Data-driven approaches to optical patterned defect detection.

Mark-Alexander Henn,Hui Zhou,Bryan M Barnes

doi:10.1364/osac.2.002683

Abstract

Computer vision and classification methods have become increasingly wide-spread in recent years due to ever-increasing access to computation power. Advances in semiconductor devices are the basis for this growth, but few publications have probed the benefits of data-driven methods for improving a critical component of semiconductor manufacturing, the detection and inspection of defects for such devices. As defects become smaller, intensity threshold-based approaches eventually fail to adequately discern differences between faulty and non-faulty structures. To overcome these challenges we present machine learning methods including convolutional neural networks (CNN) applied to image-based defect detection. These images are formed from the simulated scattering of realistic geometries with and without key defects while also taking into account line edge roughness (LER). LER is a known and challenging problem in fabrication as it yields additional scattering that further complicates defect inspection. Simulating images of an intentional defect array, a CNN approach is applied to extend detectability and enhance classification to these defects, even those that are more than 20 times smaller than the inspection wavelength.

Highlights

Compared to the Apollo 11’s onboard guidance computer, a modern cellphone is about 1,400 times faster and has 4,000,000 times more memory [1]
Intensity threshold-based approaches eventually fail to adequately discern differences between faulty and non-faulty structures. To overcome these challenges we present machine learning methods including convolutional neural networks (CNN) applied to image-based defect detection
While defects encountered in nanoelectronics fabrication often defy such straightforward classification, the presented results demonstrate the versatility of a CNN approach to addressing the ever-pressing challenge of detecting killer defects

Summary

Introduction

Compared to the Apollo 11’s onboard guidance computer, a modern cellphone is about 1,400 times faster and has 4,000,000 times more memory [1]. Transistor count is the most common measure of integrated circuit complexity and is closely related to computational performance [3] - the main force driving the feasibility and wide-spread availability of the different techniques of data-driven methods The manufacturing of these integrated circuits as of 2017 has become a $ 400 billion industry [4], and even as the semiconductor industry struggles to perpetuate Moore’s law [5], crucial challenges exist in monitoring the production process for decreasing feature sizes [6]. As killer defects decrease in size with shrinking device dimensions the scattered intensity from these defects becomes harder to detect, for either approach a large amount of data need to be processed Converting these low-intensity data into meaningful results requires exploiting the very increase in computation power that results from successfully producing more powerful devices. Image-based defect detection with machine learning has been realized in other industries e.g., textiles [19,20,21,22], steel [23], and wood [24], but a key difference is that due to the decreased dimensions in semiconductors these defects must be detected even as they are often unresolved

Simulation details

Implementation of ML algorithms

CNN results

Findings

Conclusion