WO2023148967A1 - 診断装置、診断方法、半導体製造装置システム及び半導体装置製造システム - Google Patents

診断装置、診断方法、半導体製造装置システム及び半導体装置製造システム Download PDF

Info

Publication number
WO2023148967A1
WO2023148967A1 PCT/JP2022/004679 JP2022004679W WO2023148967A1 WO 2023148967 A1 WO2023148967 A1 WO 2023148967A1 JP 2022004679 W JP2022004679 W JP 2022004679W WO 2023148967 A1 WO2023148967 A1 WO 2023148967A1
Authority
WO
WIPO (PCT)
Prior art keywords
deterioration
degree
diagnosis
sensor waveform
diagnostic
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/JP2022/004679
Other languages
English (en)
French (fr)
Japanese (ja)
Inventor
祥太 梅田
誠浩 角屋
涼次 朝倉
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hitachi High Tech Corp
Original Assignee
Hitachi High Tech Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hitachi High Tech Corp filed Critical Hitachi High Tech Corp
Priority to JP2023500380A priority Critical patent/JP7442013B2/ja
Priority to US18/025,774 priority patent/US20240395518A1/en
Priority to KR1020237005510A priority patent/KR102863239B1/ko
Priority to PCT/JP2022/004679 priority patent/WO2023148967A1/ja
Priority to CN202280005592.9A priority patent/CN116897411A/zh
Priority to TW112102363A priority patent/TWI854452B/zh
Publication of WO2023148967A1 publication Critical patent/WO2023148967A1/ja
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
    • H01J37/00—Discharge tubes with provision for introducing objects or material to be exposed to the discharge, e.g. for the purpose of examination or processing thereof
    • H01J37/32—Gas-filled discharge tubes
    • H01J37/32917—Plasma diagnostics
    • H01J37/32935—Monitoring and controlling tubes by information coming from the object and/or discharge
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
    • H01J37/00—Discharge tubes with provision for introducing objects or material to be exposed to the discharge, e.g. for the purpose of examination or processing thereof
    • H01J37/32—Gas-filled discharge tubes
    • H01J37/32431—Constructional details of the reactor
    • H01J37/32798—Further details of plasma apparatus not provided for in groups H01J37/3244 - H01J37/32788; special provisions for cleaning or maintenance of the apparatus
    • H01J37/3288—Maintenance
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
    • H01J37/00—Discharge tubes with provision for introducing objects or material to be exposed to the discharge, e.g. for the purpose of examination or processing thereof
    • H01J37/32—Gas-filled discharge tubes
    • H01J37/32917—Plasma diagnostics
    • H01J37/32926—Software, data control or modelling
    • H—ELECTRICITY
    • H10—SEMICONDUCTOR DEVICES; ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10P—GENERIC PROCESSES OR APPARATUS FOR THE MANUFACTURE OR TREATMENT OF DEVICES COVERED BY CLASS H10
    • H10P74/00—Testing or measuring during manufacture or treatment of wafers, substrates or devices
    • H—ELECTRICITY
    • H10—SEMICONDUCTOR DEVICES; ELECTRIC SOLID-STATE DEVICES NOT OTHERWISE PROVIDED FOR
    • H10P—GENERIC PROCESSES OR APPARATUS FOR THE MANUFACTURE OR TREATMENT OF DEVICES COVERED BY CLASS H10
    • H10P95/00—Generic processes or apparatus for manufacture or treatments not covered by the other groups of this subclass

Definitions

  • the present invention relates to a diagnostic apparatus, a diagnostic method, a semiconductor manufacturing apparatus system, and a semiconductor device manufacturing system for a plasma processing apparatus that processes semiconductor wafers with plasma.
  • a plasma processing apparatus is an apparatus that converts a substance into plasma and removes the substance on the wafer by the action of the substance in order to form fine features on a semiconductor wafer.
  • maintenance such as cleaning of the inside of the apparatus and replacement of parts is normally performed periodically based on the number of processed wafers or the like.
  • unplanned maintenance work may occur due to deterioration of parts due to aging and accumulation of reaction by-products depending on usage.
  • it is necessary to continuously monitor the deterioration state of parts and take early countermeasures such as cleaning and parts replacement according to the deterioration state.
  • the diagnosis device for the plasma processing apparatus has a sensor waveform, which is a time-series signal composed of multiple sensor items sequentially obtained for each plasma processing from multiple state sensors attached to the plasma processing apparatus.
  • the degree of deterioration indicating the state of deterioration of the parts is estimated, the need for maintenance is diagnosed based on the degree of deterioration, and an alarm is issued as necessary.
  • Patent Document 1 an anomaly detection device applies statistical modeling to a summary value that summarizes observed values, thereby removing noise from the summary value.
  • Japanese Patent Application Laid-Open No. 2012-9064 describes "a learning-type process abnormality diagnosis device that achieves accurate abnormality detection and abnormality diagnosis performance suitable for practical process monitoring”.
  • the state of the apparatus may change according to the number of times plasma processing is performed. Accordingly, sensor waveform data, for example, may also undergo an offset change.
  • the sensor waveform shape changes due to component deterioration the sensor waveform changes related to component deterioration are buried in unrelated sensor waveform changes in the prior art, and signs of deterioration can be accurately captured. misrepresentation or oversight may occur.
  • the degree of deterioration calculated from the sensor waveform data and this data may differ between apparatuses due to differences in processing histories and the like. For this reason, false information or oversight may occur in the prior art in which deterioration diagnosis is performed using a common threshold value for a plurality of apparatuses. Furthermore, in the plasma processing apparatus, processing intervals are short, and signs of deterioration cannot always be confirmed for all sensor waveform data in each processing, and signs of deterioration may appear intermittently. In such a case, it is difficult to determine whether the increase in the degree of deterioration is noise, that is, whether it is in a deteriorated state. Prior art techniques that dynamically set thresholds for each process are susceptible to noise and can generate false alarms.
  • the present invention includes a plurality of means for solving the above problems.
  • a diagnostic device diagnoses whether or not a maintenance target part of a group of plasma processing devices needs maintenance, and for each plasma processing, Diagnosis is performed using the degree of deterioration indicating the state of deterioration of the component calculated using sensor waveform data acquired from a group of state sensors provided in at least one of each component of the plasma processing apparatus and a preset threshold for the degree of deterioration.
  • the diagnostic device separates the sensor waveform data into components for each of a plurality of sensor waveform change types defined in advance, and separates each separated sensor waveform component into sensor waveform components during normal operation and diagnosis, or during deterioration and during diagnosis. The degree of deterioration is calculated based on the sensor waveform components of
  • the diagnostic device filters time-series data of the degree of deterioration calculated for each plasma processing, and performs filtering in a learning interval from the time of component maintenance to the time after a predetermined number of times of processing for each plasma processing device.
  • a threshold value used for deterioration diagnosis is set based on a distribution calculated using a plurality of degrees of deterioration.
  • FIG. 1 is an overall configuration diagram of a plasma processing apparatus and a diagnostic apparatus according to one embodiment.
  • FIG. 2 is a flowchart illustrating an example of the flow of diagnostic processing according to one embodiment.
  • FIG. 3 is a diagram illustrating an example of data stored in a sensor waveform storage unit according to one embodiment;
  • FIG. 4 is a diagram for explaining an example of separated sensor waveform types according to one embodiment.
  • FIG. 5 is a diagram for explaining an example in which a plurality of sensor waveform change types due to a plurality of factors of sensor waveform data are mixed.
  • FIG. 6 is a diagram showing an example in which noise and intermittent signs of deterioration coexist in the time-series data of the degree of deterioration for each process, and an example of the time-series data of the degree of deterioration after the deterioration degree filtering.
  • FIG. 7 is a diagram showing transition of the degree of deterioration as an example of a display screen of deterioration diagnosis information according to one embodiment.
  • FIG. 8 is a diagram showing a comparison of sensor waveform components as an example of a display screen of deterioration diagnosis information according to one embodiment.
  • FIG. 9 is an overall configuration diagram of a plasma processing apparatus and diagnostic apparatus according to another embodiment.
  • the plasma processing apparatus group 1 As shown in the configuration diagram of FIG. 1, the plasma processing apparatus group 1 according to the present embodiment generates plasma 100 according to preset plasma processing conditions to plasma-process a wafer (sample 101). .
  • a plasma processing apparatus group 1 includes plasma processing apparatuses 10 and 11 as semiconductor manufacturing apparatuses. Further, the plasma processing apparatus (10, 11) has a state sensor group 102 for sensing the state of the chamber (reaction chamber) inside the plasma processing apparatus (10, 11) and the state of the parts. Measured values of sensor values (for example, temperature and pressure) during medium or idle can be acquired as sensor waveform data.
  • the diagnosis device 2 is a server configured by an analysis unit 51 that sets calculation conditions for the execution unit 31 and analyzes diagnosis results, a storage unit 52 that stores information necessary for the processing of the analysis unit 51, and a display unit 53. 50.
  • the plasma processing apparatus group 1 is connected directly or via a network to a computer group (computer 30, computer 40, . . . ).
  • the computer group (computer 30, computer 40, . . . ) and the server 50 are connected via a network.
  • each computer can perform high-speed calculation using the sensor waveform data acquired from each plasma processing apparatus in the execution unit 31 .
  • the server 50 can set deterioration degree calculation conditions across the plasma processing apparatus group 1 and analyze and display diagnosis results.
  • the execution unit 31 includes a preprocessing unit 310 , a sensor waveform separation unit 311 , a deterioration diagnosis unit 312 , a deterioration degree calculation unit 313 , a deterioration degree filter unit 314 and a deterioration degree learning unit 315 for each device in this example.
  • the storage unit 32 includes a sensor waveform storage unit 320 and a deterioration degree storage unit 321 in this example.
  • the analysis unit 51 includes a deterioration degree calculation condition setting unit 510 and a diagnosis result analysis unit 511 in this example.
  • the storage unit 52 includes a deterioration diagnostic information storage unit 520, a maintenance information storage unit 521, and a deterioration sensor waveform component storage unit 522 in this example.
  • the diagnostic device 2 (computer 30 or 40) and the semiconductor manufacturing device (10 or 11) are connected via a network to constitute a semiconductor manufacturing device system.
  • a semiconductor device manufacturing system is configured by connecting the diagnostic device 2 (computer 30 or 40, server 50) and the semiconductor manufacturing device (10 or 11) via a network.
  • the diagnostic device 2 (computer 30 or 40, server 50) is a platform on which an application for diagnosing the necessity of maintenance of the parts of the semiconductor manufacturing equipment (10 or 11) using the degree of deterioration indicating the deterioration state of the parts is installed. Prepare.
  • FIG. 2 shows the processing flow for each plasma processing.
  • FIG. 3 shows an example of sensor waveform data to be stored.
  • the sensor waveform data 41 consists of a plurality of sensor items 33, and the acquired sensor values are stored in the sensor waveform data columns of the corresponding sensor item 33 columns.
  • identification information for identifying plasma processing contents and objects such as an apparatus ID 34, a processing ID 35, a processing condition ID 36, and a processing date and time 37 is stored.
  • the apparatus ID 34 is information for identifying the plasma processing apparatus 10 that has performed plasma processing.
  • the processing ID 35 is information for identifying wafers that have undergone plasma processing.
  • the processing condition ID 36 is information for identifying settings of the plasma processing apparatus 10 and process steps (one plasma processing is further divided into a plurality of processes) when plasma processing is performed.
  • the preprocessing unit 310 acquires the sensor waveform data of the sensor items used for diagnosis of the diagnosis target component from the sensor waveform storage unit 320 and performs preprocessing (S2).
  • preprocessing according to the contents preset by the deterioration degree calculation condition setting unit 510, for example, extraction of the processing condition ID 34 used for diagnosis from the sensor waveform data, extraction of the time interval within the plasma processing time, standardization of the sensor waveform data 41, Remove missing values.
  • the sensor waveform separation unit 311 separates the sensor waveform data 41 into components for each type of sensor waveform change defined in advance (S3).
  • An example of separating the sensor waveform data 41 into an offset component 42, a trend component 43, and a noise component 44 will be described with reference to FIG.
  • the offset component 42 is a component indicating the offset term of the sensor waveform data 41, and is calculated as an average value of the sensor values VS over the processing time TP, for example.
  • a trend component 43 is calculated by applying a modeling technique applicable to time-series data such as a Kalman filter or Markov chain Monte Carlo method (MCMC) to the component obtained by subtracting the offset component 42 from the sensor waveform data 41 .
  • MCMC Markov chain Monte Carlo method
  • Noise component 44 is calculated by subtracting offset component 42 and trend component 43 from sensor waveform data 41 . It should be noted that when separating the sensor waveforms, it is sufficient to separate the sensor waveform change type that is not related to the deterioration of parts due to changes in the state of the device and the sensor waveform change type that is related to the signs of deterioration of the parts. There are no particular restrictions on the type, number, and method of separation.
  • the sensor waveform separation unit 311 stores each component (42, 43, 44) of the separated sensor waveform data 41 in the sensor waveform storage unit 320 (S4).
  • the deterioration degree calculation unit 313 compares the sensor waveform component of the sensor item used for diagnosis of the diagnosis target part with the sensor waveform component data serving as a reference, and calculates the deterioration degree corresponding to the processing time point, Stored in the deterioration degree storage unit 321 (S5).
  • the reference sensor waveform component data for example, a group of sensor waveform components in the plasma processing during the period from immediately after maintenance of the target component to the designated number of times of processing can be used as a normal reference.
  • the degree of deterioration can be calculated as the degree of dissimilarity with the reference.
  • a group of sensor waveform components in plasma processing in a period immediately before maintenance of a target part can be stored in the deterioration sensor waveform component storage unit 522 and used as a reference when deterioration occurs.
  • the degree of deterioration can be calculated as the degree of similarity with the reference.
  • Machine learning methods can be applied to the method of calculating the degree of deterioration as the degree of dissimilarity or similarity with the reference. For example, methods such as the k-nearest neighbor method, the singular spectrum transformation method, and the support vector machine for time series data are used. be able to.
  • FIG. 5 is a diagram showing an example in which multiple sensor waveform change types due to multiple factors of sensor waveform data are mixed.
  • the horizontal axis represents 54 processes. Comparing the sensor waveform data 41 in the normal state 55 and in the deteriorated state 56, the offset component change 57 and the noise component change 58 are mixed in the deteriorated state 56.
  • FIG. 5 is a diagram showing an example in which multiple sensor waveform change types due to multiple factors of sensor waveform data are mixed.
  • the horizontal axis represents 54 processes. Comparing the sensor waveform data 41 in the normal state 55 and in the deteriorated state 56, the offset component change 57 and the noise component change 58 are mixed in the deteriorated state 56.
  • the offset component change 57 is a sensor waveform change caused by a change in the state of the device
  • the noise component change 58 is a sensor waveform change indicating signs of deterioration
  • the sensor waveform data 41 before the sensor waveform component separation is used to determine the degree of deterioration.
  • the noise component 44 as the sensor waveform component used for calculating the degree of deterioration, it is possible to correctly grasp the signs of deterioration.
  • the calculator 30 determines whether sufficient sensor waveform data 41 has been accumulated for threshold setting, that is, learning of the degree of deterioration, based on whether or not the specified number of times of plasma processing has elapsed (S6).
  • the deterioration degree filter unit 314 performs filtering processing on the time-series data of multiple deterioration degrees calculated for each plasma processing (S7).
  • the graph 62 of the time-series data 61 of the deterioration degree 60 on the left side of FIG. 6 there is a low-frequency deterioration degree increase 64 due to noise in the section 63 during normal operation when the processing ID 35 is small.
  • the period of deterioration 65 signs of deterioration appear frequently, and there is an increase 66 in degree of deterioration that occurs frequently and intermittently.
  • the part of the deterioration degree increase 64 with low frequency is regarded as noise and the increase in the degree of deterioration is suppressed, and the increase in the degree of deterioration with high frequency is suppressed.
  • a portion 66 is regarded as a sign of deterioration, and a deterioration degree filter is applied so as to continuously take a high degree of deterioration. This makes it possible to distinguish between noise and intermittent signs of deterioration in the diagnosis of whether or not maintenance is necessary using a threshold value, thereby reducing false alarms.
  • There are various methods that can be used for the degradation filter but for example, a method using the trend term of the Kalman filter, a method using the Kalman filter sequentially and using its error term, and a smoothing method can be used.
  • the device deterioration degree learning unit 315 sets the threshold (S9). First, the deterioration degree learning unit 315 for each device extracts a deterioration degree group of a learning interval from immediately after maintenance to the specified number of times of processing in the deterioration degree time-series data after the deterioration degree filter processing.
  • the probability distribution of the deterioration degree group is estimated, and, for example, the value of the deterioration degree in the set confidence interval is set as the threshold value used for diagnosing the target component of the plasma processing apparatus.
  • the distribution estimation if the distribution can be approximated by the normal distribution, the normal distribution is estimated, and if the approximation is not possible, the non-normal distribution is estimated using a non-normal distribution estimation method such as MCMC.
  • the threshold is automatically set for each period from immediately after the maintenance of the target part to the maintenance for each plasma processing apparatus, so the influence of the difference between apparatuses and the threshold become obsolete, resulting in deterioration of diagnostic accuracy. It becomes possible to remove the influence.
  • the deterioration diagnosis unit 312 compares the degree of deterioration after application of the deterioration degree filter in the plasma processing with the threshold set in S9, and issues an alert when the threshold is exceeded (S10).
  • the deterioration diagnosis unit 312 includes the target component ID, the used sensor item 33, the used sensor waveform components (42, 43, 44), the device ID 34 to be diagnosed, the value of the degree of deterioration for each process ID 35, and the threshold Information related to the diagnosis such as the degree of deterioration ratio is stored in the deterioration diagnosis information storage unit 520, and the diagnosis result is displayed on the display unit 53 so that the diagnosis result can be confirmed on the display unit 53 as necessary.
  • FIG. 7 is a diagram showing the transition of the degree of deterioration as an example of the display screen 70 of the deterioration diagnostic information.
  • the deterioration degree transition status and threshold settings can be listed for each combination of (component ID, sensor item, sensor waveform component) used for deterioration degree calculation.
  • an alert is displayed like D10. The user can see this and centrally manage the deterioration state of the target parts of the plasma processing apparatus group 1, and perform early maintenance on the maintenance target parts based on the alarm that is issued. This can lead to a reduction in the non-operating time of group 1.
  • FIG. 8 is a diagram showing a comparison of sensor waveform components as an example of a display screen 80 of deterioration diagnosis information.
  • the device ID, component ID, sensor item, and sensor waveform component By designating the device ID, component ID, sensor item, and sensor waveform component, it is possible to list and compare the sensor waveform component data for each process ID that is the source of the deterioration degree calculation. By looking at this, the user can, for example, check how the sensor waveform component changes when the degree of deterioration is large. can be done.
  • the present invention is not limited to the above embodiments, and can be modified in various ways without departing from the scope of the invention.
  • the analysis unit 51, the storage unit 52, and the display unit 53 of the server 50 may be provided in the computers B30 and B40 as shown in FIG. good.
  • a diagnostic device for diagnosing the need for maintenance of parts of semiconductor manufacturing equipment using the deterioration degree indicating the deterioration state of the parts The acquired sensor waveform is separated into a plurality of components for each waveform change type, The degree of deterioration is obtained based on the separated components for each waveform change type.
  • a component for each waveform change type is separated into an offset component, a trend component, or a noise component.
  • a normal distribution or a non-normal distribution is estimated by machine learning using the Markov chain Monte Carlo method with the degree of deterioration as an input value, Based on the likelihood of normal distribution or non-normal distribution, a threshold value used for diagnosing the necessity of maintenance is obtained.
  • a semiconductor manufacturing equipment system includes the diagnostic device of 1) above connected to the semiconductor manufacturing equipment via a network.
  • Semiconductor device manufacturing comprising a platform to which semiconductor manufacturing equipment is connected via a network and on which an application for diagnosing the necessity of maintenance of parts of the semiconductor manufacturing equipment is implemented using the degree of deterioration indicating the deterioration state of the parts.
  • the application executes a step of separating the acquired sensor waveform into a plurality of components for each waveform change type, and a step of obtaining a degree of deterioration based on the separated components for each waveform change type.
  • diagnostic device in diagnosing whether or not maintenance is necessary for parts of a group of plasma processing equipment, highly accurate diagnosis can be achieved with little false information or oversight. Effective countermeasures are possible.
  • Plasma processing apparatus group 1: Plasma processing apparatus group, 2: Diagnostic device, 30: Calculator, 50: Server, 311: Sensor waveform separation unit, 314: Deterioration degree filter unit, 315: Deterioration degree learning unit for each device, 522: Sensor waveform component at deterioration memory

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Plasma & Fusion (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Drying Of Semiconductors (AREA)
  • Testing And Monitoring For Control Systems (AREA)
PCT/JP2022/004679 2022-02-07 2022-02-07 診断装置、診断方法、半導体製造装置システム及び半導体装置製造システム Ceased WO2023148967A1 (ja)

Priority Applications (6)

Application Number Priority Date Filing Date Title
JP2023500380A JP7442013B2 (ja) 2022-02-07 2022-02-07 診断装置、診断方法、半導体製造装置システム及び半導体装置製造システム
US18/025,774 US20240395518A1 (en) 2022-02-07 2022-02-07 Diagnostic device, diagnostic method, semiconductor manufacturing equipment system, and semiconductor equipment manufacturing system
KR1020237005510A KR102863239B1 (ko) 2022-02-07 2022-02-07 진단 장치, 진단 방법, 반도체 제조 장치 시스템 및 반도체 장치 제조 시스템
PCT/JP2022/004679 WO2023148967A1 (ja) 2022-02-07 2022-02-07 診断装置、診断方法、半導体製造装置システム及び半導体装置製造システム
CN202280005592.9A CN116897411A (zh) 2022-02-07 2022-02-07 诊断装置、诊断方法、半导体制造装置系统以及半导体装置制造系统
TW112102363A TWI854452B (zh) 2022-02-07 2023-01-18 診斷裝置、診斷方法、半導體製造裝置系統及半導體裝置製造系統

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/JP2022/004679 WO2023148967A1 (ja) 2022-02-07 2022-02-07 診断装置、診断方法、半導体製造装置システム及び半導体装置製造システム

Publications (1)

Publication Number Publication Date
WO2023148967A1 true WO2023148967A1 (ja) 2023-08-10

Family

ID=87552003

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2022/004679 Ceased WO2023148967A1 (ja) 2022-02-07 2022-02-07 診断装置、診断方法、半導体製造装置システム及び半導体装置製造システム

Country Status (6)

Country Link
US (1) US20240395518A1 (https=)
JP (1) JP7442013B2 (https=)
KR (1) KR102863239B1 (https=)
CN (1) CN116897411A (https=)
TW (1) TWI854452B (https=)
WO (1) WO2023148967A1 (https=)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPWO2025057394A1 (https=) * 2023-09-15 2025-03-20

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010283000A (ja) * 2009-06-02 2010-12-16 Renesas Electronics Corp 半導体製造における装置異常の予兆検知方法
JP2012009064A (ja) * 2011-09-05 2012-01-12 Toshiba Corp 学習型プロセス異常診断装置、およびオペレータ判断推測結果収集装置
WO2018061842A1 (ja) * 2016-09-27 2018-04-05 東京エレクトロン株式会社 異常検知プログラム、異常検知方法および異常検知装置
WO2020152889A1 (ja) * 2019-07-30 2020-07-30 株式会社日立ハイテク 装置診断装置、プラズマ処理装置及び装置診断方法

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6421457B1 (en) * 1999-02-12 2002-07-16 Applied Materials, Inc. Process inspection using full and segment waveform matching
US7413672B1 (en) * 2006-04-04 2008-08-19 Lam Research Corporation Controlling plasma processing using parameters derived through the use of a planar ion flux probing arrangement
EP2254112B1 (en) * 2008-03-21 2017-12-20 Tokyo University Of Science Educational Foundation Administrative Organization Noise suppression devices and noise suppression methods
US20100076729A1 (en) * 2008-09-19 2010-03-25 Applied Materials, Inc. Self-diagnostic semiconductor equipment
WO2011002811A2 (en) * 2009-06-30 2011-01-06 Lam Research Corporation Arrangement for identifying uncontrolled events at the process module level and methods thereof
US9200950B2 (en) * 2014-02-25 2015-12-01 Applied Materials, Inc. Pulsed plasma monitoring using optical sensor and a signal analyzer forming a mean waveform
EP3796362A1 (en) * 2019-09-23 2021-03-24 TRUMPF Huettinger Sp. Z o. o. Method of plasma processing a substrate in a plasma chamber and plasma processing system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010283000A (ja) * 2009-06-02 2010-12-16 Renesas Electronics Corp 半導体製造における装置異常の予兆検知方法
JP2012009064A (ja) * 2011-09-05 2012-01-12 Toshiba Corp 学習型プロセス異常診断装置、およびオペレータ判断推測結果収集装置
WO2018061842A1 (ja) * 2016-09-27 2018-04-05 東京エレクトロン株式会社 異常検知プログラム、異常検知方法および異常検知装置
WO2020152889A1 (ja) * 2019-07-30 2020-07-30 株式会社日立ハイテク 装置診断装置、プラズマ処理装置及び装置診断方法

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPWO2025057394A1 (https=) * 2023-09-15 2025-03-20

Also Published As

Publication number Publication date
US20240395518A1 (en) 2024-11-28
KR20230120121A (ko) 2023-08-16
TW202333073A (zh) 2023-08-16
TWI854452B (zh) 2024-09-01
CN116897411A (zh) 2023-10-17
JP7442013B2 (ja) 2024-03-01
KR102863239B1 (ko) 2025-09-22
JPWO2023148967A1 (https=) 2023-08-10

Similar Documents

Publication Publication Date Title
US7308385B2 (en) Diagnostic systems and methods for predictive condition monitoring
US8082124B2 (en) Method and system for diagnosing abnormal plasma discharge
US7539597B2 (en) Diagnostic systems and methods for predictive condition monitoring
Madakyaru et al. Improved data-based fault detection strategy and application to distillation columns
EP0906593B1 (en) Industrial process surveillance system
JP4071449B2 (ja) センサ異常検出方法及びセンサ異常検出装置
US20140365179A1 (en) Method and Apparatus for Detecting and Identifying Faults in a Process
AU2002246994A1 (en) Diagnostic systems and methods for predictive condition monitoring
US20150219530A1 (en) Systems and methods for event detection and diagnosis
KR20200005202A (ko) 기계 학습 기반의 설비 이상 탐지 시스템 및 방법
CN116034326B (zh) 用于异常检测的监测设备和方法
KR20200005206A (ko) 기계 학습 기반의 설비 이상 분류 시스템 및 방법
WO2023148967A1 (ja) 診断装置、診断方法、半導体製造装置システム及び半導体装置製造システム
US7085681B1 (en) Symbiotic interrupt/polling approach for monitoring physical sensors
JP2010039733A (ja) 製造プロセスの監視方法、監視プログラム、監視システムおよび製品の製造方法
Madakyaru et al. Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test
CN115345190A (zh) 信号异常的检测方法、装置及服务器
KR102797798B1 (ko) 진단 장치 및 진단 방법 그리고 플라스마 처리 장치 및 반도체 장치 제조 시스템
CN120019342A (zh) 工艺处理装置的诊断装置、诊断系统以及诊断方法
US20240310821A1 (en) Diagnostic apparatus and diagnostic method, and semiconductor manufacturing apparatus system and semiconductor apparatus manufacturing system
Rostami et al. Equipment health modeling for deterioration prognosis and fault signatures diagnosis
Pizarro et al. Real-Time Bayesian Modeling for Industrial Condition-Based Maintenance Applications
WO2026023028A1 (ja) 半導体製造装置の診断装置、診断システム、および診断方法
JP2009054766A (ja) 製造プロセスの監視方法、監視装置及び監視プログラム並びに半導体装置の製造方法

Legal Events

Date Code Title Description
WWE Wipo information: entry into national phase

Ref document number: 2023500380

Country of ref document: JP

WWE Wipo information: entry into national phase

Ref document number: 202280005592.9

Country of ref document: CN

WWE Wipo information: entry into national phase

Ref document number: 18025774

Country of ref document: US

121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22924876

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 22924876

Country of ref document: EP

Kind code of ref document: A1