Method, apparatus and device for optimizing process parameter, and storage medium

A device for optimizing a process parameter, comprising:

• a processor and a memory;
• the memory is configured to store a computer program;
• and
• the processor is configured to execute the computer program stored in the memory to:
• use a first weight parameter of a pre-trained support vector regression model for a manufacturing equipment as an iterative initial value, and calculate a second weight parameter of a support vector regression model using an online proximal gradient algorithm based on training data, wherein the training data comprises:
• a historical process parameter of the manufacturing equipment, and a historical detection parameter corresponding to when the manufacturing equipment operates with the historical process parameter;
• obtain an optimized support vector regression model by updating the first weight parameter of the pre-trained support vector regression model to the second weight parameter;
• input a first process parameter of the manufacturing equipment into the optimized support vector regression model to obtain a first detection parameter output by the optimized support vector regression model;
• and
• calculate, according to the first process parameter and the first detection parameter, a target process parameter for the manufacturing equipment;
• wherein the processor is further configured to:
• calculate the second weight parameter by using a linear iterative formula with the first weight parameter as the iterative initial value when the pre-trained support vector regression model is a linear support vector regression model, and the linear iterative formula is:
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• or
• calculate the second weight parameter by using a nonlinear iterative formula with the first weight parameter as the iterative initial value when the pre-trained support vector regression model is a nonlinear support vector regression model, and the nonlinear iterative formula is:
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• wherein wk+1 represents a (k+1)-th second weight parameter, wk represents a k-th second weight parameter, an initial second weight parameter w0 is the first weight parameter, tk represents a step size for a k-th iteration, β represents an order of a penalty function, λ represents a penalty factor of the penalty function, xk represents the historical process parameter, yk represents the historical detection parameter corresponding to xk, ε represents an insensitive coefficient of the support vector regression model, and φ(xk) represents a kernel mapping function.

A non-transitory readable storage medium, wherein the readable storage medium stores a computer program;

• the computer program is used for implementing the following steps:
• using a first weight parameter of a pre-trained support vector regression model for a manufacturing equipment as an iterative initial value, and calculating a second weight parameter of a support vector regression model using an online proximal gradient algorithm based on training data, wherein the training data comprises:
• a historical process parameter of the manufacturing equipment, and a historical detection parameter corresponding to when the manufacturing equipment operates with the historical process parameter;
• obtaining an optimized support vector regression model by updating the first weight parameter of the pre-trained support vector regression model to the second weight parameter;
• inputting a first process parameter of the manufacturing equipment into the optimized support vector regression model to obtain a first detection parameter output by the optimized support vector regression model;
• and
• calculating, according to the first process parameter and the first detection parameter, a target process parameter for the manufacturing equipment;
• wherein using the first weight parameter of the pre-trained support vector regression model for the manufacturing equipment as the iterative initial value, and calculating the second weight parameter of the support vector regression model using the online proximal gradient algorithm based on the training data comprises:
• calculating, by the hardware processor, the second weight parameter by using a linear iterative formula with the first weight parameter as the iterative initial value when the pre-trained support vector regression model is a linear support vector regression model, and the linear iterative formula is:
• w
• k
• +
• 1
• =
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• k
• -
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• ⁢
• t
• k
• ⁢
• ϕ
• ⁡
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• x
• k
• )
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• ;
• or
• calculating, by the hardware processor, the second weight parameter by using a nonlinear iterative formula with the first weight parameter as the iterative initial value when the pre-trained support vector regression model is a nonlinear support vector regression model, and the nonlinear iterative formula is:
• w
• k
• +
• 1
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• k
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• ⁢
• λ
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• k
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• k
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• k
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• "\[RightBracketingBar]"
• -
• ε
• )
• β
• -
• 1
• 1
• +
• t
• k
• ;
• wherein wk+1 represents a (k+1)-th second weight parameter, wk represents a k-th second weight parameter, an initial second weight parameter w0 is the first weight parameter, tk represents a step size for a k-th iteration, β represents an order of a penalty function, λ represents a penalty factor of the penalty function, xk represents the historical process parameter, yk represents the historical detection parameter corresponding to xk, ε represents an insensitive coefficient of the support vector regression model, and φ(xk) represents a kernel mapping function.