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Machine learning based inverse optical proximity correction and process model calibration

A process model and model technology, applied in the field of inverse optical proximity effect correction and process model calibration based on machine learning, can solve problems such as difficulty in reproducing patterns

Pending Publication Date: 2021-02-19
ASML NETHERLANDS BV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Usually, k 1 The smaller it becomes, the more difficult it becomes to reproduce a pattern on a substrate that resembles the shape and dimensions planned by the designer in order to achieve specific electrical functions and properties

Method used

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  • Machine learning based inverse optical proximity correction and process model calibration
  • Machine learning based inverse optical proximity correction and process model calibration
  • Machine learning based inverse optical proximity correction and process model calibration

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Embodiment Construction

[0050] Although specific reference may be made herein to the fabrication of ICs, it is expressly understood that the description herein has many other possible applications. For example, the description can be used to fabricate integrated optical systems, guidance and detection patterns for magnetic domain memories, liquid crystal display panels, thin film magnetic heads, and the like. Those skilled in the art will appreciate that, in the context of such alternative applications, any use of the terms "reticle," "wafer," or "die" herein should be considered interchangeable with the more generic term "mask," respectively. , "substrate" and "target moiety" are interchanged.

[0051] In this document the terms "radiation" and "beam" are used to cover all types of electromagnetic radiation, including ultraviolet radiation (e.g. having a wavelength of 365 nm, 248 nm, 193 nm, 157 nm or 126 nm) and EUV (extreme ultraviolet radiation, e.g. having wavelengths in the range of about 5 nm...

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Abstract

Described herein is a method for calibrating a process model and training an inverse process model of a patterning process. The training method includes obtaining a first patterning device pattern from simulation of an inverse lithographic process that predicts a patterning device pattern based on a wafer target layout, receiving wafer data corresponding to a wafer exposed using the first patterning device pattern; and training an inverse process model configured to predict a second patterning device pattern using the wafer data related to the exposed wafer and the first patterning device pattern.

Description

[0001] Cross References to Related Applications [0002] This application claims priority to US Application 62 / 685,749, filed June 15, 2018, which is incorporated herein by reference in its entirety. technical field [0003] The description herein relates generally to patterning processes and apparatus and methods for determining patterns of patterning devices corresponding to designed layouts. Background technique [0004] Lithographic projection equipment may be used, for example, in the manufacture of integrated circuits (ICs). In this case, the patterning device (e.g., a mask) may contain or provide a pattern corresponding to a single layer of the IC (the "design layout"), and such pattern may be radiated by, for example, via the pattern on the patterning device. Transferred onto a target portion (e.g., comprising one or more dies) on a substrate (e.g., a silicon wafer) that has been coated with a radiation-sensitive material (“resist ")layer. Typically, a single subs...

Claims

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Application Information

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IPC IPC(8): G03F7/20G03F1/36
CPCG03F7/705G03F1/36G03F7/70625G06F30/392G06F30/398G06F2119/18
Inventor 马里纳斯·范登布林克曹宇邹毅
Owner ASML NETHERLANDS BV
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