公开(公告)号：WO2017215944A1

公开(公告)日：2017-12-21

申请号：PCT/EP2017/063383

申请日：2017-06-01

Applicant: ASML NETHERLANDS B.V.

Inventor： GU, Ning ,  WANG, Daimian ,  WANG, Jen-Shiang

IPC: G03F7/20

CPC classification number: G03F7/70616 ,  G03F7/705 ,  G03F7/70633 ,  G06F17/5045

Abstract: A method including computing a multi-variable cost function, the multi-variable cost function representing a metric characterizing a degree of matching between a result when measuring a metrology target structure using a substrate measurement recipe and a behavior of a pattern of a functional device, the metric being a function of a plurality of design variables comprising a parameter of the metrology target structure, and adjusting the design variables and computing the cost function with the adjusted design variables, until a certain termination condition is satisfied.

Abstract translation: 一种包括计算多变量成本函数的方法，所述多变量成本函数表示表征当使用衬底测量配方测量度量衡目标结构时的结果与表征所述结果之间的匹配程度的度量的度量， 度量是包括度量目标结构的参数的多个设计变量的函数，并且调节设计变量并且利用调节的设计变量计算成本函数，直到某个终止条件是 满意。

公开(公告)号：WO2021063728A1

公开(公告)日：2021-04-08

申请号：PCT/EP2020/076342

申请日：2020-09-22

Applicant: ASML NETHERLANDS B.V.

Inventor： GU, Ning ,  REN, Liping ,  HUANG, Kui-Jun ,  WU, Jian

IPC: G03F7/20

Abstract: A method for monitoring performance of a manufacturing process is described. The method comprises receiving one or more input signals that convey information related to geometry of a substrate generated by the manufacturing process; and determining, with a prediction model, variation in the manufacturing process based on the one or more input signals. A method for predicting substrate geometry associated with a manufacturing process is also described. The method comprises receiving input information including geometry information and manufacturing process information for a substrate; and predicting, using a machine learning prediction model, output substrate geometry based on the input information. The method further comprises tuning the predicted output substrate geometry. The tuning comprises comparing the output substrate geometry to corresponding physical substrate measurements and/or predictions from a different non machine learning prediction model, generating a loss function based on the comparison, and optimizing the loss function.