﻿ 一种应用于光学临近修正过程中的异常图像识别方法 Abnormal Image Identification Method Used for Optical Proximity Correction

Journal of Image and Signal Processing
Vol. 08  No. 04 ( 2019 ), Article ID: 32651 , 6 pages
10.12677/JISP.2019.84027

Abnormal Image Identification Method Used for Optical Proximity Correction

Shunkui Ke

Shanghai Huali Integrated Circuit Manufacturing Corporation, Shanghai

Received: Oct. 2nd, 2019; accepted: Oct. 17th, 2019; published: Oct. 24th, 2019

ABSTRACT

In order to solve the bad performance for the image in the optical proximity correction, in this paper, a solution can be applied in micro chip manufacturing process which was introduced. It can automatically identify abnormal image and filter bad performance data to save a lot of engineering intervene and prevent the data validation.

Keywords:OPC, Abnormal Image, Data, Chip Manufacturing

1. 概述

OPC一直是芯片制造的核心。制约OPC发展的关键是OPC模型的建立与模型的验证，而数据量测是OPC建模和模型验证的基础。目前普遍存在的问题是：量测机台有时不能准确对准、量测条件容易设置错误，目标量测图形未能准确曝光等，导致量测结果都需要工程师结合人工经验进行检查和校正，这不仅在很大程度上加重了工程师的工作负荷。

2. 技术方法

OPC数据采集过程中的异常图像处理方法，包括：真彩色图形转换、灰度图生成和灰度图均衡化、图像的灰度直方图获取、相邻图像的图形相关系数计算等步骤 [6] [7] [8]，能实现自动化的异常图像识别和异常数据过滤，功能模块图如图1所示。

Figure 1. Functional block diagram

2.1. 真彩色图转换

(1)

2.2. 灰度图生成与均衡化

①、灰度图生成

②、灰度图均衡化

$DB=f\left(x\right)=DMax\cdot \int H\left(u\right)\text{d}u/A0\text{ }\left(u=0~x\right)$ (2)

2.3. 灰度直方图生成

Figure 2. Abnormal image and gray histogram

Figure 3. Normal image and gray histogram

2.4. 图像相关系数求取与统计分析

$R=f\left(t\right)\cdot g\left(-t\right)=\int f\left(t\right)\cdot g\left(t\right)\text{d}t\text{ }\left(-\infty (3)

Figure 4. Comparison between normal image and abnormal image

2.5. 异常图像去除

Table 1. R value result

R = 0.2 R = 0.24 R = 0.22

Figure 5. Filtered abnormal image

3. 结束语

Abnormal Image Identification Method Used for Optical Proximity Correction[J]. 图像与信号处理, 2019, 08(04): 215-220. https://doi.org/10.12677/JISP.2019.84027

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