公开(公告)号：WO2019048137A1

公开(公告)日：2019-03-14

申请号：PCT/EP2018/070605

申请日：2018-07-30

Applicant: ASML NETHERLANDS B.V.

Inventor： TABERY, Cyrus, Emil ,  CEKLI, Hakki, Ergun ,  VAN GORP, Simon, Hendrik, Celine ,  LIN, Chenxi

IPC: G03F7/20 ,  H01L21/66

CPC classification number: G03F7/70525 ,  G03F7/70616 ,  H01L22/12 ,  H01L22/20

Abstract: Disclosed herein is a method for determining a control parameter for an apparatus utilised in a semiconductor manufacturing process, the method comprising: obtaining performance data associated with a substrate subject to the semiconductor manufacturing process; obtaining die specification data comprising values of an expected yield of one or more dies on the substrate based on the performance data and/or a specification for the performance data; and determining the control parameter in dependence on the performance data and the die specification data. Advantageously, the efficiency and accuracy of processes are improved by only determining how to perform the processes in dependence on dies within specification.

公开(公告)号：WO2018121988A1

公开(公告)日：2018-07-05

申请号：PCT/EP2017/082524

申请日：2017-12-13

Applicant: ASML NETHERLANDS B.V.

Inventor： CAO, Yu ,  ZOU, Yi ,  LIN, Chenxi

IPC: G03F7/20 ,  G05B13/00 ,  G06T7/00 ,  H01L21/66

Abstract: A method where deviations of a characteristic of an image simulated by two different process models or deviations of the characteristic simulated by a process model and measured by a metrology tool, are used for various purposes such as to reduce the calibration time, improve the accuracy of the model, and improve the overall manufacturing process.

公开(公告)号：WO2022008174A1

公开(公告)日：2022-01-13

申请号：PCT/EP2021/065947

申请日：2021-06-14

Applicant: ASML NETHERLANDS B.V.

Inventor： GUO, Chaoqun ,  KHEDEKAR, Satej, Subhash ,  GANTAPARA, Anjan Prasad ,  LIN, Chenxi ,  CASTELIJNS, Henricus, Jozef ,  CHEN, Hongwei ,  BOND, Stephen Henry ,  LI, Zhaoze ,  MOSSAVAT, Seyed Iman ,  ZOU, Yi ,  YPMA, Alexander ,  ZHANG, Youping ,  DICKER, Gerald ,  STEINMEIER, Ewout, Klaas ,  VAN BERKEL, Koos ,  BOLDER, Joost, Johan ,  HUBAUX, Arnaud ,  HLOD, Andriy, Vasyliovich ,  GONZALEZ HUESCA, Juan Manuel ,  AARDEN, Frans Bernard

IPC: G03F7/20

Abstract: Generating a control output for a patterning process is described. A control input is received. The control input is for controlling the patterning process. The control input comprises one or more parameters used in the patterning process. The control output is generated with a trained machine learning model based on the control input. The machine learning model is trained with training data generated from simulation of the patterning process and/or actual process data. The training data comprises 1) a plurality of training control inputs corresponding to a plurality of operational conditions of the patterning process, where the plurality of operational conditions of the patterning process are associated with operational condition specific behavior of the patterning process over time, and 2) training control outputs generated using a physical model based on the training control inputs.

公开(公告)号：WO2020078762A1

公开(公告)日：2020-04-23

申请号：PCT/EP2019/077146

申请日：2019-10-08

Applicant: ASML NETHERLANDS B.V.

Inventor： SIMMONS, Mark, Christopher ,  LIN, Chenxi ,  WUU, Jen-Yi

IPC: G03F7/20

Abstract: A method of generating a characteristic pattern for a patterning process and training a machine learning model. The method for generating the characteristic pattern includes obtaining a trained generator model configured to generate a characteristic pattern (e.g., hot spot pattern), and an input pattern; and generating, via simulation of the trained generator model (e.g., CNN), the characteristic pattern based on the input pattern, wherein the input pattern is at least one of a random vector, a class of pattern.

公开(公告)号：WO2019149562A1

公开(公告)日：2019-08-08

申请号：PCT/EP2019/051424

申请日：2019-01-22

Applicant: ASML NETHERLANDS B.V.

Inventor： BASTANI, Vahid ,  YPMA, Alexander ,  SONNTAG, Dag ,  MOS, Everhardus, Cornelis ,  CEKLI, Hakki, Ergun ,  LIN, Chenxi

IPC: G06N5/00 ,  G03F7/20

Abstract: Substrates to be processed (402) are partitioned based on pre-processing data (404) that is associated with substrates before a process step. The data is partitioned using a partition rule (410, 412, 414) and the substrates are partitioned into subsets (G1-G4) in accordance with subsets of the data obtained by the partitioning. Corrections (COR1-COR4) are applied, specific to each subset. The partition rule is obtained (Fig 5) using decision tree analysis on a training set of substrates (502). The decision tree analysis uses pre-processing data (256, 260) associated with the training substrates before they were processed, and post-processing data (262) associated with the training substrates after being subject to the process step. The partition rule (506) that defines the decision tree is selected from a plurality of partition rules (512) based on a characteristic of subsets of the post-processing data. The associated corrections (508) are obtained implicitly at the same time.

公开(公告)号：WO2017194281A1

公开(公告)日：2017-11-16

申请号：PCT/EP2017/059328

申请日：2017-04-20

Applicant: ASML NETHERLANDS B.V.

Inventor： SU, Jing ,  ZOU, Yi ,  LIN, Chenxi ,  HUNSCHE, Stefan ,  JOCHEMSEN, Marinus ,  LU, Yen-Wen ,  CHEONG, Lin, Lee

IPC: G03F7/20 ,  G06K9/00 ,  G06F17/50

Abstract: Disclosed herein are various methods of identifying a hot spot from a design layout or of predicting whether a pattern in a design layout is defective, using a machine learning model. An example method disclosed herein includes obtaining sets of characteristics of performance of hot spots, respectively, under a plurality of process conditions, respectively, in a device manufacturing process; determining, for each of the process conditions, for each of the hot spots, based on the characteristics under that process condition, whether that hot spot is defective; obtaining characteristics of each of the process conditions; obtaining characteristics of each of the hot spots; and training a machine learning model using a training set comprising the characteristics of one of the process conditions, the characteristics of one of the hot spots, and whether that hot spot is defective under that process condition.

Abstract translation: 这里公开了使用机器学习模型从设计布局识别热点或预测设计布局中的模式是否有缺陷的各种方法。 本文公开的示例方法包括分别在设备制造过程中的多个过程条件下分别获得热点性能的特征组; 基于在该处理条件下的特性，针对每个处理条件确定每个热点的热点是否有缺陷; 获得每个工艺条件的特性; 获得每个热点的特征; 并且使用训练集训练机器学习模型，所述训练集包括过程条件之一的特征，其中一个热点的特征以及在该过程条件下该热点是否有缺陷。

公开(公告)号：EP3899662A1

公开(公告)日：2021-10-27

申请号：EP19801044.9

申请日：2019-11-14

Applicant: ASML Netherlands B.V.

Inventor： BRANTJES, Nicolaas Petrus Marcus ,  COX, Matthijs ,  MENCHTCHIKOV, Boris ,  TABERY, Cyrus, Emil ,  ZHANG, Youping ,  ZOU, Yi ,  LIN, Chenxi ,  CHENG, Yana ,  HASTINGS, Simon, Philip, Spencer ,  GENIN, Maxime, Philippe, Frederic

IPC: G03F7/20 ,  G03F9/00

公开(公告)号：EP3891559A1

公开(公告)日：2021-10-13

申请号：EP19798274.7

申请日：2019-11-04

Applicant: ASML Netherlands B.V.

Inventor： LIN, Chenxi ,  TABERY, Cyrus Emil ,  CEKLI, Hakki Ergun ,  HASTINGS, Simon Philip Spencer ,  MENCHTCHIKOV, Boris ,  ZOU, Yi ,  CHENG, Yana ,  GENIN, Maxime Philippe Frederic ,  CHEN, Tzu-Chao ,  HARUTYUNYAN, Davit ,  ZHANG, Youping

IPC: G03F7/20 ,  H01L21/66

公开(公告)号：EP4449205A1

公开(公告)日：2024-10-23

申请号：EP22835390.0

申请日：2022-12-13

Applicant: ASML Netherlands B.V.

Inventor： FU, Jiyou ,  SU, Jing ,  LIN, Chenxi ,  LIANG, Jiao ,  CHEN, Guangqing ,  ZOU, Yi

IPC: G03F7/20 ,  G03F9/00 ,  H01J37/22 ,  H01L21/66

公开(公告)号：EP4244677A1

公开(公告)日：2023-09-20

申请号：EP21802689.6

申请日：2021-11-02

Applicant: ASML Netherlands B.V.

Inventor： LIN, Chenxi ,  ZOU, Yi ,  HASAN, Tanbir ,  XU, Huina ,  KOU, Ren-Jay ,  MOIN, Nabeel, Noor ,  NAFISI, Kourosh

IPC: G03F7/20