Arc detection method and arc detection system

Abstract

Disclosed are arc detection methods and arc detection systems. Disclosed are methods of detecting an arc generated in a semiconductor device. The method can include performing a process recipe for a substrate process and collecting data according to the process recipe; separating the collected data by setting sections; obtaining a mean value and a standard deviation of the data separated for each section; and setting an upper limit and a lower limit for detecting the arc using the mean value and the standard deviation.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to an arc detection method and an arc detection system. More particularly, the present disclosure relates to a method and system capable of detecting an arc using an average value and a standard deviation. BACKGROUND

[0002] Generally, a semiconductor device is manufactured by forming various films on a semiconductor substrate (e.g., a silicon substrate) and patterning the films. The manufacturing of the semiconductor device includes various unit processes such as chemical vapor deposition, sputtering, photolithography, etching, ion implantation, chemical mechanical polishing (CMP), cleaning, etc.

[0003] Among the above-mentioned unit processes, the etching process is mainly performed by dry etching, and the dry etching removes a predetermined portion of a film on a substrate via a chemical reaction with the film using plasma generated from a reaction gas in a process chamber. The substrate to be processed in the process chamber is fixedly supported by a substrate support. The substrate support includes, for example, an electrostatic chuck (ESC) for adsorbing and supporting a substrate by electrostatic force, or a vacuum chuck for adsorbing and supporting a substrate by vacuum pressure.

[0004] A polymer unnecessarily attached to a back surface of a substrate loaded in a process chamber can not be completely removed during loading / unloading of the substrate and fall into the process chamber, thereby generating an arc by reacting with plasma. In addition, an arc can also be generated for other reasons.

[0005] Previously, in order to detect an arc, a box was used or a method of detecting an arc by simply tracking a slope was used.

[0006] Figures 1-2 is a view for explaining an arc detection method according to a conventional method.

[0007] REFERENCE Figures 1-2 A problem of the related art is that it is difficult to detect an arc using an absolute value of a parameter or an absolute slope value due to a change in a noise level and a change in an average intensity during a process.

[0008] That is, the problem of the conventional arc detection method is that it is difficult to easily distinguish between an arc signal and a noise signal, and the conventional arc detection method has low data quality due to detection by only an absolute value or a slope. In the case of using a conventional high-resolution arc detector, even if noise is small, a time interval is small (a few ns ~ a few μs), but when an arc is detected by a tilt angle, it is also difficult to distinguish between an arc and noise because the arc signal itself is divided into several points. In order to overcome this problem, when a high-resolution detector is installed, a large amount of noise is generated. Although development and commercialization of a high-resolution arc detector have been made, unfortunately, the detection algorithm has failed to keep up with advanced detection methods. For example, the algorithm still employs a conventional method of measuring only a slope parameter or using a box algorithm.

[0009] Another problem of the known technology is that precision is low when detecting an arc using an absolute value and an absolute slope value of a parameter because the parameter is sensitively reacted according to a type of a film deposited on a substrate and a process recipe. SUMMARY

[0010] The present disclosure aims to provide an algorithm that can easily detect an arc even at a high resolution.

[0011] The problems to be solved by the present invention are not limited to the above-mentioned problems. Through the following description, those of ordinary skill in the art will clearly understand other technical problems not mentioned.

[0012] An exemplary embodiment of the present disclosure provides a method of detecting an arc generated in a semiconductor device.

[0013] A method of detecting an arc generated in a semiconductor device can include performing a process recipe for a substrate process and collecting data according to the process recipe, separating the collected data by setting sections, obtaining an average value and a standard deviation of the data separated for each section, and setting an upper limit and a lower limit for detecting an arc using the average value and the standard deviation.

[0014] In an embodiment, separating the collected data by setting sections can be separating the collected data except for data immediately after the start of a process and just before the end of the process.

[0015] In an embodiment, setting the upper limit and the lower limit for arc detection using the average value and the standard deviation can set a value obtained by adding n times a standard deviation (i.e., an arbitrary number of standard deviations) to the average value as the upper limit, and can set a value obtained by subtracting n times a standard deviation (i.e., an arbitrary number of standard deviations) from the average value as the lower limit, where n is an integer.

[0016] In an embodiment, separating the collected data by setting sections can include separating the collected data by setting sections based on the number of data.

[0017] In an embodiment, when the detected signal exceeds the upper limit and the lower limit, it is determined that an arc occurs.

[0018] In an embodiment, setting the upper limit and the lower limit for arc detection using the average value and the standard deviation can set the upper limit and the lower limit differently for each parameter or recipe in a process.

[0019] In an embodiment, a computer-readable recording medium in which a program for performing an arc detection method is recorded can be disclosed.

[0020] Another exemplary embodiment of the disclosure provides a system capable of detecting an arc generated in a semiconductor device.

[0021] The system can include a data collection unit for collecting results of processing a substrate for processing a substrate in a semiconductor device, and an arc detection unit for detecting an arc using an average value and a standard deviation based on each section of data collected by the data collection unit.

[0022] In an embodiment, the arc detection unit can include a data separation unit for separating data collected by the data collection unit for each section, a calculation unit for calculating an average value and a standard deviation of data separated for each section in the data separation unit, a reference setting unit for setting an upper limit and a lower limit for arc detection by applying values calculated by the calculation unit, and a determination unit for determining that an arc occurs when an arc signal exceeds a reference value set by the reference setting unit.

[0023] In an embodiment, the data separation unit can separate the collected data except for data immediately after a process starts and just before a process ends.

[0024] In an embodiment, the reference setting unit can set the upper limit by adding n times the standard deviation to the average value, and can set the lower limit by subtracting n times the standard deviation from the average value, where n is an integer.

[0025] In an embodiment, the reference setting unit can set the upper limit and the lower limit differently for each parameter or recipe in a process.

[0026] Another exemplary embodiment of the disclosure provides an arc detection system including a processor and a memory storing program codes executable by the processor.

[0027] The processor can perform: performing a processing recipe for substrate processing and collecting data according to the processing recipe; separating the collected data by setting sections; obtaining an average value and a standard deviation of the data separated for each section; and setting an upper limit and a lower limit for detecting an arc using the average value and the standard deviation.

[0028] The method and apparatus according to the present disclosure can easily detect an arc compared to a conventional method.

[0029] Effects of the present disclosure are not limited to the aforementioned effects. Effects not mentioned herein will be clearly understood by persons skilled in the art from the present specification and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figures 1-2 is a view for explaining an arc detection method according to a conventional method.

[0031] Figure 3 is a block diagram showing a configuration of an arc detection system according to an embodiment of the present disclosure.

[0032] Figure 4 is a block diagram showing a configuration of an arc detection unit according to an embodiment of the present disclosure.

[0033] Figure 5 is a block diagram showing a configuration of an arc detection system according to another embodiment of the present disclosure.

[0034] Figure 6 is a view showing an embodiment of setting an upper limit and a lower limit for arc detection according to an embodiment of the present disclosure.

[0035] Figure 7 is a view showing an embodiment of setting an upper limit and a lower limit under various conditions.

[0036] Figure 8 is a view showing an embodiment of performing arc detection according to an embodiment of the present disclosure.

[0037] Figure 9 is a view for explaining setting sections in data.

[0038] Figure 10 is a view for explaining an arc detection method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0039] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art can easily perform the present disclosure. The embodiments of the present disclosure can be modified in various forms, and thus the scope of the present disclosure should not be construed as being limited to the following embodiments. In addition, in the preferred embodiments of the present disclosure, detailed descriptions of known functions or configurations which can make the subject matter of the present disclosure unnecessarily unclear will be omitted. In all the drawings, like reference numerals are used to refer to the same or similar parts.

[0040] The term "include" means that other components can be further included as appropriate, unless the context clearly dictates otherwise, and does not exclude other components. More specifically, the term "include", "have" or "comprise" and the like are intended to mean that there is a presence of the described features, numbers, steps, actions, components, parts or combinations thereof in the description, unless the context clearly dictates otherwise. It should be understood that the present disclosure does not exclude the possibility of existence or addition of one or more other features or numbers, steps, operations, components, parts or combinations thereof.

[0041] Terms such as first or second can be used to describe various components, but these components should not be limited by these terms. For example, these terms are used only for the purpose of distinguishing one component from another component, and a first component can be referred to as a second component, and similarly, a second component can also be referred to as a first component, without departing from the scope of the inventive concept according to the present application.

[0042] Unless the context clearly dictates otherwise, the singular expression includes the plural expression. Therefore, the shape, size, etc. of the elements in the drawings can be exaggerated to make the description clear.

[0043] "A~unit" and "A~module" used throughout the specification are units that process at least one function or operation, and can mean, for example, software or hardware components such as an FPGA or an ASIC. However, "A~unit" and "A~module" are not limited to only software or hardware. "A~unit" and "A~module" can be configured to be located in an addressable storage medium, or can be configured to reproduce one or more processors.

[0044] In one example, "A~unit" and "A~module" are components including, for example, software components, object-oriented software components, class components, and task components, and processes, functions, attributes, procedures, sub-routines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The components and functions provided by "A~unit" and "A~module" can be respectively performed by a plurality of elements, and alternatively, "A~unit" and "A~module" can be integrated with other additional components.

[0045] The present disclosure aims to provide an algorithm for detecting an arc in a semiconductor device using a parameter sensitive to an arc during a semiconductor manufacturing process. Known techniques have problems in that the accuracy of detecting an arc is low due to tracking only a slope by tracking a difference between a current value and a previous value of a parameter, or due to tracking a value outside of a fixed bin (x-axis is time, and y-axis is a difference in intensity), and it is difficult to distinguish noise and an arc. In the present disclosure, a value can be compared in real time by setting an upper limit and a lower limit of data using a multiple of a mean value and a standard deviation within a bin. In the present disclosure, in view of the fact that an arc signal has an abnormally high or low parameter value, an algorithm outputs an arc signal when a detected signal exceeds a set upper limit or lower limit. It is not appropriate to determine the upper limit and the lower limit without considering other factors, because intensity varies depending on conditions. Therefore, in the present disclosure, the upper limit and the lower limit are calculated using a mean value and a standard deviation of a parameter of each section. When a detected signal exceeds the upper limit and the lower limit, it is considered that an arc has been detected, so that more accurate arc detection can be made than before.

[0046] Hereinafter, an arc detection method and system according to the present disclosure will be described in detail with reference to the accompanying drawings.

[0047] Figure 3 is a block diagram illustrating a configuration of an arc detection system according to an embodiment of the present disclosure.

[0048] An arc detection system according to an embodiment of the present disclosure can include a data collection unit 100 and an arc detection unit 200.

[0049] The data collection unit 100 can collect a process result of processing a substrate during a semiconductor manufacturing process. The data collection unit 100 can collect intensity information according to a change in time. The data collection unit 100 can collect a voltage value or a current value according to the process result. Information collected by the data collection unit 100 is not limited thereto, and can be parameter information related to the occurrence of an arc. In an embodiment, data collected by the data collection unit 100 is intensity.

[0050] The arc detection unit 200 can detect an arc using a mean value and a standard deviation of each section of data collected by the data collection unit 100. Hereinafter, a specific configuration of the arc detection unit 200 will be described with reference to Figure 4 A specific configuration of the arc detection unit 200 will be described.

[0051] Figure 4 is a block diagram illustrating a configuration of the arc detection unit 200 according to an embodiment of the present disclosure.

[0052] Referring to Figure 4 , the arc detection unit 200 according to the present disclosure can include a data separation unit 210, a calculation unit 220, a reference setting unit 230, and a determination unit 240.

[0053] The data separation unit 210 can separate the data collected by the data collection unit 100 for each section. In an embodiment, a criterion for separating the data by the data separation unit 210 by section can be the number of data. In an embodiment, the data can be sequentially classified based on 100 units. In an embodiment, the data can be sequentially classified based on 1000 units. In an embodiment, the data separation unit 210 can define the classified data as a bin. In an embodiment, the data separation unit 210 can sequentially separate the collected data except for the data immediately after the process starts and just before the process ends, which will be described later with reference to FIG. 2B. Figure 9

[0054] The calculation unit 220 can calculate the average and the standard deviation of the data separated for each section in the data separation unit 210. In an embodiment, the calculation unit 220 can calculate the average and the standard deviation of the data of each bin. In another embodiment, the calculation unit 220 can calculate the average and the standard deviation of the data of each bin, and can calculate a multiple of the standard deviation.

[0055] ​The reference setting unit 230 can set the upper limit (upper limit) and the lower limit (lower limit) for arc detection by applying the value calculated by the calculation unit 220. The reference setting unit 230 can set the upper limit and the lower limit for arc detection for each bin. The reference setting unit 230 can set a value obtained by adding n times (arbitrary times) of the standard deviation (standard deviation) to the average value (average value) calculated in the bin as the upper limit, i.e., upper limit = n * standard deviation + average value, and can set a value obtained by subtracting n times (arbitrary times) of the standard deviation from the average value as the lower limit, i.e., lower limit = average value - n * standard deviation. In this case, n can be an integer. The reference setting unit 230 can set different standards for setting the upper limit and the lower limit, respectively, according to a recipe controlled in the process. In an embodiment, it is assumed that there are a first recipe, a second recipe, and a third recipe. When the upper limit and the lower limit are set according to the first recipe, the upper limit can be set to one time of the standard deviation plus the average value, i.e., upper limit = 1 * standard deviation + average value. And the lower limit can be set to one time of the standard deviation from the average value, i.e., lower limit = average value - 1 * standard deviation. In another embodiment, when the upper limit and the lower limit are set according to the second recipe, the upper limit can be set to two times of the standard deviation plus the average value, i.e., upper limit = 2 * standard deviation + average value, and the lower limit can be set to two times of the standard deviation from the average value, i.e., lower limit = average value - 2 * standard deviation. In another embodiment, when the upper limit and the lower limit are set according to the third recipe, the upper limit can be set to three times of the standard deviation plus the average value, i.e., upper limit = 3 * standard deviation + average value, and the lower limit can be set to three times of the standard deviation from the average value, i.e., lower limit = average value - 3 * standard deviation. That is, the reference setting unit 230 can set the upper limit and the lower limit with different references according to each condition.

[0056] In an embodiment, the reference setting unit 230 can differently set the reference for setting the upper and lower limits according to the type of the parameter to be measured. In another embodiment, the reference setting unit 230 can differently set the reference for setting the upper and lower limits according to the type of the film or the time at which the process has proceeded. At this time, the reference to be set can be determined through experiments and experience. If the multiple is too small, there is a risk of generating noise; but if the multiple is too large, there is a risk that an arc can not be detected when the arc occurs. Thus, an appropriate multiple value can be set through experiments.

[0057] The determination unit 240 can determine that an arc has occurred when the detected signal exceeds the reference value set by the reference setting unit 230. When the data exceeds the upper and lower limits set by the reference setting unit 230, the determination unit 240 can determine that an arc has occurred.

[0058] Figure 5 is a block diagram of an arc detection system capable of performing arc detection according to an exemplary embodiment of the present application.

[0059] The arc detection method described below can be performed by a computing device. The computing device includes at least one of a computer, a workstation, a server, a desktop PC, a netbook, a smart phone, a tablet PC, a mobile phone, a video phone, an e-book reader, a PDA, a PMP, an MP3 player, a medical device, an electronic device, and a wearable device. In addition, the computing device can be implemented as a centrally managed data storage environment or a distributed data storage environment.

[0060] Figure 5 may be an exemplary computing device or a non-transitory computer readable medium for performing the arc detection method.

[0061] Referring to Figure 5 The arc detection system according to the embodiments of the present disclosure can include a memory 10, a processor 20, a display 30, an interface unit 40, and a bus.

[0062] Via the bus, various components such as the memory 10, the processor 20, the display 30, and the interface unit 40 can be connected to and communicate with each other (i.e., control message transmission and data transmission).

[0063] The memory 10 includes a volatile memory (e.g., a DRAM, SRAM, or SDRAM) and / or a non-volatile memory (e.g., an one time programmable ROM (OTPROM), a PROM, an EPROM, an EEPROM, a mask ROM, a flash ROM, a flash memory, a PRAM, a RRAM, an MRAM, a hard drive, or a solid state drive (SSD)). The memory 10 can include an internal memory and / or an external memory. For example, the memory 10 can store commands or data related to at least one other component of the electronic device. In addition, the memory 10 can store software and / or a program. The program can include, for example, a kernel, middleware, an application programming interface (API), and / or an application program (or "application"). At least a part of the kernel, middleware, or API can be referred to as an operating system.

[0064] The memory 10 stores instructions for performing the arc detection method, which will be described below.

[0065] Meanwhile, a non-transitory computer-readable medium in which a program for sequentially executing the arc detection method according to some embodiments of the disclosure is stored can be provided. The non-transitory computer-readable medium refers to a medium that stores data semi-permanently and can be read by a computer, rather than a medium that stores data for a short time, such as a register, a cache, and a memory. More specifically, the foregoing various applications or programs can be provided by being stored in a non-transitory readable medium such as a CD, a DVD, a hard disk, a Blu-ray disk, a USB, a memory card, a ROM, or the like. Examples of program instructions have not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter, etc. The hardware device described below can be configured to operate as one or more software modules to perform the operations of the disclosure, and vice versa.

[0066] The processor 20 can include one or more of a central processing unit, an application processor, and a communication processor (CP). For example, the processor 20 can perform computation or data processing related to communication and / or control of at least one other component of the computing device or the non-transitory computer-readable medium.

[0067] The display 30 can include, for example, a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, or a microelectromechanical system (MEMS) display, or an electronic paper display. For example, the display 30 can display various types of content (e.g., a text, an image, a video, an icon, and / or a symbol, etc.) to a user. The display 30 can include a touch screen, and can receive, for example, a touch, a gesture, a proximity, or a hovering input using an electronic pen or a part of a user's body.

[0068] The interface unit 40 allows the computing device to communicate with the outside through a network. The network herein includes both wired and wireless systems. In particular, wireless communication can include, for example, cellular communication using at least one of LTE, LTE-A (LTE-Advanced), CDMA (Code Division Multiple Access), WCDMA (Wideband CDMA), UMTS (Universal Mobile Telecommunications System), WiBro (Wireless Broadband), or GSM (Global System for Mobile Communications), etc. Alternatively, wireless communication can include at least one of WiFi (Wireless Fidelity), LiFi (Light Fidelity), Bluetooth, Bluetooth Low Energy (BLE), Zigbee, NFC (Near Field Communication), magnetic secure transmission, radio frequency (RF), or body area network (BAN), etc. Alternatively, wireless communication can include GNSS. The GNSS can be, for example, a global positioning system (GPS), a global navigation satellite system (Glonass), a Beidou navigation satellite system (hereinafter referred to as "Beidou"), or a Galileo global satellite-based navigation system of Europe. Wired communication can include, for example, at least one of USB (Universal Serial Bus), HDMI (High-Definition Multimedia Interface), RS-232 (Recommended Standard 232), power line communication, or POTS (Plain Old Telephone Service), a computer network (e.g., LAN or WAN), etc.

[0069] According to an embodiment of the disclosure, the memory 10 can include a data collection unit 100 and an arc detection unit 200. The data collection unit 100 and the arc detection unit 200 are the same as those shown in FIG. 1, and thus a description thereof will be omitted. Figure 4

[0070] Figure 6 is a view showing an embodiment in which upper and lower limits for arc detection are set according to an embodiment of the disclosure.

[0071] Referring to Figure 6 , a portion marked in blue (i.e., a blue region shown in Figure 6 ) represents raw data collected by the data collection unit 100. In an embodiment, the data separation unit 210 can divide a section for each data having similar characteristics, and the calculation unit 220 can calculate the average and the standard deviation of the data separated by the data separation unit 210. The reference setting unit 230 can set the upper and lower limits using the values calculated by the calculation unit 220.

[0072] Referring to Figure 6 , the red line represents the average of the data. Based on this, the reference point is set by setting a value obtained by adding n times the standard deviation to the average as the upper limit and setting a value obtained by subtracting n times the standard deviation from the average as the lower limit. At this time, it can be seen that the upper limit is represented by a gray line, and the lower limit is represented by a yellow line.

[0073] ​Figure 7 is a graph showing embodiments in which upper and lower limits are set under various conditions.

[0074] Referring to Figure 7 , embodiments in which the size of the box is set differently (horizontal axis) and the multiple of the standard deviation is set differently (vertical axis) are shown.

[0075] According to Figure 7 the horizontal axis, it can be seen that when the size of the box is set to be large, the effect of smoothing the overall data and the upper and lower limits exists, and when the size of the box is set to be small, it can be seen that the upper and lower limits are set more roughly.

[0076] According to Figure 7 the vertical axis, it can be seen that when the multiple of the standard deviation is set differently, different ranges of detectable arcs are provided.

[0077] According to the present disclosure, the upper and lower limits are tracked in real time by adding or subtracting the multiple of the standard deviation from the average value of the raw data of a certain period. When the peak value exceeds the upper and lower limits, it can be determined that an arc has occurred. Referring to Figure 7 , the smoothness and size of the upper and lower limits can be adjusted according to the length of the period set by the data separation unit 210 and the multiple of the standard deviation. The larger the size of the box, the better the smoothing effect, but there can be a delay in the process of collecting data to measure the average value.

[0078] Figure 8 is a view showing the execution of arc detection according to an embodiment of the present disclosure.

[0079] Referring to Figure 8 , when the peak value exceeds the upper limit, it can be seen that an arc signal has occurred.

[0080] Figure 9 is a view for explaining setting of a section in data.

[0081] According to the present disclosure, the data separation unit 210 can set a dead time period in which an arc is not detected, which lasts for a few seconds immediately after the process starts and just before the process ends. The data separation unit 210 can separate data other than the dead time, and can set a box. The data separation unit 210 separates data other than the dead time, so that sudden changes in intensity when the plasma is turned on and off can be ignored. In addition, the initial unstable state of generating the plasma can be ignored.

[0082] In the present disclosure, as the length of the period increases, an arc cannot be tracked in the initial period of time that is as long as the size of the first bin, but the log of the first few seconds is unreliable, so the initial dead time is set to a few seconds. This has the effect of improving the accuracy of the data.

[0083] Figure 10 is a view for describing an arc detection method according to an exemplary embodiment of the present disclosure.

[0084] The arc detection method according to the present disclosure can include the steps of performing a process recipe for a substrate process in a semiconductor device and collecting data according to the recipe (S10), separating the collected data by setting bins (S20), obtaining an average value and a standard deviation of the data separated for each bin (S30), setting an upper limit and a lower limit for detecting an arc using the average value and the standard deviation (S40), and determining that an arc has occurred when an arc signal exceeds the upper limit and the lower limit (S50).

[0085] At this time, separating the collected data by setting bins can separate the collected data except for data immediately after the process starts and just before the process ends.

[0086] At this time, setting the upper limit and the lower limit for arc detection using the average value and the standard deviation can set the upper limit by adding an arbitrary number of standard deviations (i.e., n times the standard deviation) to the average value, and can set the lower limit by subtracting n times the standard deviation from the average value. In addition, setting the upper limit and the lower limit for arc detection using the average value and the standard deviation can differently set the upper limit and the lower limit for each parameter or recipe in the process recipe.

[0087] The arc detection method according to the present disclosure is not limited to a specific device and parameters, such as a CCP / ICP device, an ashing / etching device, Vrms / EPD intensity, and can detect arcs of various semiconductor devices.

[0088] According to the present disclosure, instead of following a high-resolution parameter with time intervals by data sampling, by setting a relatively large bin and performing data processing on the relatively large bin, noise can be ignored and overall trends can be tracked in real time. A disadvantage of a conventional arc detection method (e.g., only tracking a slope value of a parameter in real time) is that it cannot keep up with the development speed of a high-resolution arc detector, thereby degrading the quality of data, or it cannot cope with various qualities of films and process recipes. An advantage of the present disclosure is that, by effectively using an average value and n times a standard deviation of a bin set in real time, a high-resolution arc detector is reacted in real time, noise is ignored, and is independent of an absolute value of a parameter, an absolute slope value, and a number of points sensitively reacting to various film qualities and process recipes. In addition, various applications are expected to be performed on various equipment groups, without being limited to a gray ozer.

[0089] It should be understood that the embodiments herein are intended to help understand the present disclosure, and are not intended to limit the scope of the present disclosure, and various modified embodiments are also within the scope of the present disclosure. Accordingly, the technical scope of the present invention will be defined by the technical spirit of the appended claims. That is, the technical scope of the present invention should be interpreted to include modifications, equivalents, and alternatives to the components described in the above embodiments.

[0090] 1: Arc detection system

[0091] 10: Memory

[0092] 20: Processor

[0093] 30: Interface unit

[0094] 40: Display

[0095] 100: Data collection unit

[0096] 200: Arc detection unit

[0097] 210: Data separation unit

[0098] 220: Calculation unit

[0099] 230: Reference setting unit

[0100] 240: Determination unit

Claims

1. A method for detecting an arc in a semiconductor device, the method comprising: performing a process for treating a substrate using the plasma and collecting data based on the process; Separate the collected data by setting segments; Obtain the mean and standard deviation of the data separated for each segment; as well as An upper limit for detecting an arc is set using a sum of the average value and a multiple of the standard deviation, and a lower limit for detecting an arc is set using a difference between the average value and a multiple of the standard deviation; wherein the multiple of the standard deviation is related to a recipe in the treatment process; Wherein, separating the collected data by setting segments includes: separating the collected data based on a preset amount of data; the size of the segment is determined by the preset amount of data; wherein the smoothness of the upper limit and the lower limit are adjusted according to the size of the segment; The separating the collected data by setting sections includes separating the collected data except for the data of the set time after the plasma is turned on and the data of the set time before the plasma is turned off.

2. The method according to claim 1, wherein When the average value and the standard deviation are used to set the upper and lower limits for arc detection, the upper limit is set to a value obtained by adding n times the standard deviation to the average value, and the lower limit is set to a value obtained by subtracting n times the standard deviation from the average value, where n is an integer.

3. The method according to any one of claims 1 to 2, further comprising: When the detected signal exceeds the upper limit and the lower limit, it is determined that an arc has occurred.

4. The method according to claim 3, wherein: Using the mean and the standard deviation to set upper and lower limits for arc detection includes setting the upper and lower limits differently for each parameter or recipe in the process. 5 . A computer-readable recording medium storing a program for executing the method according to claim 1 .

6. A system capable of detecting an arc generated in a semiconductor device, the system comprising: a data collection unit for collecting results of processing the substrate by the plasma; and an arc detection unit that detects an arc using an average value and a standard deviation for each section based on the data collected by the data collection unit; Wherein, the arc detection unit includes: a data separation unit for separating the data collected by the data collection unit for each section; a calculation unit for calculating an average value and a standard deviation of the data separated for each segment in the data separation unit; a reference setting unit that sets an upper limit for arc detection by applying a sum of the average value calculated by the calculation unit and a multiple of the standard deviation, and sets a lower limit for arc detection by applying a difference between the average value calculated by the calculation unit and a multiple of the standard deviation; wherein the multiple of the standard deviation is related to a recipe in a treatment process; and a determination unit that determines that an arc has occurred when the detected signal exceeds a reference value set by the reference setting unit; wherein the data separation unit separates the collected data based on a preset amount of data; and the size of the segment is determined by the preset amount of data; wherein the smoothness of the upper limit and the lower limit are adjusted according to the size of the segment; The data separation unit separates the collected data except for the data of the set time after the plasma is turned on and the data of the set time before the plasma is turned off.

7. The system according to claim 6, wherein: The reference setting unit sets the upper limit by adding n times the standard deviation to the average value, and sets the lower limit by subtracting n times the standard deviation from the average value, where n is an integer.

8. The system according to claim 7, wherein: The reference setting unit sets upper and lower limits differently for each parameter or recipe in the treatment process.

9. An arc detection system comprising a processor and a memory storing program code executable by the processor, wherein the processor implements: performing a process for processing a substrate using the plasma and collecting data based on the process; Separate the collected data by setting segments; Obtain the mean and standard deviation of the data separated for each segment; as well as An upper limit for detecting an arc is set using a sum of the average value and a multiple of the standard deviation, and a lower limit for detecting an arc is set using a difference between the average value and a multiple of the standard deviation; wherein the multiple of the standard deviation is related to a recipe in the treatment process; wherein the processor separates the collected data based on a predetermined number of data; and the size of the segment is determined by the predetermined number of data; wherein the smoothness of the upper limit and the lower limit are adjusted according to the size of the segment; The processor separates the collected data except for the data of the set time after the plasma is turned on and the data of the set time before the plasma is turned off.

10. The system according to claim 9, wherein: The processor sets an upper limit by adding n times the standard deviation to the mean value, and sets a lower limit by subtracting n times the standard deviation from the mean value, where n is an integer.

11. The system according to any one of claims 9 to 10, wherein: The processor determines that an arc has occurred when the detected signal exceeds the upper limit and the lower limit.

12. The system according to claim 11, wherein The processor sets the upper limit and the lower limit differently for each parameter or recipe in the treatment process.

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