Spin coating has been used as a photoresist application method for many years,[1,2] and it has continued to include applications such as the tri-layer with stacked photoresist, Si containing anti-reflected coating (Si-ARC), and Spin on Carbon (SOC). Last year we reported EUV defectivity improvement, but the causes of some defect types were not found.[3,4] In this study, the defects unique to the coated organic film were detected using an LS9300 by Hitachi High-Technologies, and some of these defects were able to be mitigated by optimizing the SOKUDO-DUO track system. Utilizing these systems in tandem, we have revealed a mechanism of EUV pattern defect reduction linked to novel detected film coating defects. During the conference, we will discuss expansion of this concept to other film coatings.
KEYWORDS: Particles, Algorithm development, Yield improvement, Back end of line, Image classification, Manufacturing, System on a chip, Scanning electron microscopy, Chemical vapor deposition, Semiconductor manufacturing
Since the semiconductor manufacturing process has become more and more complicated due to the introduction of either
new materials or new structures, detecting the source of a defect has become dramatically difficult. Automatic Defect
Classification (ADC) is one of the most effective ways for identifying the source of a defect. However, the current ADC
algorithm is insufficient in identifying a defect source, because its classification results are quite simple. Since the
classification is determined by the shape or size of the defect, it is difficult to figure out the process or processing tool in
which the defects are generated. To solve this problem, we propose a new ADC algorithm and have already applied it to
a high-volume System-on-Chip (SoC) production line to verify its efficiency. We confirmed with the classification
results that the new ADC algorithm is almost as accurate as manually classifying them, but with the reduction of the time
required for identifying the defect source.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.