Diagnostic device, semiconductor device manufacturing system, and diagnostic method

Abstract

To provide a technique for detecting an abnormality in a surface condition of a film of an electrostatic chuck.SOLUTION: In a diagnostic device that diagnoses the condition of a semiconductor manufacturing device having a sample stage on which a sample electrostatically adsorbed to a film is placed, temperature data is acquired before and after a change in energy input to the sample, and an abnormality in the film is detected on the basis of the acquired temperature data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a diagnostic device, a semiconductor manufacturing device system, a semiconductor device manufacturing system, and a diagnostic method, and more particularly to a diagnostic device (PHM: Prognostics and Health Management) that uses time-series signals (sensor waveform data) sequentially acquired from multiple sensors in a plasma processing device, which is a semiconductor manufacturing device that processes semiconductor wafers. [Background technology]

[0002] The surface condition of the electrostatic chuck (ESC), which mounts and attracts the wafer during plasma processing, gradually deteriorates due to surface damage, deposit adhesion, etc. This can cause abnormalities in the wafer processing speed and wafer attraction, so it is desirable to have technology that can detect changes in the ESC surface condition and perform maintenance before an abnormality occurs. However, due to the lack of related sensors, it is difficult to monitor the surface condition of the ESC in real time on operating equipment. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-226407 Summary of the Invention [Problem to be solved by the invention]

[0004] An abnormality in the surface condition of the ESC is detected by a change in the thermal conductivity of the ESC surface. For general equipment, a method has been proposed in which changes in thermal conductivity are detected by changes in temperature sensor data, such as the method described in Patent Document 1. However, in the ESC of an etching equipment, the temperature control system keeps the temperature sensor value constant, so this method cannot detect changes in the thermal conductivity of the ESC surface.

[0005] Therefore, an object of the present disclosure is to provide a technique for detecting an abnormality in the surface condition of a film of an electrostatic chuck. [Means for solving the problem]

[0006] A brief summary of representative aspects of this disclosure is as follows.

[0007] According to one embodiment, in a diagnostic device that diagnoses the condition of a semiconductor manufacturing device that has a sample stage on which a sample electrostatically adsorbed to a film is placed, temperature data before and after a change in energy input to the sample is acquired, and an abnormality in the film is detected based on the acquired temperature data.

[0008] In addition, the diagnostic device capable of predicting abnormalities disclosed herein changes the energy input to the wafer using a plasma control unit, acquires temperature change data before and after the energy change from a temperature sensor using a data collection unit, calculates the amount of change or the rate of change in the temperature change data as a feature value using a feature calculation unit, and determines that the surface condition of the electrostatic chuck is abnormal if the feature value exceeds a threshold value using an abnormality detection unit. [Effects of the Invention]

[0009] It is possible to improve the accuracy of detecting abnormalities in the surface condition of the electrostatic chuck. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a diagram illustrating an example of the configuration of a fault diagnosis device according to a first embodiment. [Figure 2] 2 is a diagram showing an example of an electrode configuration of the etching apparatus of FIG. 1. [Figure 3] FIG. 4 is a diagram illustrating an example of sensor data according to the first embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of a processing flow of feature amount calculation and abnormality determination according to the first embodiment. [Figure 5] FIG. 10 is a diagram illustrating an example of calculation of feature amounts F1, F2, and F3. [Figure 6] FIG. 10 is a diagram illustrating an example of calculation of a feature amount F4. [Figure 7] FIG. 4 is a diagram illustrating an example of abnormality determination according to the first embodiment. [Figure 8] FIG. 10 is a diagram illustrating an example of a wafer chucking operation according to the second embodiment. [Figure 9] FIG. 10 is a diagram showing an example of a display of a diagnosis result according to the first embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] An embodiment of the present invention is a diagnostic device for a plasma processing apparatus. As an example of the embodiment, the diagnostic device may be a general personal computer equipped with a processor and a memory, and may be implemented as software that processes according to a program, or may be implemented as dedicated hardware rather than a general computer.

[0012] In addition, dedicated hardware may be incorporated into the computer, and software and hardware implementation may be combined. The diagnostic device may be connected externally, or may be connected externally as a module that is also used for other data processing. Hereinafter, embodiments will be described with reference to the drawings. [Example]

[0013] 1 includes a fault diagnosis equipment (FDE, sometimes simply referred to as a diagnosis equipment) 100 and an etching equipment (PEE) 200. The fault diagnosis equipment 100 and the etching equipment 200 are connected via a network line NW. In this example, the etching equipment 200 is a plasma processing equipment serving as a semiconductor manufacturing device.

[0014] Fault diagnosis system (FDE) 100 has a data collection unit (DCD) 101, a feature calculation unit (FCP) 102, and an anomaly detection unit (ADD) 103, and is connected to an etching system 200 via a network NW. The etching system 200 includes a plasma control unit (PCD) 201 and a chamber (CHA) 202, which are related to the present invention. Fault diagnosis system 100 receives time-series data (hereinafter referred to as sensor data) 204 measured by a sensor during a processing process from the etching system 200 via the network NW, analyzes the received sensor data 204, and outputs an analysis result RS.

[0015] A plasma control unit 201 controls the energy input to a wafer 203 serving as a sample in a chamber 202. In the chamber 202, the wafer 203 is processed under set process conditions, and sensor data 204 from this process is transmitted in real time to a data collection unit 101. The data collection unit 101 extracts energy and temperature sensor data from the received sensor data 204 and transmits the extracted data to a feature calculation unit 102. The feature calculation unit 102 acquires temperature change data before and after the energy change from the sensor data 204, and calculates the amount of change in the temperature change data or the rate of change in the temperature change data as a feature. Anomaly detection unit 103 analyzes the changes over time in the calculated feature values ​​and outputs an analysis result RS indicating whether an abnormality exists.

[0016] FIG. 2 shows an example of the configuration of the chamber 202. Inside the chamber 202, a sample stage is installed, including an electrostatic chuck (ESC) 205 that mounts and electrostatically attracts a wafer 203 during plasma processing. The wafer 203 is placed on the sample stage, electrostatically attracted to a film 210 that constitutes the ESC 205. When processing the wafer 203, the temperature of the ESC 205 is controlled according to process settings, the wafer 203 is moved above the ESC 205, and plasma PLA is generated in the space above the wafer 203. The surface condition of the film 210 on the ESC 205 is related to the thermal conductivity THC between the wafer 203 and the ESC 205. Therefore, the objective of the present invention is to detect abnormalities in the surface condition of the film 210 on the ESC 205 by monitoring changes in this thermal conductivity THC.

[0017] The temperature of the ESC 205 is controlled by a feedback temperature control system using multiple heaters 206 and temperature sensors 207. The feedback temperature control system controls the heater power by decreasing it when the temperature of the temperature sensor 207 is higher than the temperature of the set condition, and by increasing it when the temperature of the temperature sensor 207 is lower than the temperature of the set condition. Therefore, the sensor value (detected temperature value) of the temperature sensor 207 remains almost constant during the process. When there is a change in the heat source, etc., the sensor value of the temperature sensor 207 changes temporarily, but the temperature control by the feedback temperature control system operates, and the temperature of the temperature sensor 207 returns to the temperature of the set condition.

[0018] Temperature change data can be obtained from the above phenomenon, and the change in thermal conductivity THC can be estimated using that temperature change data. For example, changing the power of the plasma PLA changes the amount of energy (plasma heat input) 209 input from the plasma PLA to the wafer 203, causing the sensor value of the temperature sensor 207 to temporarily deviate from the set condition value and return to its original value. The temperature change data from this process can be used to calculate the temperature change rate, and if the rate is faster than usual, it can be determined that the thermal conductivity THC has increased.

[0019] FIG. 3 shows an example of the sensor data. FIG. 3(a) is an example of sensor data showing the change over time in the plasma power (Plasma Power) of the plasma PLA, with the vertical axis representing plasma power and the horizontal axis representing time (TT). FIG. 3(b) is an example of sensor data showing the change over time in the temperature sensor value (Sensor Temperature 01) of the temperature sensor 207, with the vertical axis representing the temperature sensor value and the horizontal axis representing time (TT). FIG. 3(c) is a table showing an example of sensor data collected at 0.1 second intervals. The timestamps are at 0.1 second intervals, and in this example, the sensor data shown exemplarily includes the power of the plasma PLA (Plasma Power), the temperature sensor value (Sensor Temperature 01), and the power of the heater 206 (Heater Power 01).

[0020] As shown in Figure 3(a), the plasma power of the plasma PLA is set to decrease and then increase. As shown in Figure 3(b), the temperature sensor value (Sensor Temperature 01) decreases and then increases as the plasma power of the plasma PLA decreases. As the plasma power increases, the temperature sensor value of the temperature sensor 207 increases and then decreases. Sensor data is collected at 0.1 second intervals and is saved and transmitted as shown in the table in Figure 3(c).

[0021] The processing flow of feature calculation will be explained with reference to Fig. 4. Fig. 4 is a diagram showing an example of the processing flow of feature calculation and abnormality determination according to the embodiment. The processing flow in Fig. 4 is a processing flow executed by an application in a semiconductor device manufacturing system including a platform on which an application for diagnosing the state of semiconductor manufacturing equipment is implemented.

[0022] Step S40: First, in a semiconductor manufacturing apparatus 200 equipped with a sample stage on which a sample (wafer) 203 electrostatically attracted to a film 210 of an ESC 205 is placed, the plasma power is controlled to change the energy input to the wafer 203. Here, the plasma power change portion in the original process conditions can be used, but a process condition dedicated to fault diagnosis may also be added to the original process conditions.

[0023] Step S41: Then, sensor data (T) before and after the energy change (before and after the energy change) performed in step S40 is collected. For example, sensor data (T) is collected for a time range of 20 seconds, from 5 seconds before the energy change to 20 seconds after the energy change. That is, in the diagnostic device 100 that diagnoses the state of the semiconductor manufacturing equipment 200 including the sample stage on which the sample 203 electrostatically adsorbed to the film 210 of the ESC 205 is placed, sensor data (hereinafter also referred to as temperature data) T before and after the change in energy input to the sample 203 is acquired. Then, an abnormality in the film 210 of the ESC 205 is detected by the diagnostic device 100 based on the acquired temperature data T.

[0024] Step S42: From here, the feature F1 is calculated using data T. Data T1 before the energy change is extracted. For example, the first 10 pieces of data in data T are taken as data (T1). Data T2 after the energy change is extracted. For example, the last 10 pieces of data in data T are taken as data (T2). Then, the average values ​​(MEAN(T1) and MEAN(T2)) are calculated for the data before the energy change (T1) and the data after the energy change (T2), respectively.

[0025] Step S43: Then, the feature amount F1 is calculated using Equation 1.

[0026] F1=MEAN(T1)-MEAN(T2) Equation 1 The difference between the T1 average value and the T2 average value (feature amount F1) is calculated using Equation 1. In other words, the difference between the average value of the temperature data (T1) before the energy change and the average value of the temperature data (T2) after the energy change is found as feature amount F1.

[0027] Step S44: Next, the maximum value (TMAX) and minimum value (TMIN) of the data T are obtained.

[0028] Step S45: Then, the feature amount F2 is calculated using Equation 2.

[0029] F2=TMAX-TMIN Equation 2 The difference (feature amount F2) between the maximum value and the minimum value of the temperature data T is calculated using Equation 2. In other words, the difference between the maximum value and the minimum value of the temperature data T is obtained as the feature amount F2. Step S46: Next, the time (L1) of the maximum value (TMAX) of the data T and the time (L2) of the minimum value (TMIN) of the data T are obtained.

[0030] Step S47: The slope of the data T with respect to time between time L1 and time L2 is calculated as feature F3. That is, the slope with respect to time (L1, L2) is obtained as feature F3 using the data between the maximum value (TMAX) of the temperature data T and the minimum value (TMIN) of the temperature data T.

[0031] Step S48: Before this process, normal waveform data of data T is prepared. This normal waveform data is past data T extracted from sensor data of past normal processing processes under the same calculation conditions. Feature value F4 is calculated using Equation 3.

[0032] F4 = MEAN (difference between data T at each time and normal waveform data) Equation 3 The difference (feature value F4) between the temperature data T and the predefined normal waveform data is calculated using Equation 3. In other words, the difference between the normal waveform data of the predefined normal temperature data and the waveform data of the temperature data T is obtained as feature value F4.

[0033] Step S49: The calculation of the feature quantities F1, F2, F3, and F4 is completed through the above calculations. The time-dependent changes in the feature quantities (F1, F2, F3, and F4) are monitored, and if a specified threshold is exceeded, an abnormality is determined. During the calculation, a general statistical processing method may be added to the feature quantity calculation method for purposes such as noise reduction. Furthermore, if the pattern of plasma power change allows for multiple maximum and minimum values ​​instead of the maximum and minimum values ​​described above, the number of feature quantities may be increased.

[0034] Figure 5 shows examples of feature quantities F1, F2, and F3. The plasma power changed twice, and data T is data from the period TP before and after the energy change, from 5 seconds before the time TF of the first energy change to 20 seconds after the time TE of the final energy change. The first 10 pieces of data T are used to calculate the T1 average (MEAN(T1)), and the last 10 pieces of data are used to calculate the T2 average (MEAN(T2)), and feature quantity F1 can then be calculated. Feature quantities F2 and F3 can then be calculated using the maximum value L1 and minimum value L2 of data T and data T between maximum value L1 and minimum value L2.

[0035] Figure 6 shows an example of the feature value F4. Data T(61) is acquired in the same way as in the example of Figure 5. Then, the feature value F4, which is the difference between normal waveform data 60 and data T(61), can be calculated.

[0036] FIG. 7 shows an example of anomaly determination. FIG. 7(a) shows an example of monitoring the change over time of feature F1, where the vertical axis shows the value of feature F1 and the horizontal axis shows the cumulative time CT of the etching process (or the number of processed wafers N): CT (or N). FIG. 7(b) shows an example of monitoring the change over time of feature F2, where the vertical axis shows the value of feature F2 and the horizontal axis shows the cumulative time CT of the etching process (or the number of processed wafers N). FIG. 7(c) shows an example of monitoring the change over time of feature F3, where the vertical axis shows the value of feature F3 and the horizontal axis shows the cumulative time CT of the etching process (or the number of processed wafers N). FIG. 7(d) shows an example of monitoring the change over time of feature F4, where the vertical axis shows the value of feature F4 and the horizontal axis shows the cumulative time CT of the etching process (or the number of processed wafers N).

[0037] As shown in FIG. 7, an anomaly is determined by analyzing the time series of the feature values. For example, there are two thresholds, TH1 and TH2, for feature value F3. If the value of feature value F3 exceeds either of the thresholds TH1 and TH2, an anomaly is determined for feature value F3. That is, if feature value F3 exceeds the range between the thresholds TH1 and TH2, an anomaly is determined for feature value F3. (In other words, if feature value F3 falls outside the range between TH1 and TH2 (i.e., if F3 > TH1 or TH2 > F3), an anomaly is determined for feature value F3.) Feature value F4 has a single threshold TH3, and if feature value F4 exceeds this threshold TH3, an anomaly is determined for feature value F4. (In other words, if F4 > TH3, an anomaly is determined for feature value F4.) If any of feature values ​​F1, F2, F3, or F4 becomes abnormal overall, an anomaly is determined to have occurred in the equipment. However, considering the correlation between feature values ​​and failures, an anomaly may be determined to have occurred in the etching equipment 200 when two or more feature values ​​are abnormal.

[0038] Also, some ESC205s have multiple zones. (e) of FIG. 7 shows an ESC205 with four zones (first zone Z1, second zone Z2, third zone Z3, and fourth zone Z4). The process flow for calculating the feature quantities (F1-F4) and determining anomalies in FIG. 4 shows, for example, the process flow for calculating the feature quantities (F1-F4) and determining anomalies in the first zone Z1 of the ESC205. Using the process flow for calculating the feature quantities (F1-F4) and determining anomalies in FIG. 4 for each of the zones Z1, Z2, Z3, and Z4, the feature quantities (F1-F4) for each zone Z1, Z2, Z3, and Z4 can be calculated and anomalies can be determined.

[0039] In other words, the diagnostic method for diagnosing the condition of a semiconductor manufacturing apparatus 200 having a sample stage on which a sample 203 electrostatically adsorbed to a film 210 is placed is configured to include a step of acquiring temperature data before and after a change in energy input to the sample 203, and a step of detecting an abnormality in the film 210 based on the acquired temperature data.

[0040] 1 can be rephrased as a semiconductor device manufacturing system. Here, the semiconductor device manufacturing system includes a platform on which a semiconductor manufacturing equipment 200 is connected via a network NW and an application for diagnosing the state of the semiconductor manufacturing equipment 200 is implemented, the semiconductor manufacturing equipment 200 having a sample stage on which a sample 203 electrostatically attracted to a film 210 is placed. The system is configured so that the application executes a step of acquiring temperature data before and after a change in energy input to the sample 203, and a step of detecting an abnormality in the film 210 based on the acquired temperature data.

[0041] The list of feature quantities, calculation results, abnormality diagnosis results, etc. can be displayed using a GUI (Graphic User Interface). For example, the diagnostic device 100 has a display screen that displays the list of feature quantities, calculation results, abnormality diagnosis results, etc. using a GUI (Graphic User Interface). Alternatively, in a case where the analysis results RS output by the diagnostic device 100 are transmitted to a server via a network line, the server may be provided with a display screen that displays the list of feature quantities, calculation results, abnormality diagnosis results, etc. using a GUI (Graphic User Interface).

[0042] An example of a GUI screen is shown in Figure 9. The GUI screen 90 in Figure 9 depicts an example of an ESC Fault Diagnostic screen. On the GUI screen 90, the user can select the equipment data (temperature data T) of the semiconductor manufacturing equipment 200 to be diagnosed using the device ID 91, start time 92, and end time 93 of the semiconductor manufacturing equipment 200. The feature list 94 allows the user to set the features (Features: F1, F2, F3, F4), zones (Zone 1 = Z1, 2 = Z2, 3 = Z3, 4 = Z4), parameter values ​​(Para), and threshold values ​​(TH) used for diagnosis. The Anomaly Judgment area 95 displays the time-dependent changes in each calculated feature (F1-F4). If an abnormality is determined, the feature with an abnormality (F4 in this example) is displayed in the Alarm area 96. Action 97 presents work such as performing maintenance or adjusting process conditions as a countermeasure to the abnormality. That is, the feature amounts (F1, F2, F3, F4) which are the amount of change in the temperature data or the rate of change in the temperature data, the change over time of the feature amounts (F1, F2, F3, F4), or the result of whether or not there is an abnormality in the film 210 are displayed on the GUI screen 90, and if there is an abnormality in the film 210, the GUI screen 90 presents actions to be taken when there is an abnormality in the film 210.

[0043] According to the first embodiment, it is possible to provide a technique for detecting an abnormality in the surface state of the film 210 of the electrostatic chuck 205. This improves the accuracy of detecting an abnormality in the surface state of the film 210 of the electrostatic chuck 205. [Example]

[0044] In the second embodiment, a process will be described in which a wafer chuck 80 (where a wafer 203 is placed on an ESC 205) is used instead of plasma heat input. Parts that are not explained are the same as in the first embodiment. In other words, duplicate explanations of parts that are the same as in the first embodiment will be omitted.

[0045] 8(a) and 8(b) show the situation of Example 1 (same as FIG. 3(a) and 3(b)), while FIG. 8(c) and 8(d) show an example in which wafer chuck 80 is used. FIG. 8(c) shows the change between the on state (On) and the off state (Off) of wafer chuck 80, with the vertical axis representing the on state (On) and the off state (Off) of wafer chuck 80 and the horizontal axis representing time TT. FIG. 8(d) shows the state of heater power value (data P), which is the amount of power consumed by heater 206, with the vertical axis representing the state of heater power value (data P) and the horizontal axis representing time TT.

[0046] In the second embodiment, the temperature sensor value (data T) used in the feature calculation in the first embodiment is changed to a heater power value (data P) that is the amount of power consumed by the heater.

[0047] That is, before the wafer chuck 80, the temperature sensor value and heater power value are kept constant by the temperature control. When the wafer chuck 80 is in the process of being chucked, the temperature of the wafer 203 is lower than that of the ESC 205, so the temperature of the ESC 205 drops. The temperature control system detects the temperature change of the ESC 205 and increases the heater power of the heater 206. When the temperature of the wafer 203 becomes the same as that of the ESC 205, the heater power value of the heater 206 gradually returns to its original value.

[0048] Using the data P from the above process, the feature amount can be calculated in the same way as in the first embodiment, and an abnormality can be determined.

[0049] That is, in Example 2, instead of temperature data before and after a change in the energy input to the sample 203, the amount of power consumed by the heater 206 is acquired, and an abnormality in the film 210 is detected based on the acquired data on the change in power consumption of the heater 206.

[0050] As a modified example, instead of the temperature data before and after the change in energy input to the sample 203, temperature data of the ESC 205 before and after the electrostatic adsorption of the sample 203 may be acquired, and an abnormality in the film 203 may be detected based on the acquired temperature data of the ESC 205 before and after the electrostatic adsorption of the sample 203.

[0051] In the second embodiment and the modified example, the same effects as those in the first embodiment can be obtained.

[0052] The invention made by the inventor has been specifically described above based on examples, but it goes without saying that the present invention is not limited to the above-described embodiments and examples, and various modifications are possible. [Explanation of symbols]

[0053] 10: Semiconductor manufacturing equipment system 100: Fault diagnosis device (diagnostic device) 101: Data collection department 102: Feature calculation unit 103: Anomaly detection unit 200: Etching equipment (semiconductor manufacturing equipment) 201: Plasma control unit 202: Chamber 203: Sample (wafer) 205: Electrostatic chuck (ESC) 206: Heater 207: Temperature sensor

Claims

1. 1. A diagnostic apparatus for diagnosing the state of a semiconductor manufacturing apparatus having a sample stage on which a sample electrostatically adsorbed to a film is placed, temperature data before and after electrostatic adsorption of the sample is acquired; A diagnostic device characterized in that an abnormality in the film is detected based on the acquired temperature data.

2. 2. The diagnostic device according to claim 1, A diagnostic device characterized in that a difference between an average value of the temperature data before electrostatic chucking of the sample and an average value of the temperature data after electrostatic chucking of the sample is obtained as a feature amount.

3. 2. The diagnostic device according to claim 1, The diagnostic device is characterized in that the difference between the maximum value of the temperature data and the minimum value of the temperature data is obtained as a feature amount.

4. 2. The diagnostic device according to claim 1, A diagnostic device characterized in that a slope with respect to time is determined as a feature quantity using data between a maximum value of the temperature data and a minimum value of the temperature data.

5. 2. The diagnostic device according to claim 1, A diagnostic device characterized in that a difference between the temperature data in a predefined normal state and the temperature data is obtained as a feature amount.

6. 2. The diagnostic device according to claim 1, A diagnostic device characterized in that the characteristic amount, which is the amount of change in the temperature data or the rate of change in the temperature data, the change in the characteristic amount over time, or the presence or absence of an abnormality in the membrane are displayed on a GUI screen, and actions to be taken if the membrane is abnormal are presented.

7. A semiconductor device manufacturing system including a platform on which an application for diagnosing the state of a semiconductor manufacturing device including a sample stage on which a sample electrostatically adsorbed to a film is placed, acquiring temperature data before and after electrostatic adsorption of the sample; and a step of detecting an abnormality in the film based on the acquired temperature data, the step being executed by the application.

8. 1. A diagnostic method for diagnosing a state of a semiconductor manufacturing apparatus having a sample stage on which a sample electrostatically adsorbed to a film is placed, comprising: acquiring temperature data before and after electrostatic adsorption of the sample; and detecting an abnormality in the film based on the acquired temperature data.

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