WO2009067952A1 - Procédé de commande de technique et son dispositif - Google Patents

Procédé de commande de technique et son dispositif Download PDF

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Publication number
WO2009067952A1
WO2009067952A1 PCT/CN2008/073169 CN2008073169W WO2009067952A1 WO 2009067952 A1 WO2009067952 A1 WO 2009067952A1 CN 2008073169 W CN2008073169 W CN 2008073169W WO 2009067952 A1 WO2009067952 A1 WO 2009067952A1
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Prior art keywords
target
variable
preset
prediction model
feedback control
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PCT/CN2008/073169
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English (en)
Chinese (zh)
Inventor
Shangui Zhang
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Beijing Nmc Co., Ltd.
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Publication of WO2009067952A1 publication Critical patent/WO2009067952A1/fr

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    • 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
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/048—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators using a predictor

Definitions

  • the present invention relates to the field of industrial process control technology, and more particularly to a method and apparatus for process control. Background technique
  • Process feedback control that is, control using feedback information.
  • the actual performance of the production system is measured by the sensing system or the measurement system and then compared with the required standards. If there is a difference, it is interpreted by the control system, and the control system commands the operating device to correct the performance. Eliminate the difference. This is the simplest feedback mode.
  • process feedback control techniques are a critical technology.
  • control requirements for etching equipment and wafer quality have become higher and higher in the etching process.
  • how to effectively realize the feedback control of equipment and silicon performance has become a hot issue in the IC industry in the world today.
  • the current principle methods for process feedback control mainly include: Exponential weighted moving average method
  • EWMA including single exponential weighted moving average method (SEWMA) and double exponential weighted moving average method (DEWMA)
  • NLP nonlinear programming control method
  • MPC model predictive control method
  • the response surface analysis method (RSM, mainly linear regression, least squares or neural network method) is used to fit out.
  • the functional relationship between the process output variable and the process input variable, that is, the prediction model for a sample to be detected, first use the prediction model to calculate the process output variable, and then based on the difference between the process prediction output value and the actual output value, Adjust the prediction model and control parameters of the process until the difference between the predicted value and the actual value of the output variable complies with the current process regulations. Appropriate prediction models and control parameters.
  • the exponential weighted moving average method, the nonlinear programming control method and the model predictive control method are all closed-loop control methods.
  • the feedback control process of the process they are Generally have a good control effect.
  • the exponential weighted moving average method is only suitable for single-input and single-output control systems.
  • the calculation process is very complicated, and a small link in the control model is not well processed. Leading to high errors;
  • the difficulty lies in the complexity and uncertainty of the calculation process (not all nonlinear programming problems can be solved);
  • the technical problem to be solved by the present invention is to provide a process feedback control method and device, which can realize process feedback control very effectively, and to some extent, improve the precision of process feedback control and avoid computational complexity, and reduce the calculation of feedback control. the amount.
  • the present invention discloses a process control method, which specifically includes: Step a: receiving a target output variable;
  • Step b calculating a target process variable by using a preset process prediction model and a feedback control model; the process prediction model is used to describe a functional relationship between the process variable and the output variable;
  • the objective function of the control model is: solving the target process variable with the smallest distance from the preset reference value; the constraint condition of the feedback control model is: the target output variable and the target process variable are in accordance with the process prediction model;
  • Step C input the target process variable to the actual process device to obtain an actual output variable;
  • Step d determine a deviation between the target output variable and the actual output variable; if the preset requirement is not met, adjust the The preset process prediction model returns to step b; if the preset requirement is met, it ends.
  • the distance between the target process variable and the preset reference value comprises: a relative distance, an absolute distance or a weighted distance.
  • the manner of adjusting the preset process prediction model includes adjusting a gain coefficient
  • the process prediction model is obtained by data sample analysis.
  • an optimal set of target process variables is selected according to the deviation values and/or the adjustment range of the single target process variables.
  • a process control apparatus including: a process target receiving module, configured to receive a target output variable;
  • a feedback processing module configured to calculate a target process variable by using a preset process prediction model and a feedback control model; the process prediction model is used to describe a functional relationship between the process variable and the output variable; and the objective function of the feedback control model is : solving a target process variable with a minimum distance from a preset reference value; the constraint condition of the feedback control model is: the target output variable and the target process variable are in accordance with a process prediction model;
  • a device interface module configured to input the target process variable to an actual process device, and detect an actual output variable
  • the distance between the target process variable and the preset reference value comprises: a relative distance, an absolute distance or a weighted distance.
  • the manner of adjusting the preset process prediction model may include adjusting a gain factor and/or an intercept.
  • the process prediction model is obtained by data sample analysis.
  • the device may further include: a screening module, configured to: when there are multiple sets of target process variables that meet preset requirements, select an optimal group according to the deviation value and/or the adjustment range of the single target process variable Target process variable.
  • a screening module configured to: when there are multiple sets of target process variables that meet preset requirements, select an optimal group according to the deviation value and/or the adjustment range of the single target process variable Target process variable.
  • the overall idea of the present invention still uses the principle of the nonlinear programming control method, but by introducing the technical idea of the nearest neighbor rule to realize the feedback control of the process, a better control error can be obtained, and the process drift can be better avoided;
  • the calculation mode of searching for the most advantageous in the multi-dimensional space is changed to search for the most advantageous calculation mode on the multi-dimensional plane, thereby reducing the amount of calculation.
  • the present invention can be adapted to all control systems, such as single-input single-output (SISO) systems, multiple-input single-output (MISO) systems, multiple-input multiple-output (MIMO) systems, etc.
  • SISO single-input single-output
  • MISO multiple-input single-output
  • MIMO multiple-input multiple-output
  • the present invention reduces computational feedback control calculations. The quantity and the accuracy of the process feedback control also have a significant effect.
  • FIG. 1 is a flow chart showing the steps of an embodiment of a process control method of the present invention
  • FIG. 3 is a block diagram showing the structure of an embodiment of a process control device of the present invention. detailed description The present invention will be further described in detail with reference to the drawings and specific embodiments.
  • program modules include routines, programs, objects, components, data structures, and the like that perform particular tasks or implement particular abstract data types.
  • the invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are connected through a communication network.
  • program modules can be located in both local and remote computer storage media including storage devices.
  • the feedback control process described herein can be accomplished by a number of general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, distributed computing environments including any of the above systems or devices, and the like.
  • FIG. 1 an embodiment of a process control method of the present invention is shown, which may specifically include the following steps:
  • Step 101 Receive a target output variable.
  • Step 102 Calculate a target process variable by using a preset process prediction model and a feedback control model; the process prediction model is used to describe a functional relationship between the process variable and the output variable; and the objective function of the feedback control model is: a target process variable having a minimum preset reference distance; the constraint condition of the feedback control model is: the target output variable and the target process variable conform to a process prediction model;
  • the calculation of the distance between the target process variable and the preset reference value in the feedback control model wherein the distance may include a relative distance, and may also include an absolute distance or a weighted distance.
  • Step 103 Input the target process variable to the actual process device to obtain an actual output variable.
  • Step 104 Determine a deviation between the target output variable and the actual output variable; If the preset requirement is not met, the preset process prediction model is adjusted, and the process returns to step 102; if the preset requirement is met, the process ends.
  • the process target parameters received in step 101 are determined according to the main control logic for the entire process. For different processes or process equipment, or different process moments, the process target parameters may be changed, and the present invention is based on the established The process target, to feedback adjustment to get the optimal process input parameters (Recipe, process variables, input variables).
  • the present invention is not necessarily limited to specific process target parameters.
  • Step 102 is one of the core steps of the entire feedback control.
  • One or more sets of target process variables that meet the requirements are obtained mainly by mathematical analytic operations (since the general process control may have multiple input variables at the same time).
  • the process prediction model is based on pre-applied multiple sets of data samples; for example, by means of response surface analysis, multiple linear regression or neural network, the functional relationship between input variables and output variables is calculated based on data samples.
  • the feedback control model is established according to the determined control target (objective function) and control conditions (constraints), and then can be calculated by programming (for example, C language or MATLAB, etc.) for the above model.
  • Target process variable for example, C language or MATLAB, etc.
  • Step 103 is used to actually run the target process variable obtained in step 102, and obtain the actual output variable as the input variable of the actual device, and then the actual output variable obtained by the actual operation of step 103 and the required process target output variable through step 104. Compare and calculate the control error; if the control error is within the preset requirement, the overall feedback control process meets the requirements; and if the control error is not within the preset requirement, the process prediction model is adjusted according to certain rules ( The coefficients are generally adjusted) and then returned to step 102 for recalculation.
  • the above process is executed cyclically until the calculated control error meets the preset requirements.
  • the preset requirement may be: a control error of less than 1% or 0.5%, and the like.
  • step 103 the process variables that do not meet the requirements may be run, and the raw materials of these actual operations are invalidated, and these possible wastes are required as a process trial run.
  • step 102 through the calculation of step 102, it is possible to obtain multiple sets of target process variables that conform to the feedback control model, but they cannot be handed over to the actual device for operation, because it causes a large waste. Therefore, preferably, when there are multiple sets of target process variables that meet the preset requirements, the embodiment shown in FIG. 1 can also select the optimal according to the deviation value (control error value) and/or the adjustment range of the single target process variable. A set of target process variables.
  • the adjustment range is the magnitude of the target process variable compared to the baseline (Baseline). From a process point of view, it is optimal to achieve the best control results with the smallest Recipe variation. Therefore, this parameter can be used to filter a set of optimal solutions, and in general, the adjustment is no more than 10%.
  • the core idea of the solution adopted by the present invention still uses the idea of the nonlinear control method, but for the nonlinear control idea, the specific solution and the specific control model are not seen in practice.
  • Min Z (T 0 - y(x l , x 2 , A , x k )) 2
  • Min represents the minimum value for solving the objective function
  • st represents the constraint.
  • each process variable of the process can be freely changed within its own range of values until the deviation of the predicted value of the process output variable (control target) from the expected value satisfies the accuracy requirement of the control.
  • control target the deviation of the predicted value of the process output variable (control target) from the expected value satisfies the accuracy requirement of the control.
  • the above-mentioned process control law is indeed no problem, but in actual process production, it may have unreasonable factors, such as: etching process as a high-precision industry, if During the production process, the process recipe is greatly fluctuated due to the adjustment of the process control target, which may cause the process to drift (equipment instability), resulting in more waste.
  • the nearest neighbor rule feedback control model proposed a feedback control model based on the nearest neighbor rule (called the nearest neighbor rule feedback control model), as follows:
  • the constraint condition is a k-dimensional plane
  • the calculation process is equivalent to searching for the best advantage in a k-dimensional plane, and the calculation amount is greatly reduced compared to searching in the k-dimensional space.
  • the flow of feedback control is further explained:
  • Stepl Selection of modeling sample data.
  • process variables output variables and input variables (process variables)
  • a certain number of modeling samples are selected from the historical data according to the following factors: 1 consistency of sample data types; 2 rationality of sample data accuracy; 3 sample data changes Diversity.
  • the purpose of the above selection rules is to obtain sample data that can fully reflect the actual situation.
  • a specific simulation example is given in the following paper: Data sample
  • Step2 The establishment of the process prediction model. After the input and output variables and data samples are determined, the functional relationship between the input variables and the output variables can be obtained by using multiple linear regression methods, that is, the process prediction model. Let the output variable of the process be the input variable ⁇ , ⁇ , , then the prediction model of the process can be expressed by the following equation:
  • y(x l , x 2 ,A , x k ) a 0 + ⁇ ⁇ ⁇ ⁇ + ⁇ 2 ⁇ 2 + ⁇ + a k x k
  • the multivariate linear regression method can be used to obtain the prediction model of the corresponding process, as follows:
  • the specific manner of feedback adjusting the preset process prediction model may be an adjustment coefficient, and the adjustment coefficient may include adjusting a gain coefficient, and may also include adjusting an intercept (eg, “ 0 ”).
  • Step3 The establishment of the feedback control model.
  • the basic idea of the process adopted by the present invention closest to the control law is: pre-set the reference value of each process variable (also known as the Baseline of the process recipe), in the process of feedback control, the value of the process variable is To meet the error requirement between the predicted and expected values of the output variable, it must also be closest to the set reference value, ie the change between the newly generated Recipe and the reference value is minimal.
  • the mathematical feedback method can be used to establish the feedback control model of the process.
  • Step4 Assume that the Baseline of the four process control variables are [80, 250, 65, 10.0] respectively.
  • the output of the process feedback control in this case is shown in Table 2.
  • the feedback control absolute error refers to the actual output.
  • the absolute error between the value and the target output value (the values here are all absolute values, not percentage values).
  • FIG. 2 an error comparison diagram of the common feedback control model and the most recent rule feedback control model is shown. Comparing the error results of the process feedback control method of the nearest rule under the same conditions with the general nonlinear process control method, it can be found that:
  • the process feedback control method using the nearest neighbor rule is more suitable for the actual process requirements, and it is controlled by feedback. Accuracy is also more in line with current process standards than existing process control methods, and this is the key to achieving advanced process automation control in the future.
  • the nearest feedback control model in the above specific example uses the standard of relative distance.
  • the change between the feedback value and the reference value can be used in addition to the relative distance.
  • Absolute distance, weighted distance and other methods are described.
  • the process feedback control model can be expressed by the following equation:
  • Model one 0 Model 2
  • model one is the absolute distance control model (which can also be weighted)
  • the weights of each process variable can be determined according to the importance of each process variable and their correlation with the control target. If the two models are given reasonable initial conditions, the feedback control of the process can be similarly realized.
  • a process control device that can be used as a stand-alone feedback control device, typically as a stand-alone functional module integrated into an Advanced Process Control (APC) system.
  • the device may specifically include the following components: a process target receiving module 301, configured to receive a target output variable;
  • the feedback processing module 302 is configured to calculate a target process variable by using a preset process prediction model and a feedback control model; the process prediction model is used to describe a functional relationship between the process variable and the output variable; and the objective function of the feedback control model For: solving a target process variable with a minimum distance from a preset reference value; the constraint condition of the feedback control model is: the target output variable and the target process variable are in accordance with a process prediction model; and the target process variable is between a preset reference value and a preset reference value Distances include: relative distance, absolute distance, or weighted distance.
  • the process prediction model can be obtained by data sample analysis.
  • a device interface module 303 configured to input the target process variable to an actual process device, and detect an actual output variable
  • a judgment feedback module 304 configured to determine the target output variable and the actual output variable If the preset requirement is not met, the preset process prediction model is adjusted and returned to the feedback processing module; if the preset requirement is met, the process ends.
  • the manner of adjusting the preset process prediction model may include adjusting coefficients and/or intercepts.
  • the judgment feedback module 304 is divided into a judgment module and a feedback adjustment module. It is within the scope of the device embodiment to split or recombine the functions of the various modules described above.
  • the apparatus shown in FIG. 3 may further include a screening module for filtering an optimal set of targets according to the deviation value and/or the adjustment range of the single target process variable. Process variable. The set of target process variables is then transferred to the actual device for operation.

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Feedback Control In General (AREA)

Abstract

L'invention concerne un procédé de commande de technique. Le procédé comprend les étapes suivantes : ÉTAPE a, recevoir les variables de sortie cibles ; ÉTAPE b, calculer les variables de traitement cibles à l'aide du modèle prédictif de technique prédéfini et du modèle de commande de rétroaction. Ledit modèle prédictif de technique est utilisé pour décrire la relation de fonction entre les variables de traitement et les variables de sortie. La fonction cible dudit modèle de commande de rétroaction calcule les variables de traitement cibles ayant la distance minimale à partir des références prédéfinies. Le terme de restriction dudit modèle de commande de rétroaction s'explique par le fait que les variables de sortie cibles et les variables de traitement cibles sont toutes à la hauteur dudit modèle prédictif de technique. ÉTAPE c, entrer lesdites variables de traitement cibles dans des dispositifs de technique réels et obtenir les variables de sortie réelles ; ÉTAPE d, décider des différences entre lesdites variables de sortie cibles et lesdites variables de sortie réelles ; si les différences ne satisfont pas la demande prédéfinie, ajuster ledit modèle prédictif de technique prédéfini et retourner à l'ETAPE b ; si les différences satisfont la demande prédéfinie, alors terminer.
PCT/CN2008/073169 2007-11-26 2008-11-24 Procédé de commande de technique et son dispositif WO2009067952A1 (fr)

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CN103050421A (zh) * 2011-10-17 2013-04-17 中芯国际集成电路制造(上海)有限公司 刻蚀控制方法
CN104111604A (zh) * 2013-04-16 2014-10-22 中国石油化工股份有限公司 乙苯脱氢生产过程的预测函数控制方法
CN105068640B (zh) * 2015-08-13 2018-06-26 浪潮(北京)电子信息产业有限公司 一种提高高性能计算能耗比的方法及系统
JP2018120327A (ja) * 2017-01-24 2018-08-02 オムロン株式会社 制御装置、制御プログラムおよび制御システム
CN107968042B (zh) * 2017-11-28 2020-07-17 北京北方华创微电子装备有限公司 一种不同反应腔室之间工艺结果的匹配方法和装置
CN111190393B (zh) * 2018-11-14 2021-07-23 长鑫存储技术有限公司 半导体制程自动化控制方法及装置
CN113693272B (zh) * 2021-08-31 2022-09-23 河南中烟工业有限责任公司 卷烟重量控制方法
CN116913815B (zh) * 2023-07-26 2024-02-23 数语技术(广州)有限公司 一种高温cvd生产制程的控制方法、装置及存储介质

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