizes real-time data acquisition of ion implantation energy, and provides a reliable data foundation for subsequent energy monitoring.

[0041] The fault detection system 400 is connected to the energy feedback controller 101 in the monitoring module 100. It is used to set the monitoring range, the sampling frequency, and the threshold range of preset conditions, and is configured to receive the monitoring energy parameters of the monitoring module 100. The fault detection system 400 is also connected to the manufacturing execution system 500. The manufacturing execution system 500 is used to provide the fault detection system 400 with relevant parameters for setting the monitoring range, sampling frequency, and threshold range of preset conditions. That is, it can provide the fault detection system 400 with the standard process program set by the current ion implantation equipment, such as the preset implantation energy value, implantation dose, beam current, process steps, process time, etc. The fault detection system 400 sets the monitoring range, sampling frequency, and preset threshold range based on parameters provided by the manufacturing execution system 500, establishing corresponding monitoring standards (when the preset energy value of ion implantation is less than 1000 keV, the threshold range of preset conditions is set according to a ±1 keV accuracy control standard; when the preset energy value of ion implantation is greater than or equal to 1000 keV, the threshold range of preset conditions is set according to a ±2 keV accuracy control standard), and determines whether the monitored energy parameters received from the monitoring module 100 meet the preset threshold range. By comparing the monitored energy parameters with the preset threshold range in real time, the fault detection system 400 can detect abnormal states in a timely manner when energy deviation occurs. The control module 700 is connected to the fault detection system 400. When the monitored energy parameters exceed the preset threshold range, the fault detection system 400 triggers a process pause signal to the control module 700, which then executes a process pause operation. This avoids continuing the ion implantation process under abnormal energy conditions, reducing the device electrical parameter shift problem caused by energy deviation.

[0042] In one specific implementation, see [reference] Figure 9 The fault detection system 400 includes a parameter receiving unit 410, a monitoring and setting unit 430, a real-time monitoring unit 440, and a control signal output unit 450. These units constitute a fault detection and monitoring control link.

[0043] Specifically, the parameter receiving unit 410 is connected to the energy feedback controller 101 and serves as the input interface of the fault detection system 400. It is used to receive and monitor energy parameters to ensure that various monitoring data from the energy feedback controller 101 can be received stably.

[0044] The monitoring setting unit 430 is used to set the monitoring range, sampling frequency, and threshold range of preset conditions based on relevant parameters provided by the manufacturing execution system 500. Specifically, the monitoring setting unit 430 receives basic process parameters (such as the preset energy value of ion implantation) from the manufacturing execution system 500, and then establishes corresponding monitoring standards according to energy accuracy control standards: when the preset energy value of ion implantation is less than 1000 keV, the threshold range of preset conditions is set according to the ±1 keV accuracy control standard; when the preset energy value of ion implantation is greater than or equal to 1000 keV, the threshold range of preset conditions is set according to the ±2 keV accuracy control standard. Based on these standards, the monitoring setting unit 430 sets the monitoring range and the threshold range of preset conditions, wherein the monitoring range determines the working boundary of the monitoring system (i.e., the preset implantation energy range of the ion implantation equipment), and the threshold range of preset conditions is used to determine whether an abnormal state occurs. In addition, the monitoring setting unit 430 sets the sampling frequency based on the process time for a single wafer to complete ion implantation. Preferably, the sampling frequency can be 1 Hz, that is, the voltage and current signals of the ion beam are collected once per second. Since the ion implantation of a single wafer takes at least 20-30 seconds, a sampling frequency of 1Hz can obtain sufficient data during the implantation process of a single wafer, ensuring the reliability of monitoring and judgment. This sampling frequency is usually consistent with or an integer multiple of the acquisition frequency of the energy feedback controller 101.

[0045] The real-time monitoring unit 440 is connected to the parameter receiving unit 410 and the monitoring setting unit 430, and is used to monitor the energy parameters in real time according to a set sampling frequency. Furthermore, the real-time monitoring unit 440 compares the received energy parameter values ​​with a preset threshold range set by the monitoring setting unit 430. When the energy parameter exceeds the threshold range, it is determined to be an abnormal situation, thereby promptly detecting energy deviation.

[0046] The control signal output unit 450 is connected to the real-time monitoring unit 440 and the control module 700. When the monitored energy parameter exceeds the threshold range of the preset conditions, the control signal output unit 450 sends a process pause signal to the control module 700, and the control module 700 performs the corresponding process pause operation.

[0047] In one embodiment, see Figure 4 or Figure 5 As shown, the monitoring system 1 also includes a data connection system (EAP) 600, which connects the fault detection system 400 and the manufacturing execution system 500 to establish a parameter transmission channel. By using a dedicated data transmission channel, reliable data exchange between the two systems is ensured, thereby avoiding data loss or errors during data transmission.

[0048] Continue readingFigure 4 or Figure 5 As shown, the manufacturing execution system 500 is connected to the fault detection system 400 through the data connection system 600, which provides the fault detection system 400 with basic process parameter information such as the preset energy value of ion implantation, and the fault detection system 400 sets the monitoring conditions accordingly.

[0049] In one embodiment, such as Figure 4 and Figure 5 As shown, the monitoring system 1 also includes a signal transmission module 300. The signal transmission module 300 connects the accelerator resonant cavity 200, the output of the extract voltage controller 1100, and the input of the energy feedback controller 101, and is used to transmit the energy feedback signal of the ion beam. By using the signal transmission line of the signal transmission module 300, the energy feedback signal can be reliably transmitted from the acquisition end to the processing end. Furthermore, the energy feedback controller 101 and the fault monitoring system 400 are also connected via the signal transmission module 300. The signal transmission module 300 is connected to the fault detection system 400 through a communication identifier selection interface, which uses a standard communication protocol to ensure compatible communication between system modules from different equipment manufacturers.

[0050] In the above embodiments, see Figure 6 As shown, the signal transmission module 300 plays a role in signal relay and compatibility assurance in the real-time energy monitoring system of the ion implantation equipment. Between the energy feedback controller 101 and the accelerator resonant cavity 200, the signal transmission module 300 collects energy feedback signals from the accelerator resonant cavity 200 through a standard signal interface, ensuring the acquisition of real-time monitoring data without interfering with the normal operation of the accelerator. Between the energy feedback controller 101 and the fault detection system 400, the signal transmission module 300 also acts as a signal relay, ensuring stable transmission of monitoring data, thereby ensuring the compatibility of the entire monitoring system with the original system of the ion implantation equipment.

[0051] Specifically, the signal transmission module 300 includes a communication identifier selection unit 310, a signal transmission line 320, and a data channel interface 330. The communication identifier selection unit (SVID) 310 automatically selects an available data transmission channel in the ion implantation device, avoiding conflicts with the device's existing communication system by selecting an idle communication channel (i.e., an idle ion beam). The signal transmission line 320 transmits energy feedback signals. The data channel interface 330 uses a standard signal interface to ensure compatibility with connected devices and transmission reliability.

[0052] exist Figure 4 In the embodiments shown, in conjunction with reference to Figure 7As shown, the energy feedback controller 101 in the monitoring module 100 includes a signal acquisition unit 110, a power calibration unit 150, and a current detection unit 160. These units work together to acquire the energy state of the ion beam and monitor the current. In this embodiment, the input terminal of the signal acquisition unit 110 is connected to the accelerator resonant cavity 200 and is used to acquire the energy feedback signal generated after the idle ion beam passes through the accelerator resonant cavity 200 at a preset sampling frequency. The energy feedback signal includes the voltage signal and current signal corresponding to the ion beam. The signal acquisition unit 110 includes a voltage acquisition unit 111 and a current acquisition unit 112. The voltage acquisition unit 111 is used to obtain the feedback voltage from the energy feedback signal, and the obtained feedback voltage provides electrical characteristic data for energy state analysis. Taking 1500 keV ion implantation as an example, the voltage acquisition unit 111 can obtain a real-time feedback voltage such as 1501.2V. The current acquisition unit 112 is used to obtain the feedback current from the energy feedback signal. The change in the feedback current can reflect the change in ion beam intensity and energy transmission efficiency. Taking 1500keV ion implantation as an example, the current acquisition unit 112 can obtain a real-time feedback current such as 89.85mA.

[0053] The power calibration unit 150 is connected to the signal acquisition unit 110 and is used to apply a preset voltage range for calibration to confirm the system response throughout the entire operating range. The fault detection system 400 determines the preset voltage range based on the correspondence between the preset implanted ion beam energy (Linac Energy), the accelerator ion beam feedback voltage (Linac Feedback Voltage), and the feedback current (Feedback Current) shown in Table 1, combined with energy accuracy control requirements. Table 1 is based on the factory calibration data of the ion implantation equipment and includes the standard correspondence under different energy settings, as shown in the following table:

[0054] Energy value (KeV) Feedback voltage (V) Feedback current (mA) 100 100.25 5.04 150 150.36 8.08 200 180.29 11.1 300 300.35 17.15 500 500.65 29.25 1000 1000.66 59.6 1500 1500.77 89.75 2000 2000.88 119.8 2500 2500.96 150.25 3000 3001.58 180.5

[0055] The energy meter, provided by the manufacturing execution system 500, covers an energy range from 100 keV to 3000 keV, providing a reference benchmark for determining the monitoring range. Based on the technical specifications of the ion implantation equipment and actual application requirements, the monitoring system establishes a monitoring range of 100 keV to 3000 keV corresponding to the energy values ​​in Table 1. Taking 1500 keV ion implantation as an example, the fault detection system 400, according to the energy accuracy control standard (when the energy value ≥ 1000 keV, the preset voltage range applied by the power calibration unit is set to ±2 keV; when the energy value < 1000 keV, the preset voltage range applied by the power calibration unit is set to ±1 keV), sets the applied preset voltage range of the power calibration unit 150 to ±2 keV. The voltage corresponding to the preset voltage range applied by the voltage calibration unit 150 is calibrated to 1498.77V~1502.77V. During the application of the preset voltage, the current detection unit 160 simultaneously performs current detection. The fault detection system 400, by applying a calibration voltage after applying a preset voltage and using a preset fitting formula (y = 0.0605x - 1, where x represents the energy value and y represents the current value), can obtain the corresponding preset current range: when x = 1498.77 keV, y = 89.68 mA; when x = 1502.77 keV, y = 89.92 mA. In this case, the preset current range is 89.68 mA to 89.92 mA. Based on the preset fitting formula (y = 0.0605x - 1), a determination relationship between the energy value and the current value is established, as follows... Figure 13 As shown, the current parameter exhibits a good linear relationship with the ion implantation energy, with a correlation coefficient R0. 2 =1 indicates that the fitting formula has extremely high accuracy and reliability. This formula is used to calculate the theoretical current parameter values ​​under different energy settings, providing a basis for establishing the threshold range of preset conditions.

[0056] exist Figure 7 In the illustrated embodiment, the current detection unit 160 is connected to the current acquisition unit 112 and is used to detect current changes at a preset sampling frequency. That is, the current detection unit 160 collects current value changes in real time based on data from the current acquisition unit 112 at the sampling frequency. The current value provided by the current detection unit 160 is directly used as the monitored energy parameter. When the power calibration unit 150 applies a voltage to the energy feedback controller 101 according to a preset voltage, the corresponding preset current range is the threshold range of the preset conditions. If the monitored energy parameter exceeds this preset current range, the fault detection system 400 sends a process pause signal to the control module 700, and the control module 700 executes the corresponding process pause operation.

[0057] The following will further describe the applicable... Figure 4 and Figure 7The monitoring method shown in the embodiment is as follows:

[0058] See Figure 4 and Figure 7 As shown, the fault detection system 400 sets the monitoring range (i.e., the data in Table 1 above), the fitting formula 1 above, and the preset condition threshold range (i.e., the preset current range corresponding to when the voltage power supply calibration unit 150 applies voltage to the energy feedback controller 101 according to the preset voltage), and monitors through the relationship between these three: the standard reference value is determined by Table 1, the preset voltage range is applied by the power supply calibration unit 150, the preset current range is obtained by the preset fitting formula 1 (y = 0.0605x - 1, where x represents the energy value and y represents the current value), and the preset condition threshold range (upper control limit UCL, target value Target, lower control limit LCL) is established and monitored and judged accordingly. Taking 1500 keV ion implantation as an example, after the fault detection system 400 receives the basic process parameters provided by the manufacturing execution system 500, the monitoring and setting unit 430 determines the preset voltage range to be applied based on the data in Table 1 (1500 keV corresponds to a voltage value of 1500.77V) and the energy accuracy control standard (when the energy value is ≥1000 keV, the preset voltage range applied by the power supply calibration unit is set to ±2 keV, and when the energy value is <1000 keV, the preset voltage range applied by the power supply calibration unit is set to ±1 keV). After applying the preset voltage range, the voltage is calibrated to 1498.77V~1502.77V, and the corresponding preset current range is obtained through the preset fitting formula 1 (y=0.0605x-1): when x=1498.77 keV, y=89.68mA; when x=1502.77 keV, y=89.92mA. Based on these results, the monitoring setting unit 430 sets the lower control limit LCL to 89.68mA, the target value Target to 89.75mA, and the upper control limit UCL to 89.92mA. When the current detection unit 160 detects a current value of 89.85mA, the fault detection system 400 compares it with the preset current range (89.68mA~89.92mA) and determines it to be in a normal state; if the detected current value exceeds the preset current range, it is determined to be in an abnormal state, thereby realizing the control of the monitored energy parameters.

[0059] In another embodiment of this application, see [reference] Figure 5 and Figure 8 As shown, the energy feedback controller 101 in the monitoring module 100 includes a signal acquisition unit 110, an accelerator voltage acquisition unit 140, a power calibration unit 150, a current detection unit 160, and a voltage detection unit 170. Compared to Figure 7 The embodiment shown, Figure 8The embodiment shown adds an accelerator voltage acquisition unit 140 and a voltage detection unit 170 to achieve higher precision multi-parameter monitoring.

[0060] exist Figure 8 In the illustrated embodiment, the signal acquisition unit 110 also includes a voltage acquisition unit 111 and a current acquisition unit 112. It should be noted that in this embodiment, the voltage and current signals (i.e., energy feedback signals) acquired by the voltage acquisition unit 111 and the current acquisition unit 112 originate from the energy feedback signal of the ion beam output by the extraction voltage controller 1100, and are related to... Figure 7 The signal acquisition from the accelerator resonant cavity 200 differs in the illustrated embodiment. Specifically, the voltage acquisition unit 111 is used to acquire feedback voltage data from the ion beam voltage response of the extraction voltage controller 1100, and the current acquisition unit 112 is used to acquire feedback current data from the ion beam current response of the extraction voltage controller 1100.

[0061] It is worth noting that, in Figure 8 In the illustrated embodiment, the accelerator voltage acquisition unit 140 is connected to the accelerator resonant cavity 200 and is used to acquire the accelerator resonant cavity voltage signal according to a preset sampling frequency. This accelerator resonant cavity voltage signal can reflect the acceleration degree and energy level of the ion beam. Taking 1000 keV ion implantation as an example, the accelerator voltage acquisition unit 140 can obtain an accelerator voltage such as 1000.2V. The voltage detection unit 170 is connected to the voltage acquisition unit 111 and is used to detect voltage change values ​​according to a preset sampling frequency. That is, the voltage detection unit 170 provides real-time voltage detection information based on the data from the voltage acquisition unit 111. Taking 1000 keV ion implantation as an example, the voltage detection unit 170 can detect a voltage change value such as 1000.5V, and the current detection unit 160 can detect a current change value such as 59.8mA.

[0062] In this embodiment, refer to Figure 5 and Figure 8 As shown, the monitoring module 100 also includes a multi-parameter fitting analysis module 800. The multi-parameter fitting analysis module 800 is connected between the energy feedback controller 101 and the fault detection system 400 to achieve higher-precision energy monitoring. In this embodiment, as... Figure 10 As shown, the multi-parameter fitting analysis module 800 may include a fitting model unit 810, a correlation analysis unit 820, and a monitoring energy parameter generation unit 830.

[0063] Specifically, the fitting model unit 810 is used to receive multiple monitoring parameters. Among these parameters, the feedback voltage and feedback current are obtained from the signal acquisition unit 110 of the energy feedback controller 101, specifically from the energy feedback signal of the ion beam output by the extract voltage controller 1100. The accelerator voltage is obtained from the accelerator resonant cavity voltage signal of the accelerator resonant cavity 200 acquired by the accelerator voltage acquisition unit 140. A multi-parameter linear fitting model is established based on these feedback voltages, feedback currents, and accelerator voltages. This fitting model uses a preset fitting formula three: Yef = K1*FEY V + K2*FEYCurrent + K3*Linac V, where FEY V represents the feedback voltage of the extract voltage controller, FEY Current represents the feedback current of the extract voltage controller, Linac V represents the accelerator voltage (i.e., the abbreviation for accelerator resonant cavity voltage, hereinafter the same), and K1 / K2 / K3 are fitting coefficients. By establishing a model, the relationship between multiple monitoring parameters and ion implantation energy is characterized. Compared with the existing single-parameter monitoring method, this application can more comprehensively reflect the actual state of ion implantation energy.

[0064] The correlation analysis unit 820 is connected to the fitting model unit 810 to determine the fitting coefficient values ​​corresponding to each monitoring parameter. Specifically, based on the energy composition principle of the ion implantation device, these three monitoring parameters (feedback voltage of the extraction voltage controller, feedback current of the extraction voltage controller, and accelerator voltage) need to be strongly correlated with the total energy state. Therefore, the correlation analysis unit 820 needs to determine each monitoring parameter and its corresponding fitting coefficient values, and use a multi-parameter linear fitting model (i.e., the preset fitting formula three: Yef=K1*FEY V+K2*FEY Current+K3*Linac V) to obtain the monitoring energy parameters that are strongly correlated with the total energy state. Each fitting coefficient needs to meet the minimum coefficient requirements: the fitting coefficient K1 of the feedback voltage is greater than or equal to 0.6, the fitting coefficient K2 of the feedback current is greater than or equal to 0.5, and the fitting coefficient K3 of the accelerator voltage is greater than or equal to 0.4. In practical applications, for example, the fitting coefficient K1 of the feedback voltage is 0.6, the fitting coefficient K2 of the feedback current is 0.5, and the fitting coefficient K3 of the accelerator voltage is 0.4.

[0065] The monitoring energy parameter generation unit 830 is connected to the correlation analysis unit 820 and the fault detection system 400, respectively. It generates monitoring energy parameters based on multiple monitoring parameters and corresponding fitting coefficients using a multi-parameter linear fitting model, and provides these parameters to the fault detection system 400 for real-time monitoring. The monitoring energy parameter generation unit 830 integrates multiple monitoring parameters (feedback voltage, feedback current, and accelerator voltage) into a single monitoring energy parameter, which characterizes the current value. By using a preset fitting formula (Formula 3) and combining it with a dimensionless coefficient of 0.1, a unified monitoring energy parameter is generated, simplifying the monitoring operation and improving monitoring accuracy. The monitoring energy parameter generation unit 830 also establishes a judgment relationship between energy and current values ​​based on a preset fitting formula (Formula 2) (y = 0.1001x - 0.5667, where x represents the energy value and y represents the current value). The judgment relationship between energy and current values ​​is established based on the preset fitting formula (Formula 2) (y = 0.1001x - 0.5667). Figure 16 As shown, the current parameter exhibits a good linear relationship with the ion implantation energy, with a correlation coefficient R0. 2 =1 indicates that the fitting formula has extremely high accuracy and reliability. This formula is used to calculate the theoretical current parameter values ​​under different energy settings, providing a basis for establishing the threshold range of preset conditions.

[0066] The following will further describe the applicable... Figure 5 and Figure 8 The monitoring method shown in the embodiment is as follows:

[0067] See Figure 5 and Figure 8As shown, the fault detection system 400 sets the monitoring range (i.e., the data in Table 1 above), the multi-parameter linear fitting model, and the preset condition threshold range (calculated using the energy accuracy control standard and the fitting formula 2 above). It monitors according to the multi-parameter linear fitting model and the preset fitting formula: First, a multi-parameter linear fitting model is established (i.e., the preset fitting formula 3: Yef=K1*FEY V+K2*FEY Current+K3*Linac V), each monitoring parameter and its corresponding fitting coefficient value are determined, and the monitoring energy parameters that are strongly correlated with the total energy state are obtained using the multi-parameter linear fitting model. Then, the judgment rules are established through fitting formula 2 to perform monitoring and judgment. Specifically, the multi-parameter fitting analysis module 800 uses the preset fitting formula three (Yef=K1*FEY V+K2*FEY Current+K3*Linac V, where FEY V represents the feedback voltage of the extractor voltage controller, FEY Current represents the feedback current of the extractor voltage controller, Linac V represents the voltage of the accelerator, K1 / K2 / K3 are fitting coefficients, and Yef is a monitoring energy parameter used to characterize the current value) to establish a multi-parameter linear fitting model. The correlation analysis unit 820 determines each monitoring parameter and its corresponding fitting coefficient value, and uses the multi-parameter linear fitting model (i.e., the preset fitting formula three: Yef=K1*FEY V+K2*FEY Current+K3*Linac V) combined with a dimensionless coefficient of 0.1 (since the numerical magnitudes of the feedback voltage, feedback current, and accelerator voltage have a 1:10 correspondence with the energy value, it is necessary to use the coefficient 0.1 for dimensionless calculation) to obtain the monitoring energy parameters that are strongly correlated with the total energy state. Taking 1000 keV ion implantation as an example, the real-time data collected during the monitoring process, such as feedback voltage 1000.5V, feedback current 59.8mA, and accelerator voltage 1000.2V, are used to calculate the following using fitting formula three: Yef = 0.6 * 1000.5 + 0.5 * 59.8 + 0.4 * 1000.2 = 1030.38. Combined with the dimensionless coefficient of 0.1, the monitoring energy parameter is obtained as: 1030.38 * 0.1 = 103.04. Based on the preset fitting formula two (y = 0.1001x - 0.5667), a judgment rule is established. When x = 1000 keV, the theoretical monitoring energy parameter should be: 0.1001 * 1000 - 0.5667 = 99.533. Based on the energy precision control standard (±2 keV), the preset condition threshold range is calculated as follows: when x = 998 keV, the lower limit is 0.1001*998 - 0.5667 = 99.333; when x = 1002 keV, the upper limit is 0.1001*1002 - 0.5667 = 99.733. Therefore, the preset condition threshold range is 99.333 to 99.733.The calculated monitoring energy parameter (=103.04) is compared with the preset condition threshold range (99.333~99.733). If the monitoring energy parameter exceeds the upper control limit (=99.733), it is determined to be an abnormal state. In other words, after receiving the basic process parameters provided by the manufacturing execution system 500, the monitoring setting unit 430 of the fault detection system 400 sets the corresponding preset condition threshold range (e.g., lower control limit LCL=99.333, target value Target=99.533, upper control limit UCL=99.733). When the monitoring energy parameter is within the preset condition threshold range, it is determined to be a normal state; when the monitoring energy parameter exceeds the preset condition threshold range, it is determined to be an abnormal state, thereby realizing the control of the monitoring energy parameter.

[0068] In one embodiment, see Figure 5 As shown, the monitoring system 1 also includes a calibration control module 900. The calibration control module 900 is connected to the control module 700 and is used to perform energy calibration recovery operations. When the control module 700 performs a process pause operation on the ion implantation equipment, the control module 700 starts the calibration control module 900 to perform the calibration process.

[0069] Specifically, the calibration control module 900 may include the following units: an energy shift analysis unit, a calibration verification unit, and a parameter adjustment unit. The energy shift analysis unit is used to analyze the possible sources of energy shift. Specifically, the energy shift analysis unit uses monitoring data from the fault detection system 400 and a preset energy shift judgment rule to determine whether the energy shift source is due to an abnormal extraction voltage or an abnormal accelerator resonant cavity. The calibration verification unit is used to control the ion implantation equipment to perform actual testing and verification. Specifically, by providing a test wafer and performing actual ion implantation, a secondary ion mass spectrometer (SIMS) is used to obtain the ion concentration depth distribution curve, and the peak position is compared with the target depth to determine the actual energy shift. The parameter adjustment unit is used to adjust parameters based on the calibration verification results. Specifically, according to the calibration verification results, the extraction voltage or accelerator resonant cavity voltage is adjusted using the total energy calculation formula E. total =V EXT +∑(i=1to 12)V Linac_i (V EXT For the pull-out voltage, V Linac_i The calibration control module 900 performs parameter compensation (for the accelerator voltage) until calibration verification is successful. This calibration control module 900 uses a cyclic verification method to ensure the energy state returns to the normal range. When the fault detection system 400 detects that the monitored energy parameters have returned to the preset threshold range, it triggers a process recovery signal to the control module 700, which then performs the process recovery operation.

[0070] To achieve the aforementioned ion implantation energy monitoring function with real-time monitoring and automatic control, this application also provides a method for real-time energy monitoring of an ion implantation device. This monitoring method is based on... Figure 4 and Figure 7 This is implemented using the ion implantation equipment real-time energy monitoring system in the illustrated embodiment. The specific structure and function of the ion implantation equipment real-time energy monitoring system can be found above. Figure 4 and Figure 7 The descriptions in the illustrated embodiments will not be repeated here.

[0071] See Figure 11 As shown, the monitoring method may include the following steps:

[0072] Step S100: Set the monitoring range, sampling frequency, and preset threshold range. (See also...) Figure 12 As shown, step S100 may include steps S101 to S103.

[0073] Step S101: The fault detection system 400 sets the monitoring range based on the preset energy table (Linac Energy Table) provided by the manufacturing execution system 500. This energy table contains the correspondence between the preset implanted ion beam energy (Linac Energy), the accelerator ion beam feedback voltage (Linac Feedback Voltage), and the feedback current (Feedback Current), as shown in Table 1 above. This energy table is established based on the factory calibration data and equipment factory calibration data of the ion implantation equipment, and includes standard correspondences under different energy settings. The energy values ​​in Table 1 are derived from the standard process program of the ion implantation equipment, and the corresponding voltage and current values ​​are obtained through precision measuring instruments during equipment calibration.

[0074] Step S102: The manufacturing execution system 500 provides the fault detection system 400 with basic process parameters such as the preset energy value for ion implantation. The monitoring and setting unit 430 in the fault detection system 400 sets the threshold range of the preset conditions based on these parameters. The monitoring and setting unit 430 establishes corresponding monitoring standards based on the energy control range and energy accuracy control standards. When the preset energy value for ion implantation is less than 1000 keV, the threshold range of the preset conditions is set according to the ±1 keV accuracy control standard; when the preset energy value for ion implantation is greater than or equal to 1000 keV, the threshold range of the preset conditions is set according to the ±2 keV accuracy control standard.

[0075] Specifically, the threshold range of the preset conditions is set as follows: The fault detection system 400 determines the corresponding standard current value in Table 1 based on the preset energy value (monitoring range) of ion implantation. According to the energy accuracy control standard, the power calibration unit 150 applies a preset voltage (±2 keV for ≥1000 keV, ±1 keV for <1000 keV) to obtain the calibration voltage range. Using the calibration voltage and fitting formula 1 (y = 0.0605x - 1, where x represents the energy value and y represents the current value), the corresponding preset current range can be obtained. This preset current range is the threshold range of the preset conditions. Based on the preset fitting formula 1 (y = 0.0605x - 1), a determination relationship between the energy value and the current value is established, such as... Figure 13 As shown, the current parameter exhibits a good linear relationship with the ion implantation energy, with a correlation coefficient R0. 2 =1 indicates that the fitting formula has extremely high accuracy and reliability. This formula is used to calculate the theoretical current parameter values ​​under different energy settings, providing a basis for establishing the threshold range of the preset conditions. For example, for a 1500keV ion implantation setting, the standard current value is 89.75mA. According to the energy accuracy control standard (±2keV for ≥1000keV), the corresponding calibration voltage range is 1498.77V~1502.77V. After voltage calibration, the current range obtained is 89.68mA~89.92mA, which is the threshold range of the preset conditions.

[0076] Step S103: The energy feedback controller 101 in the monitoring module 100 sets the sampling frequency. The signal acquisition unit 110 in the energy feedback controller 101 sets the sampling frequency to 1Hz, that is, it acquires the voltage and current signals of the ion beam once per second. The monitoring setting unit 430 in the fault detection system 400 sets the frequency for receiving and processing the monitored energy parameters, which is usually consistent with the acquisition frequency of the energy feedback controller 101. It should be noted that since the ion implantation of a wafer takes at least 20 to 30 seconds, a sampling frequency of 1Hz can obtain sufficient data during the implantation process of a single wafer, ensuring the reliability of monitoring and judgment.

[0077] Step S200: Acquire the energy feedback signal of the idle ion beam according to the sampling frequency. In one embodiment, step S200 may include: acquiring the energy feedback signal of the idle ion beam through the signal acquisition unit 110 of the energy feedback controller 101 in the monitoring module 100 according to a set sampling frequency (e.g., 1Hz). The input terminal of the signal acquisition unit 110 is connected to the accelerator resonant cavity 200, and acquires the energy feedback signal in real time during the energy acceleration process of the ion beam passing through the accelerator resonant cavity 200, as well as the voltage and current signals corresponding to the energy feedback signal of the idle ion beam in the accelerator resonant cavity 200. Among them, the voltage acquisition unit 111 is used to acquire feedback voltage data, and the current acquisition unit 112 is used to acquire feedback current data. Further, the energy feedback controller 101 transmits the acquired voltage and current signals to the fault detection system 400 through the signal transmission module 300.

[0078] Step S300: Generate monitoring energy parameters based on the energy feedback signal. In one embodiment, step S300 may include: after receiving the voltage signal and current signal, the fault detection system 400 determines the corresponding energy value based on Table 1 above. According to the corresponding energy accuracy control standard, the fault detection system 400 sets the power supply calibration unit 150 to apply pressure according to the adopted energy accuracy control standard: when the energy value corresponding to the received voltage signal and current signal is less than 1000 keV, the voltage range corresponding to the ±1 keV accuracy standard is used for pressure application, and the current range corresponding to the calibrated voltage range after pressure application is the threshold range of the preset condition. The subsequent step S400 uses the threshold range of the preset condition as the judgment range of the monitoring energy parameters; when the energy value corresponding to the received voltage signal and current signal is greater than or equal to 1000 keV, the voltage range corresponding to the ±2 keV accuracy standard is used for pressure application, and the current range corresponding to the calibrated voltage range after pressure application is the threshold range of the preset condition. The subsequent step S400 uses the threshold range of the preset condition as the judgment range of the monitoring energy parameters. The current detection unit 160 acquires the current value after the power calibration unit 150 applies the corresponding voltage. This current value is a monitoring energy parameter, and the detected monitoring energy parameter is transmitted to the fault detection system 400.

[0079] Step S400: Monitor the energy parameters in real time according to the sampling frequency. When the monitored energy parameters exceed a preset threshold range, trigger a process pause signal. In one embodiment, step S400 may include:

[0080] (1) The real-time monitoring unit 440 in the fault detection system 400 monitors the energy parameters in real time according to the set sampling frequency. The energy parameters monitored here refer to the current value in step 300.

[0081] The real-time monitoring unit 440 is connected to the monitoring setting unit 430 and is used to compare the monitored energy parameters with the aforementioned preset condition threshold range. When the monitored energy parameters are within the threshold range of their corresponding preset conditions, it indicates that the ion implantation energy state is normal; when the monitored energy parameters exceed the threshold range of their corresponding preset conditions, it is determined that the energy state is abnormal.

[0082] Taking 1500 keV ion implantation as an example (corresponding to the standard current value of 89.75 mA in Table 1), according to the preset condition threshold range (89.68 mA~89.92 mA) set in step S102, a preset condition threshold range is established in the fault detection system 400, where the lower control limit (LCL) is set to 89.68 mA, the upper control limit (UCL) is set to 89.92 mA, and the target value is set to the standard current value of 89.75 mA. Figure 14 As shown. This threshold range provides a criterion for judging the monitored energy parameters: when the monitored energy parameter is between LCL and UCL, it indicates that the energy status is normal; when the monitored energy parameter exceeds UCL or falls below LCL, the monitoring system will trigger a process pause procedure to ensure that the ion implantation process is carried out under stable energy conditions.

[0083] (2) When the real-time monitoring unit 440 in the fault detection system 400 detects that the monitored energy parameter exceeds the preset condition threshold range, the control signal output unit 450 in the fault detection system 400 sends a process pause signal to the control module 700, and the control module 700 performs the corresponding process pause operation accordingly.

[0084] By implementing steps S100 to S400 above, energy anomaly detection and control response based on current monitoring are achieved, thereby avoiding the continuation of the ion implantation process under abnormal energy conditions and preventing device electrical parameter shifts caused by energy deviations. This monitoring method is characterized by its simplicity and fast response, making it suitable for applications with relatively low monitoring accuracy requirements.

[0085] Another embodiment of this application also provides a method for real-time monitoring of the energy of an ion implantation device. This monitoring method is based on... Figure 5 and Figure 8 This is implemented using the ion implantation equipment real-time energy monitoring system in the illustrated embodiment. The specific structure and function of the ion implantation equipment real-time energy monitoring system can be found in the above text. Figure 5 and Figure 8 The descriptions in the illustrated embodiments will not be repeated here.

[0086] See Figure 15 As shown, in this other embodiment, the monitoring method may include the following steps:

[0087] Step S500: Set the monitoring range, sampling frequency, and threshold range for preset conditions. Step S500 may include:

[0088] (1) Setting the monitoring range. Based on the preset energy table (see Table 1 above for description), the monitoring range of the feedback voltage and feedback current is determined. The acquisition of the monitoring range is the same as the real-time energy monitoring method of the ion implantation device in the previous embodiment, and will not be repeated here. The accelerator voltage, as the third monitoring parameter, can be directly acquired from the accelerator resonant cavity 200 through the accelerator voltage acquisition unit 140, and its monitoring range is determined according to the specifications of the ion implantation device.

[0089] (2) The manufacturing execution system 500 provides the fault detection system 400 with basic process parameters such as the preset energy value for ion implantation. The monitoring and setting unit 430 in the fault detection system 400 sets the threshold range of the preset conditions based on these parameters and the preset fitting formula 2. A determination relationship between the energy value and the current value is established based on the preset fitting formula 2 (y = 0.1001x - 0.5667), such as... Figure 16 As shown, the current parameter exhibits a good linear relationship with the ion implantation energy, with a correlation coefficient R0. 2 =1 indicates that the fitting formula has extremely high accuracy and reliability. This formula is used to calculate the theoretical current parameter values ​​under different energy settings, providing a basis for establishing the threshold range of the preset conditions. The monitoring setting unit 430 establishes corresponding monitoring standards based on the energy control range and energy accuracy control standards. When the preset energy value of ion implantation is less than 1000 keV, the threshold range of the preset conditions is set according to the ±1 keV accuracy control standard; when the preset energy value of ion implantation is greater than or equal to 1000 keV, the threshold range of the preset conditions is set according to the ±2 keV accuracy control standard, that is, the threshold range of the monitored energy parameters. (See reference...) Figure 17 As shown, the specific method for determining the threshold range is as follows: Taking 1000 keV ion implantation as an example, based on the preset fitting formula two (y = 0.1001x - 0.5667), when x = 1000 keV, the theoretical monitoring energy parameter should be: 0.1001 * 1000 - 0.5667 = 99.533. According to the energy accuracy control standard (±2 keV for ≥1000 keV, ±1 keV for <1000 keV), the preset threshold range is calculated as follows: when x = 998 keV, the lower limit is 0.1001 * 998 - 0.5667 = 99.333; when x = 1002 keV, the upper limit is 0.1001 * 1002 - 0.5667 = 99.733. The fault detection system 400 establishes threshold ranges for the lower control limit (LCL = 99.333), target value (Target = 99.533), and upper control limit (UCL = 99.733) to provide judgment criteria for real-time monitoring of energy parameters.

[0090] (3) The sampling frequency of the monitoring module 100 is set to 1Hz to ensure the synchronous acquisition of multiple parameter data.

[0091] Step S600: Acquire the energy feedback signal of the idle ion beam and the voltage signal of the accelerator according to a preset sampling frequency, and obtain multiple monitoring parameters from them. Establish a multi-parameter linear fitting model based on these monitoring parameters. Specifically, step S600 may include:

[0092] (1) The signal acquisition unit 110 in the energy feedback controller 101 acquires the energy feedback signal of the idle ion beam. It should be noted that in this embodiment, the voltage and current signals (i.e., energy feedback signals) acquired by the voltage acquisition unit 111 and the current acquisition unit 112 originate from the energy feedback signal of the ion beam output by the extraction voltage controller. The voltage acquisition unit 111 and the current acquisition unit 112 in the signal acquisition unit 110 obtain the feedback voltage and feedback current from the energy feedback signal, respectively, while the accelerator voltage acquisition unit 140 directly acquires the accelerator voltage from the accelerator resonant cavity 200. By acquiring these parameters, more comprehensive energy state information can be obtained, thereby improving the accuracy of energy state assessment.

[0093] (2) The fitting model unit 810 in the multi-parameter fitting analysis module 800 receives multiple monitoring parameters from the energy feedback controller 101. The feedback voltage data is acquired by the voltage acquisition unit 111 and detected by the voltage detection unit 170; the feedback current data is acquired by the current acquisition unit 112 and detected by the current detection unit 160; and the accelerator voltage data is directly acquired by the accelerator voltage acquisition unit 140. A multi-parameter linear fitting model is established based on the feedback voltage of the extractive voltage controller, the feedback current of the extractive voltage controller, and the accelerator voltage. This fitting model uses a preset fitting formula: Yef = K1*FEY V + K2*FEY Current + K3*Linac V, to characterize the relationship between multiple monitoring parameters and the ion implantation energy state, where Yef is the monitoring energy parameter and is used to characterize the current value. It should be noted that FEY V represents the feedback voltage of the extractive voltage controller, FEY Current represents the feedback current of the extractive voltage controller, and Linac V represents the accelerator voltage.

[0094] Step S700: Determine the fitting coefficients of each monitoring parameter, and based on the multiple monitoring parameters and their corresponding fitting coefficients, generate monitoring energy parameters using the multi-parameter linear fitting model. Step S700 includes:

[0095] (1) The correlation analysis unit 820 determines the fitting coefficient values ​​corresponding to each monitoring parameter (feedback voltage, feedback current, and accelerator voltage). Specifically, based on the energy composition principle of the ion implantation device, these three monitoring parameters need to be strongly correlated with the total energy state in order to obtain monitoring energy parameters that are strongly correlated with the total energy state. Therefore, to ensure the accuracy of the multi-parameter linear fitting model using the preset fitting formula three (Yef=K1*FEY V+K2*FEY Current+K3*Linac V), each fitting coefficient needs to meet the minimum coefficient requirements: the fitting coefficient K1 of the feedback voltage is greater than or equal to 0.6, the fitting coefficient K2 of the feedback current is greater than or equal to 0.5, and the fitting coefficient K3 of the accelerator voltage is greater than or equal to 0.4. These minimum coefficient requirements ensure that each monitoring parameter has sufficient weight in the fitting model, avoids parameter failure due to excessively small coefficients, and thus ensures the reliability of the monitoring energy parameter calculation. In practical applications, for example, the fitting coefficient K1 of the feedback voltage is 0.6, the fitting coefficient K2 of the feedback current is 0.5, and the fitting coefficient K3 of the accelerator voltage is 0.4.

[0096] (2) The monitoring energy parameter generation unit 830 integrates multiple monitoring parameters (feedback voltage, feedback current, and accelerator voltage) into a single monitoring energy parameter, which is used to characterize the current value. Specifically, based on multiple monitoring parameters and corresponding fitting coefficients, the monitoring energy parameter is generated by using a preset fitting formula three (i.e., a multi-parameter linear fitting model: Yef = K1*FEY V + K2*FEY Current + K3*Linac V) and a dimensionless coefficient of 0.1 (since the numerical magnitudes of the feedback voltage, feedback current, and accelerator voltage have a 1:10 correspondence with the energy value, dimensionless coefficient 0.1 is required). Taking 1000keV ion implantation as an example, the data collected in real time during the monitoring process, such as feedback voltage 1000.5V, feedback current 59.8mA, and accelerator voltage 1000.2V, are used to calculate the following using the preset fitting coefficients K1 = 0.6, K2 = 0.5, and K3 = 0.4: Yef = 0.6 * 1000.5 + 0.5 * 59.8 + 0.4 * 1000.2 = 1030.38. Then, by combining the dimensionless coefficient of 0.1, the monitored energy parameter is obtained: 1030.38 * 0.1 = 103.04.

[0097] Step S800: Monitor the energy parameters in real time. When the monitored energy parameters exceed a preset threshold range, trigger a process pause signal. Step S800 may include:

[0098] (1) The real-time monitoring unit 440 monitors the energy parameters in real time based on the set monitoring range, sampling frequency and preset threshold range. The monitoring energy parameters are used as a unified monitoring object, which improves the monitoring efficiency.

[0099] (2) When the monitored energy parameter exceeds the preset threshold range, the control signal output unit 450 in the fault detection system 400 sends a process pause signal to the control module 700, which then pauses the process operation of the ion implantation equipment. For example, when the monitored energy parameter is between LCL and UCL, it indicates that the ion implantation energy status is normal; when the monitored energy parameter exceeds the preset threshold range, the control signal output unit 450 triggers the process pause signal. Taking the monitored energy parameter (=103.04) calculated in the aforementioned step S700 as an example, this value exceeds the upper control limit (=99.733) of the preset threshold range (99.333~99.733), and is determined to be an abnormal energy status, so the control signal output unit 450 triggers the process pause signal.

[0100] Step S900: When the monitored energy parameter exceeds the preset threshold range, the following steps are also included: pausing process operation and then initiating a calibration procedure; performing energy offset analysis; performing calibration verification and parameter adjustment based on the analysis results until calibration verification is successful; when the monitored energy parameter is detected to have returned to the preset threshold range, triggering a process recovery signal and resuming process operation. Specifically, as follows... Figure 18 As shown, step S900 may further include steps S901 to S903.

[0101] Step S901: When the monitored energy parameter is detected to exceed the preset threshold range and a process pause signal is triggered, the control module 700 starts the calibration control module 900 to execute the calibration procedure.

[0102] Step S902: The calibration control module 900 performs energy offset analysis, calibration verification, and parameter adjustment sequentially based on the calibration procedure. This process adopts a cyclic verification method, analyzing the possible sources of energy offset, determining the energy offset through actual testing, and adjusting parameters until the calibration verification is successful.

[0103] In one embodiment, see Figure 19 As shown, step S902 may include steps S9021 to S9023.

[0104] Step S9021: The calibration control module 900 performs energy deviation analysis. The calibration control module 900 obtains feedback voltage and feedback current detection data from the voltage detection unit 170 and the current detection unit 160, as well as accelerator voltage acquisition data from the accelerator voltage acquisition unit 140, through the fault detection system 400, and judges the possible sources of energy deviation based on the preset energy deviation judgment rules. Specifically, the judgment rules include the following two: (1) Accelerator deviation judgment rule: According to the preset fitting formula two (y=0.1001x-0.5667), when the target energy is greater than or equal to 1000KeV, taking 1000KeV ion implantation as an example, the theoretical monitoring energy parameter y is 99.533. If the detected energy deviation is below 998KeV, the energy deviation Δx is greater than 2KeV. According to the above fitting formula, the corresponding monitoring energy parameter deviation Δy is greater than 0.2, and the absorption is observed through the machine. The output voltage is maintained at the set value of 80 KeV without deviation. In this case, it can be preliminarily judged that there is an abnormality in the accelerator resonant cavity. When the target energy is less than 1000 KeV, if the energy deviation Δx is greater than 1 KeV, the corresponding monitoring energy parameter deviation Δy is greater than 0.1. The output voltage is maintained at the set value of 80 KeV without deviation as observed by the machine. In this case, it can be preliminarily judged that there is an abnormality in the accelerator resonant cavity. (2) Extraction voltage deviation judgment rule: The judgment is made by using the power supply calibration of the extraction voltage. See Table 2 below:

[0105]

[0106] When the relative deviation between the set value of the extraction voltage and the actual feedback voltage is less than 0.5%, the extraction voltage is considered to be working normally. When the relative deviation is greater than or equal to 0.5%, it is preliminarily determined that the extraction voltage deviation is abnormal. Taking an 80keV setting as an example, if the actual feedback voltage is 80.05V and the relative deviation is 0.06%, which is less than 0.5%, the extraction voltage is considered to be in a normal state.

[0107] Step S9022: The calibration control module 900 performs actual calibration verification. Specifically, the calibration control module 900 controls the ion implantation equipment to provide a wafer test piece and selects the process program to be calibrated for actual ion implantation. Then, a secondary ion mass spectrometer (SIMS) is used for testing to obtain the ion concentration depth distribution curve. For example... Figure 20 As shown in the figure, the horizontal axis represents depth, indicating the depth of ion implantation into the wafer (unit: nm), and the vertical axis represents concentration, indicating the concentration distribution of ions at different depths (unit: atoms / cm). 3The figure shows an example of verification results during the calibration process, where the two curves (B#20 and B#22) represent the detection results of two test wafers, respectively. The calibration control module 900 determines the actual energy offset by comparing the peak position of the curves with the target depth.

[0108] Step S9023: The calibration control module 900 adjusts parameters or completes calibration based on the verification results. If the SIMS test in step S9022 shows that the peak position of the ion concentration depth distribution curve reaches the target depth, the calibration verification is passed and the calibration procedure is completed. If the SIMS test shows that the peak position of the ion concentration depth distribution curve deviates from the target depth, the calibration control module 900 converts the depth deviation into the corresponding energy deviation, and combines it with the analysis results of step S9021 above, using the total energy calculation formula E. total =V EXT +∑(i=1to 12)V Linac_i (V EXT For the pull-out voltage, V Linac_i Adjust parameters for the accelerator voltage: If the analysis results indicate an abnormal extraction voltage, adjust the extraction voltage V. EXT If the analysis results indicate that the accelerator resonant cavity is malfunctioning, then adjust the corresponding accelerator resonant cavity voltage V. Linac_i After the parameters are adjusted, return to step S9022 to re-perform SIMS verification until the peak position of the ion concentration depth distribution curve reaches the target depth.

[0109] Step S903: The calibration control module 900 completes the calibration and resumes process operation. After the calibration verification is completed in step S902, the calibration control module 900 confirms through the fault detection system 400 that the monitored energy parameters have returned to the preset threshold range, and then notifies the control module 700 to perform the process recovery operation and resume process operation. Through this calibration recovery process, the accuracy of energy calibration is ensured, downtime caused by energy deviation is reduced, thereby improving the operating efficiency of the equipment.

[0110] By implementing steps S500 to S900, the problems of low efficiency, poor real-time performance, and delayed energy shift detection in existing ion implantation energy monitoring methods are solved, thereby reducing the risk of device electrical parameter deviations caused by energy shifts and improving the stability of semiconductor manufacturing processes. This monitoring method features high monitoring accuracy and strong anti-interference capability, making it suitable for applications requiring high monitoring accuracy.

[0111] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The features, structures, or characteristics described above can be combined in any suitable manner in one or more embodiments. Those skilled in the art, guided by the teachings of the above embodiments, can combine various implementation methods in the above embodiments to obtain technical solutions with multiple implementation methods. The above descriptions are merely preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A real-time energy monitoring system for an ion implantation device, characterized in that, include: The monitoring module is located downstream of the accelerator resonant cavity of the ion implantation device. It is used to collect the energy feedback signal of the idle ion beam and obtain the monitoring energy parameters according to the preset sampling frequency during the ion implantation process of the ion implantation device with preset energy. A fault detection system, connected to the monitoring module, is used to set the monitoring range, the sampling frequency, and the threshold range of preset conditions, and is configured to receive the monitoring energy parameters of the monitoring module, and determine whether the monitoring energy parameters meet the threshold range of the preset conditions based on the monitoring range, the sampling frequency, and the threshold range of the preset conditions. A manufacturing execution system, connected to the fault detection system, is used to provide the fault detection system with relevant parameters for setting the monitoring range, the sampling frequency, and the threshold range of the preset conditions; A control module, connected to the fault detection system, is used to execute a process pause operation on the ion implantation device when the monitored energy parameter exceeds the threshold range of the preset conditions.

2. The system as described in claim 1, characterized in that, The monitoring module includes an energy feedback controller, which specifically includes: The signal acquisition unit is used to acquire the energy feedback signal of the idle ion beam according to the sampling frequency. The signal acquisition unit includes a voltage acquisition unit and a current acquisition unit. The energy feedback signal includes the voltage signal and the current signal corresponding to the ion beam. A power calibration unit, connected to the signal acquisition unit, is used to apply a preset voltage range; A current detection unit is connected to the current acquisition unit and is used to acquire current change values ​​according to the sampling frequency.

3. The system as described in claim 2, characterized in that, The energy feedback controller also includes: A voltage detection unit, connected to the voltage acquisition unit, is used to acquire voltage change values ​​according to the sampling frequency; An accelerator voltage acquisition unit is connected to the accelerator resonant cavity and is used to acquire accelerator voltage signals according to the preset sampling frequency.

4. The system as described in claim 3, characterized in that, The monitoring module also includes a multi-parameter fitting analysis module, wherein: The multi-parameter fitting analysis module includes: The fitting model unit is used to receive multiple monitoring parameters and establish a multi-parameter linear fitting model. A correlation analysis unit, connected to the fitting model unit, is used to determine the fitting coefficients of each of the monitoring parameters; A monitoring energy parameter generation unit is connected to the correlation analysis unit and the fault detection system, respectively, and is used to generate the monitoring energy parameters and provide them to the fault detection system for real-time monitoring.

5. The system as described in claim 4, characterized in that, The monitoring system also includes a calibration control module, which is connected to the control module and is used to perform energy calibration recovery operations. When the control module performs a process pause operation on the ion implantation equipment, the control module starts the calibration control module to perform the calibration process.

6. A method for real-time monitoring of the energy of an ion implantation device, characterized in that, include: Set the monitoring range, sampling frequency, and threshold range for preset conditions; The energy feedback signal of the idle ion beam is acquired according to the sampling frequency described above; Based on the energy feedback signal, monitoring energy parameters are generated; The monitoring energy parameter is monitored in real time according to the sampling frequency. When the monitoring energy parameter exceeds the threshold range of the preset conditions, a process pause signal is triggered.

7. A method for real-time monitoring of the energy of an ion implantation device, characterized in that, include: Set the monitoring range, sampling frequency, and threshold range for preset conditions; According to the preset sampling frequency, the energy feedback signal of the idle ion beam and the accelerator voltage signal are collected, and multiple monitoring parameters are obtained from them. A multi-parameter linear fitting model is established based on the multiple monitoring parameters. Determine the fitting coefficients for each monitoring parameter, and generate monitoring energy parameters based on the multiple monitoring parameters and their corresponding fitting coefficients using the multi-parameter linear fitting model; The monitored energy parameters are monitored in real time, and when the monitored energy parameters exceed the threshold range of preset conditions, a process pause signal is triggered.

8. The method as described in claim 7, characterized in that, The multi-parameter linear fitting model is: Yef = K1*FEY V + K2*FEY Current + K3*Linac V, where FEY V represents the feedback voltage of the extract voltage controller, FEY Current represents the feedback current of the extract voltage controller, Linac V represents the voltage of the accelerator, K1 represents the fitting coefficient of the feedback voltage of the extract voltage controller, K2 represents the fitting coefficient of the feedback current of the extract voltage controller, and K3 represents the fitting coefficient of the voltage of the accelerator.

9. The method as described in claim 8, characterized in that, Determining the fitting coefficients for each monitoring parameter includes: determining the fitting coefficient values ​​for each monitoring parameter as follows: K1 is greater than or equal to 0.6, K2 is greater than or equal to 0.5, and K3 is greater than or equal to 0.

4.

10. The method as described in claim 9, characterized in that, When the monitored energy parameter exceeds the threshold range of the preset conditions, the following steps are also included: Start the calibration procedure after pausing the process. Perform energy shift analysis; Based on the analysis results, perform calibration verification and parameter adjustment until the calibration verification is successful; When the monitored energy parameter is detected to have recovered to the threshold range of the preset conditions, a process recovery signal is triggered and the process operation is resumed.

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