WO2009028743A1 - Procédé de surveillance de chambre en temps réel utilisant un algorithme d'intelligence artificielle - Google Patents

Procédé de surveillance de chambre en temps réel utilisant un algorithme d'intelligence artificielle Download PDF

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Publication number
WO2009028743A1
WO2009028743A1 PCT/KR2007/004076 KR2007004076W WO2009028743A1 WO 2009028743 A1 WO2009028743 A1 WO 2009028743A1 KR 2007004076 W KR2007004076 W KR 2007004076W WO 2009028743 A1 WO2009028743 A1 WO 2009028743A1
Authority
WO
WIPO (PCT)
Prior art keywords
leak
chamber
real time
occurrence
plasma
Prior art date
Application number
PCT/KR2007/004076
Other languages
English (en)
Inventor
Bong-Joo Woo
Dong-Seok Lee
Joo-Young Moon
Tae-Dong Kim
Hak-Kwon Kim
Byoung-Chan Park
Kwang-Tae Kim
Original Assignee
Semisysco Co., Ltd.
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 Semisysco Co., Ltd. filed Critical Semisysco Co., Ltd.
Priority to CN2007800521346A priority Critical patent/CN101663735B/zh
Priority to PCT/KR2007/004076 priority patent/WO2009028743A1/fr
Publication of WO2009028743A1 publication Critical patent/WO2009028743A1/fr

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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
    • H01J37/32972—Spectral analysis
    • C—CHEMISTRY; METALLURGY
    • C23—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
    • C23C—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
    • C23C16/00—Chemical coating by decomposition of gaseous compounds, without leaving reaction products of surface material in the coating, i.e. chemical vapour deposition [CVD] processes
    • C23C16/44—Chemical coating by decomposition of gaseous compounds, without leaving reaction products of surface material in the coating, i.e. chemical vapour deposition [CVD] processes characterised by the method of coating
    • C23C16/4401—Means for minimising impurities, e.g. dust, moisture or residual gas, in the reaction chamber
    • C—CHEMISTRY; METALLURGY
    • C23—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; CHEMICAL SURFACE TREATMENT; DIFFUSION TREATMENT OF METALLIC MATERIAL; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL; INHIBITING CORROSION OF METALLIC MATERIAL OR INCRUSTATION IN GENERAL
    • C23C—COATING METALLIC MATERIAL; COATING MATERIAL WITH METALLIC MATERIAL; SURFACE TREATMENT OF METALLIC MATERIAL BY DIFFUSION INTO THE SURFACE, BY CHEMICAL CONVERSION OR SUBSTITUTION; COATING BY VACUUM EVAPORATION, BY SPUTTERING, BY ION IMPLANTATION OR BY CHEMICAL VAPOUR DEPOSITION, IN GENERAL
    • C23C16/00—Chemical coating by decomposition of gaseous compounds, without leaving reaction products of surface material in the coating, i.e. chemical vapour deposition [CVD] processes
    • C23C16/44—Chemical coating by decomposition of gaseous compounds, without leaving reaction products of surface material in the coating, i.e. chemical vapour deposition [CVD] processes characterised by the method of coating
    • C23C16/52—Controlling or regulating the coating process
    • 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

Definitions

  • the present invention relates to technology of monitoring a chamber in real time during a process of holding, depositing (CVD) or etching a substrate to manufacture a semiconductor substrate or a flat display substrate, and more particularly, to a real time chamber monitoring method using an intelligent algorithm capable of comparing a detection signal with a time series normal pattern having periodicity through an integral process using a time series neural network algorithm (or an intelligent algorithm) to detect a leak from the chamber in real time when the chamber leaks to introduce external air into the chamber, and monitoring plasma emission from the chamber in real time during a process of holding, depositing, or etching a substrate, and thus, a specific spectrum of nitrogen (N ), oxygen (O ), argon (Ar), and so on, among a plasma spectrum is detected through the monitoring.
  • N nitrogen
  • O oxygen
  • Ar argon
  • the layers are typically formed by chemical vapor deposition (CVD), physical vapor deposition (PVD), or oxidation and nitration processes.
  • CVD chemical vapor deposition
  • PVD physical vapor deposition
  • oxidation and nitration processes are typically formed by chemical vapor deposition (CVD), physical vapor deposition (PVD), or oxidation and nitration processes.
  • a reactive gas is decomposed to deposit a material layer onto a substrate.
  • a target is sputtered to deposit a material onto a substrate.
  • an oxidation layer or a nitration layer may be a silicon dioxide layer or a silicon nitride layer formed on the substrate.
  • a patterned mask layer formed of photoresist or a hard mask is formed on the substrate by a photolithography method, such that an exposed part of the substrate is etched by an activated gas such as Cl , HBr or BCl .
  • a leak detection method may use a conventional time series neural network method.
  • the conventional time series neural network method includes obtaining a level value of an optical emission spectroscopy (OES) signal of a normal pattern of plasma and a level value of an OES signal of an abnormal pattern of plasma, in which a leak occurs, and determining the leak through a time series analysis method.
  • OES optical emission spectroscopy
  • the conventional time series neural network system can perform the analysis only when the numbers of data values constituting the normal pattern and the abnormal pattern are equal to each other.
  • a real time chamber monitoring method using an intelligent algorithm capable of comparing a detection signal with a time series normal pattern having periodicity through an integral process using a time series neural network algorithm (or intelligence algorithm) to detect a leak from the chamber in real time when the chamber leaks to introduce external air into the chamber in a state that plasma emission from the chamber is monitored in real time during a process of holding, depositing, or etching a substrate, and thus a specific spectrum of nitrogen (N ), oxygen (O ), argon (Ar), and so on, among a plasma spectrum can be detected through the monitoring.
  • a time series neural network algorithm or intelligence algorithm
  • the method includes previously obtaining normal pattern values of substrates during the substrate holding, depositing or etching process of semiconductor substrates, substituting the previously obtained normal pattern value with an actual pattern value of each substrate obtained by the substrate holding, depositing or etching process while sequentially performing the substrate holding, depositing or etching process of the substrates on the basis of the normal pattern value, and comparing the substituted pattern value , thereby determining occurrence of the leak in real time just after each substrate holding, depositing or etching process of a single substrate is completed.
  • a real time chamber monitoring method using an intelligent algorithm including: a first step of previously and sequentially obtaining normal pattern values obtained by of integral values of an optical emission spectroscopy (OES) signal of plasma in a chamber during a substrate holding, depositing or etching process of a certain number of substrates in a state that there is no leak in the chamber; a second step of substituting the previously obtained normal pattern with the OES signal integral value of the plasma upon occurrence of the leak in the first step when a specific spectrum due to the leak is detected from a plasma spectrum through monitoring in a state that a certain number of substrates pass through the substrate holding, depositing or etching process; and a third step of comparing the substituted pattern value with the normal pattern value through a time series neural network algorithm to detect the leak and determine a process error.
  • OES optical emission spectroscopy
  • the integral value may be substituted for the nth previously obtained normal pattern value.
  • the present invention uses the method of obtaining a normal pattern value through a time series neural network algorithm for leak detection, substituting the normal pattern value with a value obtained by performing a process just after obtaining the normal pattern values, without obtaining all data of abnormal patterns in real time, and inspecting the pattern. Therefore, it is possible to determine occurrence of a leak in real time just after a substrate holding, depositing or etching process of a single wafer and check the occurrence of a leak from a chamber in real time depending on the determination through a leak detector without equipment shutdown. In addition, when the leak occurs from the chamber, a determination time can be reduced and thus productivity can be improved. Further, when cracks are generated in the chamber during a high temperature HDP CVD process, it is possible to readily determine the cracks and prevent damage to the chamber and accidents from occurring due to the damage.
  • FIG. 1 is a schematic view of a leak detection system in accordance with an exemplary embodiment of the present invention
  • FIG. 2 is a view showing a neural network structure adapted to a time series pattern analysis method using a neural network algorithm in accordance with an exemplary embodiment of the present invention
  • FIG. 3 is a flowchart of real time leak analysis through time series neural network analysis in accordance with an exemplary embodiment of the present invention
  • FIG. 4 is a graph showing normal pattern values as a reference having periodicity, in which there is no leak, used for time series analysis in accordance with an exemplary embodiment of the present invention
  • FIG. 5 is a graph showing abnormal pattern values having non-periodicity, in which a leak occurs, in accordance with an exemplary embodiment of the present invention.
  • FIG. 6 is a graph in which the abnormal pattern values are substituted for the normal pattern values in order to perform a time series neural network algorithm having a non-periodic pattern in accordance with an exemplary embodiment of the present invention. Best Mode for Carrying Out the Invention
  • FIG. 1 is a schematic view of a leak detection system in accordance with an exemplary embodiment of the present invention
  • FIG. 2 is a view showing a neural network structure adapted to a time series pattern analysis method using a neural network algorithm in accordance with an exemplary embodiment of the present invention
  • FIG. 3 is a flowchart of real time leak analysis through time series neural network analysis in accordance with an exemplary embodiment of the present invention.
  • FIG. 4 is a graph showing normal pattern values as a reference having periodicity, in which there is no leak, used for time series analysis in accordance with an exemplary embodiment of the present invention
  • FIG. 5 is a graph showing abnormal pattern values having non-periodicity, in which a leak occurs, in accordance with an exemplary embodiment of the present invention
  • FIG. 6 is a graph in which the abnormal pattern values are substituted for the normal pattern values in order to perform a time series neural network algorithm having a non-periodic pattern in accordance with an exemplary embodiment of the present invention.
  • a leak detection part 30 for monitoring plasma emission from a chamber 20 during a substrate holding, depositing or etching process of equipment using plasma 10 in a vacuum to detect occurrence of a leak from the chamber 20.
  • an algorithm related to a time series neural network method for detecting a leak in real time is installed in the leak detection part 30.
  • the leak detection part 30, in which the time series neural network algorithm is installed integrates a level value of an OES signal, and substituting the previously obtained normal pattern value with the integral value, thereby detecting a leak in the chamber of each process in real time.
  • the real time leak detection is performed through the time series neural network algorithm using the normal patterns, when fifteen values are collected to constitute a pattern having the fifteen values.
  • the normal pattern values are previously and sequentially obtained using OES signals of plasma 10 in the chamber 20 and integrated during a substrate holding, depositing or etching process of a certain number (e.g., 15) of substrates in a state that no leak occurs in the chamber 20, like a concealment class Ml of FIG. 2.
  • the normal pattern values of FIG. 3 are stored in the concealment class Ml, a memory in the leak detection part 30.
  • the leak detection part 30 searches the normal pattern value corresponding to the process from the concealment class Ml whenever each substrate's process is completed.
  • the substrate holding, depositing, or etching process of the certain number of substrates S(I), S(2), ...., S(9), ...., S(15) is performed.
  • the leak detection part 30 integrates the plasma OES signal on the leak occurrence and substitutes the previously obtained normal pattern value with Pl the integral value P2 of the concealment class Ml.
  • the leak detection part 30 substitutes the normal pattern value Pl with the integral value P2 generated after performing a process of each substrate on the leak occurrence in the chamber 20, and compares the substituted pattern value with the normal pattern value Pl through the time series neural network algorithm to detect occurrence of the leak in real time.
  • the integral value of the plasma OES signal which is generated at the same time that the first substrate process is completed, is substituted for the previously obtained first normal pattern.
  • the integral value of the plasma OES signal which is generated at the same time the second substrate process is completed, is substituted for the previously obtained second normal pattern, and the above step is repeated to the fifteenth process through a time series algorithm.

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  • Chemical & Material Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Metallurgy (AREA)
  • Materials Engineering (AREA)
  • Mechanical Engineering (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Organic Chemistry (AREA)
  • General Chemical & Material Sciences (AREA)
  • Plasma & Fusion (AREA)
  • Analytical Chemistry (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Drying Of Semiconductors (AREA)
  • Chemical Vapour Deposition (AREA)

Abstract

L'invention porte sur un procédé de surveillance d'une chambre en temps réel pendant un procédé de maintien, de dépôt ou de gravure d'un substrat pour fabriquer un substrat semi-conducteur ou un substrat d'écran plat. Le procédé de détection de fuite par l'intermédiaire d'un procédé d'analyse par réseau neural en série temporel utilisant des informations de spectroscopie d'émission optique (OES) sur plasma consiste à détecter, sur la base du signal OES, si un spectre spécifique tel que l'azote, l'oxygène, l'argon, etc., est généré ou non à partir d'un spectre de plasma en raison d'intrusion d'air dans la chambre suite à une fuite, et à déterminer l'apparition de la fuite par le procédé d'analyse par réseau neural en série temporel à l'aide d'un motif de signal OES normal lors d'absence de fuite et d'un motif de signal OES anormal du spectre spécifique lors de l'apparition de la fuite. Ainsi, il est possible de déterminer l'apparition d'une fuite juste après un procédé de maintien, de dépôt ou de gravure de substrat d'une tranche unique, et de vérifier l'apparition d'une fuite d'une chambre en temps réel en fonction du signal de détection sans arrêt de l'appareil, par contraste avec la détection de fuite via un détecteur de fuite classique après un arrêt de l'appareil. En outre, lorsque la fuite se produit depuis la chambre, un temps de détermination peut être réduit et ainsi une productivité peut être améliorée. En outre, lorsque des craquelures sont générées dans la chambre pendant un processus de dépôt chimique en phase vapeur par plasma haute densité (HDP CVD) haute température, il est possible de déterminer facilement les craquelures et de prévenir un endommagement de la chambre et des accidents dus aux endommagements.
PCT/KR2007/004076 2007-08-24 2007-08-24 Procédé de surveillance de chambre en temps réel utilisant un algorithme d'intelligence artificielle WO2009028743A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CN2007800521346A CN101663735B (zh) 2007-08-24 2007-08-24 使用智能算法的实时腔室监控方法
PCT/KR2007/004076 WO2009028743A1 (fr) 2007-08-24 2007-08-24 Procédé de surveillance de chambre en temps réel utilisant un algorithme d'intelligence artificielle

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/KR2007/004076 WO2009028743A1 (fr) 2007-08-24 2007-08-24 Procédé de surveillance de chambre en temps réel utilisant un algorithme d'intelligence artificielle

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WO2009028743A1 true WO2009028743A1 (fr) 2009-03-05

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015172130A1 (fr) * 2014-05-09 2015-11-12 Varian Semiconductor Equipment Associates, Inc. Appareil et procédé de commande dynamique d'énergie et d'angle de faisceau d'ions

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Publication number Priority date Publication date Assignee Title
CN102373445B (zh) * 2010-08-25 2014-01-08 无锡华润上华半导体有限公司 化学气相淀积反应腔中漏率的监控方法
CN102403191B (zh) * 2010-09-14 2014-05-21 中微半导体设备(上海)有限公司 一种反应腔漏气检测方法及真空反应器控制方法
CN102157412B (zh) * 2011-01-07 2012-10-10 清华大学 一种基于光学发射谱信号的等离子刻蚀过程故障检测方法
US11039527B2 (en) * 2019-01-28 2021-06-15 Mattson Technology, Inc. Air leak detection in plasma processing apparatus with separation grid
CN113780522B (zh) * 2021-08-27 2023-09-08 核工业西南物理研究院 基于深度神经网络的托卡马克等离子体大破裂预测算法

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US6117243A (en) * 1996-07-24 2000-09-12 Schott Glaswerke CVD device for coating the inside of hollow bodies
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EP0942453A3 (fr) * 1998-03-11 2001-02-07 Axcelis Technologies, Inc. Contrôle des constituants d'un plasma par spectroscopie d'émission
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US5658423A (en) * 1995-11-27 1997-08-19 International Business Machines Corporation Monitoring and controlling plasma processes via optical emission using principal component analysis
US6117243A (en) * 1996-07-24 2000-09-12 Schott Glaswerke CVD device for coating the inside of hollow bodies
KR20020054479A (ko) * 2000-12-28 2002-07-08 이순종 플라즈마 챔버의 공정 상태 관찰방법

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015172130A1 (fr) * 2014-05-09 2015-11-12 Varian Semiconductor Equipment Associates, Inc. Appareil et procédé de commande dynamique d'énergie et d'angle de faisceau d'ions
US9336998B2 (en) 2014-05-09 2016-05-10 Varian Semiconductor Equipment Associates, Inc. Apparatus and method for dynamic control of ion beam energy and angle

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Publication number Publication date
CN101663735A (zh) 2010-03-03
CN101663735B (zh) 2011-07-06

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