JPWO2019182913A5 - - Google Patents

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JPWO2019182913A5
JPWO2019182913A5 JP2020550765A JP2020550765A JPWO2019182913A5 JP WO2019182913 A5 JPWO2019182913 A5 JP WO2019182913A5 JP 2020550765 A JP2020550765 A JP 2020550765A JP 2020550765 A JP2020550765 A JP 2020550765A JP WO2019182913 A5 JPWO2019182913 A5 JP WO2019182913A5
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処理システムを使用して、ワークピースを処理して前記ワークピース上に電子デバイスを製作するための製造プラットフォームであって、前記製造プラットフォームは、
共通の製造プラットフォーム上にホストされた複数の処理モジュールであって、前記処理モジュールは、処理シーケンスの一部としての処理工程内で、ワークピース上の材料を操作するように構成されており、
前記複数の処理モジュールは第１モジュール及び第２モジュールを含み、前記第１モジュール及び前記第２モジュールは、プロセスシーケンス内の異なるプロセスを容易にする、複数の処理モジュールと、
前記共通の製造プラットフォーム上にホストされた少なくとも１つの測定モジュールであって、前記測定モジュールは、前記ワークピースが前記共通の製造プラットフォームの処理モジュール内で処理される前又は後の少なくとも一方に、前記ワークピースの属性に関連するデータを測定するように動作可能な検査システムを含む、少なくとも１つの測定モジュールと、
前記共通の製造プラットフォーム上にホストされ、前記処理モジュールと前記少なくとも１つの測定モジュールとの間で前記ワークピースを移動させるように構成されている少なくとも１つのワークピース搬送モジュールと、
前記共通の製造プラットフォーム上に少なくとも部分的にホストされ、前記測定モジュールと結合された能動的阻止制御システムであって、前記能動的阻止制御システムは、不適合を検出するために、前記ワークピースの属性に関連する前記測定の測定データを処理するように構成されており、不適合が検出された場合に、少なくとも部分的に、前記プロセスシーケンスの上流及び／又は下流の処理モジュール内で前記ワークピースの補正処理を実施するように構成されており、
前記能動的阻止制御システムは、更に、前記処理シーケンスにおける前記ワークピースの移動及び処理を制御するように構成されている、能動的阻止制御システムと、
を含む、能動的阻止制御システムと、を含み、
前記共通の製造プラットフォーム、前記処理モジュール、及び前記測定モジュールは、制御された環境内で動作し、前記ワークピース搬送モジュールは、前記制御された環境を離れることなく、前記処理シーケンス内の前記複数の処理モジュールと前記測定モジュールとの間で前記ワークピースを搬送するように構成されている、
製造プラットフォーム。 A manufacturing platform for processing a workpiece and manufacturing an electronic device on the workpiece using a processing system .
A plurality of processing modules hosted on a common manufacturing platform, said processing module being configured to manipulate materials on a workpiece within a processing process as part of a processing sequence.
The plurality of processing modules include a first module and a second module, wherein the first module and the second module include a plurality of processing modules and a plurality of processing modules that facilitate different processes in a process sequence.
At least one measurement module hosted on the common manufacturing platform, wherein the measurement module is at least one before or after the workpiece is processed in the processing module of the common manufacturing platform. With at least one measurement module, including an inspection system that can operate to measure data related to the attributes of the workpiece.
At least one workpiece transfer module hosted on the common manufacturing platform and configured to move the workpiece between the processing module and the at least one measuring module.
An active arrest control system that is at least partially hosted on the common manufacturing platform and coupled with the measurement module, the active arrest control system being an attribute of the workpiece to detect nonconformities. It is configured to process the measurement data of the measurement related to, and if a nonconformity is detected, at least in part, correct the workpiece in the processing module upstream and / or downstream of the process sequence. It is configured to carry out the process and
The active deterrence control system further comprises an active deterrence control system configured to control the movement and processing of the workpiece in the processing sequence.
Including active deterrence control system, including,
The common manufacturing platform, the processing module, and the measuring module operate in a controlled environment, and the workpiece transfer module has the plurality of in the processing sequence without leaving the controlled environment. It is configured to transport the workpiece between the processing module and the measuring module.
Manufacturing platform. 前記第１モジュールは膜形成モジュールであり、前記第２モジュールはエッチングモジュールである、請求項１に記載の製造プラットフォーム。 The manufacturing platform according to claim 1 , wherein the first module is a film forming module and the second module is an etching module. 前記能動的阻止制御システムは、前記測定データからデータパターンを抽出して分類し、不適合の存在を予測するためのパターン認識コンポーネントを含む、請求項１に記載の製造プラットフォーム。 The manufacturing platform according to claim 1 , wherein the active deterrence control system includes a pattern recognition component for extracting and classifying data patterns from the measurement data and predicting the presence of nonconformities. 前記パターン認識コンポーネントは、深層学習アーキテクチャを含む、請求項３に記載の製造プラットフォーム。 The manufacturing platform of claim 3 , wherein the pattern recognition component comprises a deep learning architecture. 前記パターン認識コンポーネントは、抽出したデータパターンを前記ワークピースの学習した属性と相関させる、請求項４に記載の製造プラットフォーム。 The manufacturing platform according to claim 4 , wherein the pattern recognition component correlates the extracted data pattern with the learned attributes of the workpiece. 前記学習した属性は、前記ワークピースの欠陥を含む、請求項５に記載の製造プラットフォーム。 The manufacturing platform according to claim 5 , wherein the learned attributes include defects in the workpiece. 前記欠陥は、属性の許容範囲外条件を含み、前記属性は、厚さ、クリティカルディメンション、表面粗さ、膜若しくは表面組成、フィーチャプロファイル、パターンエッジ配置、ボイド、選択性の喪失、不均一性の程度、又はローディング効果、又はこれらの２つ以上のいずれかの組み合わせを含む、請求項６に記載の製造プラットフォーム。 The defect includes conditions outside the tolerance of the attribute, which are thickness, critical dimension, surface roughness, film or surface composition, feature profile, pattern edge placement, voids, loss of selectivity, non-uniformity. The manufacturing platform according to claim 6 , comprising a degree, or a loading effect, or a combination of any one or more of these. 前記能動的阻止制御システムは、前記不適合が存在する前記ワークピースの領域を示すためのディスプレイコンポーネントを更に含む、請求項１に記載の製造プラットフォーム。 The manufacturing platform according to claim 1 , wherein the active deterrence control system further comprises a display component for indicating the area of the workpiece in which the nonconformity is present. 前記能動的阻止制御システムは、前記ワークピース上の２つ以上の位置で測定されたデータの相関に基づいて不適合の存在を予測するための相関コンポーネントを含む、請求項１に記載の製造プラットフォーム。 The manufacturing platform according to claim 1 , wherein the active deterrence control system comprises a correlation component for predicting the presence of nonconformity based on the correlation of data measured at two or more locations on the workpiece. 前記能動的阻止制御システムは、自律学習コンポーネントを含み、前記自律学習コンポーネントは、前記測定データを受け取り、（ｉ）前記測定データ及び前記プロセスシーケンスの性能を特徴付けること、及び（ｉｉ）不適合が存在する場合に前記プロセスシーケンスを補正するためのアクションプランを決定すること、に少なくとも部分的に基づいて知識を生成する、請求項１に記載の製造プラットフォーム。 The active arrest control system includes an autonomous learning component, which receives the measurement data and (i) characterizes the performance of the measurement data and the process sequence, and (ii) there is a nonconformity. The manufacturing platform according to claim 1 , wherein the knowledge is generated, at least in part, in determining an action plan for amending the process sequence in case. 前記自律学習コンポーネントは、教師あり学習、クラスタリング、次元削減、構造化予測、異常検知、若しくは強化学習のうちの少なくとも１つ、又はこれらの２つ以上のいずれかの組み合わせを実行する、請求項１０に記載の製造プラットフォーム。 10. The autonomous learning component performs at least one of supervised learning, clustering, dimensionality reduction, structured prediction, anomaly detection, or reinforcement learning, or any combination of two or more of these. The manufacturing platform described in. 前記能動的阻止制御システムは、
前記測定データを受け取る相互作用コンポーネントであって、前記相互作用コンポーネントは、前記測定データをパッケージし、パッケージされたデータを伝達するアダプタコンポーネントを含む、相互作用コンポーネントと、
前記パッケージされたデータを受け取り、前記パッケージされたデータ及び前記プロセスシーケンスの性能を特徴付ける知識を生成する自律学習コンポーネントと、
を含む、請求項１に記載の製造プラットフォーム。 The active deterrence control system is
An interaction component that receives said measurement data, the interaction component comprises an adapter component that packages the measurement data and conveys the packaged data.
An autonomous learning component that receives the packaged data and generates the knowledge that characterizes the packaged data and the performance of the process sequence.
The manufacturing platform according to claim 1 . 前記自律学習コンポーネントは、
前記パッケージされたデータを処理する処理プラットフォームであって、前記処理プラットフォームは、前記パッケージされたデータ上で動作する一連の機能ユニットを含み、前記一連の機能ユニットは、
前記パッケージされたデータを分析し、前記プロセスシーケンスのプロセス目標に少なくとも部分的に基づいて、実施すべきアクションを推論する適応的推論エンジンと、
データ又はコンテキスト変化のうちの１つに少なくとも部分的に基づいて前記プロセス目標を進化させる目標コンポーネントと、
前記知識を記憶するメモリプラットフォームであって、前記メモリプラットフォームは、長期メモリ、短期メモリ、及びエピソードメモリを含むメモリの階層を含み、前記長期メモリは、エンティティ、関係、又は手続きのうちの少なくとも１つを含む一連の概念を記憶し、前記一連の概念のうちの一概念は、前記プロセスシーケンスの現在の状態に対する概念の関連性を示す第１の数値的属性と、前記概念を使用する難易度を示す第２の数値的属性とを含む、メモリプラットフォームと、
を含む、処理プラットフォーム
を含む、請求項１２に記載の製造プラットフォーム。 The autonomous learning component is
A processing platform that processes the packaged data, wherein the processing platform includes a set of functional units that operate on the packaged data.
An adaptive inference engine that analyzes the packaged data and infers actions to be taken, at least in part, based on the process goals of the process sequence.
A goal component that evolves the process goal based at least in part on one of the data or context changes.
A memory platform for storing the knowledge, the memory platform including a hierarchy of memories including long-term memory, short-term memory, and episode memory, wherein the long-term memory is at least one of an entity, relationship, or procedure. Memorize a set of concepts, one of which is a first numerical attribute that indicates the relevance of the concept to the current state of the process sequence, and the difficulty of using the concept. A memory platform, including the second numerical attribute shown, and
12. The manufacturing platform according to claim 12 , comprising a processing platform. インタラクティブコンポーネントは、更に、前記複数の前記処理モジュールのうちの１つ以上からモジュール診断データを受け取り、前記パッケージされたデータを準備するときに、前記モジュール診断データを前記測定データと共にパッケージする、請求項１３に記載の製造プラットフォーム。 The interactive component further claims to receive module diagnostic data from one or more of the plurality of processing modules and package the module diagnostic data with the measurement data when preparing the packaged data. 13. The manufacturing platform according to 13. 外部アクターとのデータ交換を容易にする相互作用マネージャを前記相互作用コンポーネントが更に含む、請求項１３に記載の製造プラットフォーム。 13. The manufacturing platform of claim 13 , wherein the interaction component further comprises an interaction manager that facilitates data exchange with external actors. パッケージされたデータ又は前記外部アクターと交換される前記データのうちの少なくとも１つは、トレーニングデータを含む、請求項１５に記載の製造プラットフォーム。 15. The manufacturing platform of claim 15 , wherein at least one of the packaged data or the data exchanged with said external actors comprises training data. 前記トレーニングデータは、タスクに関連するモジュールプロセス又は変数の識別のうちの少なくとも１つ、前記タスクに関連する２つ以上のモジュールプロセス又は変数間の機能的関係、因果グラフであって、前記タスクに関連し、前記因果グラフ中に存在する一連のモジュールプロセス又は変数に関連する一連の先験的確率と、前記タスクに関連し、前記因果グラフ中に存在する１つ以上のモジュールプロセス又は変数に関連する一連の条件付き確率とを含む因果グラフ、又は前記プロセスシーケンスの挙動を記述する一連のパラメータを更に含む、請求項１６に記載の製造プラットフォーム。 The training data is at least one of the identification of a module process or variable related to the task, a functional relationship between two or more module processes or variables related to the task, a causal graph, and the task. A set of a priori probabilities associated with a set of modular processes or variables that are related and present in the causal graph and related to one or more modular processes or variables that are related to the task and present in the causal graph. 16. The manufacturing platform according to claim 16 , further comprising a set of causal graphs, including a set of conditional probabilities, or a set of parameters describing the behavior of the process sequence. 共通の製造プラットフォーム上にホストされた複数の処理モジュールを通してワークピースを処理することであって、前記ワークピースは複数の電子デバイスを含み、前記処理することは、少なくとも１つの膜形成プロセスと少なくとも１つのエッチングプロセス又は膜処理プロセスとを有するプロセスシーケンスを実施することを含む、ことと、
前記共通の製造プラットフォーム上で前記ワークピースの製作測定データを収集することであって、前記製作測定データは、前記共通の製造プラットフォーム上で実施される前記プロセスシーケンスの少なくとも一部に基づいた前記ワークピースの属性の測定値である、ことと、
前記プロセスシーケンスの少なくとも一部のプロセスを実施している前記処理モジュールのうちの少なくとも１つで処理されているワークピースからその場プロセス計測データを収集することと、
前記ワークピースの不適合を、前記収集された製作測定データ及び前記その場プロセス計測データのうちの少なくとも１つに基づいて検出することと、
不適合が検出された場合、前記製作測定データ及び前記その場プロセス計測データのうちの少なくとも１つが収集された後に、前記共通の製造プラットフォーム上の前記プロセスシーケンス内の前記ワークピースの補正処理を実行するために、前記プロセスシーケンスに対して能動的阻止を実施することと、
を含み、
前記共通の製造プラットフォームは、第１搬送測定モジュール及び第２搬送測定モジュールを備え、
前記第１搬送測定モジュール及び前記第２搬送測定モジュールの各々は、それぞれ測定モジュールを備え、
前記第１搬送測定モジュールは第１パススルーチャンバを介して前記第２搬送測定モジュールに接続されており、
前記第２搬送測定モジュールは第２パススルーチャンバを介してワークピースバッチ／デバッチモジュールに接続されており、
前記複数の処理モジュールの各処理モジュールは、前記第１搬送測定モジュール及び前記第２搬送測定モジュールのうちの１つに接続されている、
方法。 Processing a workpiece through multiple processing modules hosted on a common manufacturing platform, said workpiece comprising multiple electronic devices, said processing is at least one membrane forming process and at least one. Including performing a process sequence having one etching process or film treatment process, and
Collecting fabrication measurement data for the workpiece on the common manufacturing platform, where the fabrication measurement data is based on at least a portion of the process sequence performed on the common manufacturing platform. It is a measurement of the attributes of the piece, and
Collecting in-situ process measurement data from a workpiece being processed by at least one of the processing modules performing at least a portion of the process of the process sequence.
Detecting the work piece nonconformity based on at least one of the collected fabrication measurement data and the in-situ process measurement data.
If a nonconformity is detected, after at least one of the production measurement data and the in-situ process measurement data has been collected, a correction process for the workpiece in the process sequence on the common manufacturing platform is performed. In order to do so, it is necessary to carry out active blocking of the process sequence.
Including
The common manufacturing platform includes a first transport measurement module and a second transport measurement module.
Each of the first transfer measurement module and the second transfer measurement module includes a measurement module.
The first transfer measurement module is connected to the second transfer measurement module via the first pass-through chamber.
The second transfer measurement module is connected to the workpiece batch / debatch module via the second pass-through chamber.
Each processing module of the plurality of processing modules is connected to one of the first transfer measurement module and the second transfer measurement module .
Method. 前記補正処理は、前記共通の製造プラットフォーム上の処理モジュールの処理を変化させること、又は前記ワークピースを廃棄すること、又は前記不適合についてオペレータに通知することのうちの少なくとも１つを含む、請求項１８に記載の方法。 The amendment process comprises at least one of altering the processing of the processing module on the common manufacturing platform, disposing of the workpiece, or notifying the operator of the nonconformity. 18. The method according to 18. その場プロセス計測データを前記収集することは、前記プロセスシーケンスのプロセス工程の最中に前記処理モジュール内その場で行われる、請求項１９に記載の方法。 19. The method of claim 19 , wherein collecting the in-situ process measurement data is performed in-situ in the processing module during the process process of the process sequence. 前記補正処理は、前記その場プロセス計測データが取得されたのと同じ処理モジュール内その場で行われる、請求項２０に記載の方法。 The method according to claim 20 , wherein the correction process is performed in-situ in the same processing module in which the in-situ process measurement data is acquired. 製作測定データを前記収集することは、前記処理モジュールの現場外、且つ前記共通の製造プラットフォーム内で行われる、請求項１８に記載の方法。 18. The method of claim 18 , wherein the collection of manufacturing measurement data is performed outside the field of the processing module and within the common manufacturing platform. 前記補正処理は、前記その場プロセス計測データが収集された前記処理モジュールの現場外、且つ前記共通の製造プラットフォーム内で行われる、請求項２０に記載の方法。 The method of claim 20 , wherein the correction process is performed outside the field of the process module from which the in-situ process measurement data is collected and within the common manufacturing platform. １つ以上の処理モジュールの動作に関するプロセスパラメータデータ、又は前記共通の製造プラットフォームの動作に関するプラットフォーム性能データのうちの少なくとも１つを収集することと、
製作測定データ、前記プロセスパラメータデータ、又は前記プラットフォーム性能データのうちの少なくとも１つに基づいて、後続のワークピースのプロセスシーケンスに対する能動的阻止を実施することと、
を更に含む、請求項１８に記載の方法。 Collecting at least one of the process parameter data for the operation of one or more processing modules or the platform performance data for the operation of the common manufacturing platform.
Performing active blocking of subsequent workpiece process sequences based on at least one of the production measurement data, the process parameter data, or the platform performance data.
18. The method of claim 18 . プロセスシーケンスに対する能動的阻止は、前記不適合を補正するために前記ワークピースを改善プロセスシーケンスに曝すことを含む、請求項１８に記載の方法。 18. The method of claim 18 , wherein active blocking of the process sequence comprises exposing the workpiece to an improved process sequence to compensate for the nonconformity. 前記改善プロセスシーケンスは、前記ワークピースを洗浄すること、前記ワークピースから膜を除去すること、又は前記ワークピースから前記膜の一部を除去することのうちの少なくとも１つを含む、請求項２５に記載の方法。 25. The improvement process sequence comprises at least one of cleaning the workpiece, removing the membrane from the workpiece, or removing a portion of the membrane from the workpiece. The method described in. 前記改善プロセスシーケンスは、前記共通の製造プラットフォーム上で実施される、請求項２５に記載の方法。 25. The method of claim 25 , wherein the improvement process sequence is performed on the common manufacturing platform. 前記改善プロセスシーケンスは、前記共通の製造プラットフォームの外部で実施される、請求項２６に記載の方法。 26. The method of claim 26 , wherein the improvement process sequence is performed outside of the common manufacturing platform. 前記改善プロセスシーケンスは、極低温冷却されたスプレーを用いて前記ワークピースを洗浄することを含む、請求項２５に記載の方法。 25. The method of claim 25 , wherein the improvement process sequence comprises cleaning the workpiece with a cryocooled spray. プロセスシーケンスに対する能動的阻止は、検出された前記不適合を修正するために、前記ワークピースを調整プロセスシーケンスに曝すことを含む、請求項１８に記載の方法。 18. The method of claim 18 , wherein active blocking of the process sequence comprises exposing the workpiece to the conditioning process sequence in order to correct the detected nonconformity. 前記調整プロセスシーケンスは、１つ以上のプロセス条件を、不適合が検出された製作測定データ又はその場プロセス計測データに少なくとも部分的に基づいて制御することを含む、請求項３０に記載の方法。 30. The method of claim 30 , wherein the adjustment process sequence comprises controlling one or more process conditions based at least in part on production measurement data or in-situ process measurement data in which nonconformity is detected. 前記調整プロセスシーケンスは、処理モジュールの１つ以上のプロセス条件を、前記不適合の補正に対応するモデルに少なくとも部分的に基づいて制御することを含む、請求項３０に記載の方法。 30. The method of claim 30 , wherein the adjustment process sequence comprises controlling one or more process conditions of the processing module based at least in part on the model corresponding to the correction of the nonconformity. 前記調整プロセスシーケンスは、膜形成プロセス、エッチングプロセス、又は膜処理プロセス間で交互させることを含む、請求項３０に記載の方法。 30. The method of claim 30 , wherein the conditioning process sequence comprises alternating between film forming processes, etching processes, or film processing processes. 能動的阻止を実施した後、前記能動的阻止に基づいた前記不適合に対する影響を決定するために、前記ワークピースの製作測定データを収集することを更に含む、請求項１８に記載の方法。 18. The method of claim 18 , further comprising collecting fabrication measurement data of the workpiece to determine the effect on the nonconformity based on the active arrest after performing the active arrest. 前記決定された前記不適合に対する影響に基づいて、ワークピースに対する前記プロセスシーケンスを継続させることを更に含む、請求項３４に記載の方法。 34. The method of claim 34 , further comprising continuing the process sequence for the workpiece based on the effect on the determined nonconformity. 前記製作測定データは、以下、層厚さ、層のコンフォーマル性、層の被覆範囲、層のプロファイル、エッジ配置位置、エッジ配置誤差（ＥＰＥ）、クリティカルディメンション（ＣＤ）、ブロッククリティカルディメンション（ＣＤ）、グリッドクリティカルディメンション（ＣＤ）、ライン幅粗さ（ＬＷＲ）、ラインエッジ粗さ（ＬＥＲ）、ブロックＬＷＲ、グリッドＬＷＲ、選択的堆積に関する特性、選択性エッチングに関する特性、物性、光学的性質、電気的性質、屈折率、抵抗、電流、電圧、温度、質量、速度、加速度、又はワークピース上に製作される電子デバイスに関連するこれらの組み合わせのうちの１つ以上を含む属性に関連する測定値である、請求項１８に記載の方法。 The production measurement data are described below as follows: layer thickness, layer conformability, layer coverage, layer profile, edge placement position, edge placement error (EPE), critical dimension (CD), block critical dimension (CD). , Grid Critical Dimension (CD), Line Width Roughness (LWR), Line Edge Roughness (LER), Block LWR, Grid LWR, Selective Accumulation Properties, Selective Etching Properties, Physical Properties, Optical Properties, Electrical Measurements related to an attribute that includes one or more of these combinations related to properties, refraction, resistance, current, voltage, temperature, mass, velocity, acceleration, or electronic devices manufactured on the workpiece. The method according to claim 18 . プロセスパラメータデータは、前記処理モジュールで実行される１つ以上のプロセス条件の表示を含む、請求項２４に記載の方法。 24. The method of claim 24 , wherein the process parameter data comprises displaying one or more process conditions executed by the processing module. 前記プロセス条件は、プラズマ密度、プラズマ均一性、プラズマ温度、エッチングレート、エッチング均一性、堆積速度、及び／又は堆積均一性のうちの少なくとも１つに基づいている、請求項３７に記載の方法。 37. The method of claim 37 , wherein the process conditions are based on at least one of plasma density, plasma uniformity, plasma temperature, etching rate, etching uniformity, deposition rate, and / or deposition uniformity. 前記プロセス条件は、前記処理モジュール内に配置されたプラズマ源に印加されるエネルギーの振幅、周波数、変調のうちの少なくとも１つを含む、請求項３７に記載の方法。 37. The method of claim 37 , wherein the process conditions include at least one of an amplitude, frequency, or modulation of energy applied to a plasma source located within the processing module. 前記プロセス条件は、前記プロセスシーケンス中に前記処理モジュールに流入させるガス流量を含む、請求項３７に記載の方法。 37. The method of claim 37 , wherein the process conditions include a gas flow rate flowing into the processing module during the process sequence. 前記プロセス条件は、前記処理モジュール内に配置されたワークピースホルダの温度を含む、請求項３７に記載の方法。 37. The method of claim 37 , wherein the process conditions include the temperature of a workpiece holder placed within the processing module. 前記プロセス条件は、前記プロセスシーケンス中の前記処理モジュール内の圧力を含む、請求項３７に記載の方法。 37. The method of claim 37 , wherein the process conditions include pressure in the processing module during the process sequence. プラットフォーム性能データは、前記プロセスシーケンスの実行に寄与するプラットフォーム属性の表示又はプロセスモジュールが前記プロセスシーケンスに供された時間の長さの表示のうちの少なくとも１つを含む、請求項３７に記載の方法。 37. The method of claim 37 , wherein the platform performance data comprises at least one of a display of platform attributes that contribute to the execution of the process sequence or a display of the length of time a process module has been devoted to the process sequence. .. プラットフォーム属性は、プロセス冷却水温度、プロセス冷却水流量、プロセスモジュールの処理時間、プロセスモジュールの累積厚さを含む、請求項４３に記載の方法。 43. The method of claim 43 , wherein the platform attributes include process cooling water temperature, process cooling water flow rate, process module processing time, and process module cumulative thickness.
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