As semiconductor process technology needs to be advanced, the difficulty of patterning increases and unexpected pattern defects occur. In fact, it is very important to quickly identify and resolve these pattern defects in advance, but there is a limit to measuring all of these pattern defects that occur in wafers. In particular, in order to overcome metrological limitations, it may be best to extract actual potential pattern defects by classifying various types of patterns that actually exist. We measured how to classify weak patterns by sampling them based on previously known weak pattern group libraries, simulation-based weak patterns, and unique patterns. Through this method, engineers can subjectively judge how to find weak patterns, and it can be difficult because there is a possibility that the probability of weak patterns is low depending on the limited measurement capacity. In this paper, unsupervised learning is used to cluster and classify by pattern type based on the various features of pattern. Then, based on reliable wafer data for various classified pattern types, the degree of vulnerability to defect was quantified for each classified cluster to give a ranking for extracting a weak pattern group for each cluster, and the weak pattern was extracted based on this to confirm a high weak pattern detection rate. In addition, it provides effective solutions to extract weak patterns from various databases (DBs) and specifically to give reliability to visualization methods.
Background: Natural physical phenomena occurring at length scales of a few nm produces variation in many aspects of the EUV photoresist relief image: edge roughness, width roughness, feature-tofeature variability, etc. 1,2,3,4. But the most damaging of these variations are stochastic or probabilistic printing failures 5, 6. Stochastic or probabilistic failures are highly random with respect to count and location and occur on wafers at spectra of unknown frequencies. Examples of these are space bridging, line breaking, missing and merging holes. Each has potential to damage or destroy the device, reducing yield 6, 10. Each has potential to damage or destroy the device, reducing yield 6, 10. The phenomena likely originates during exposure where quantized light and matter interact1 . EUV lithography is especially problematic since the uncertainty of energy absorbed by a volume of resist is much greater at 13.5 nm vs. 248 nm and 193 nm. Methods: In this paper, we use highly accelerated rigorous 3D probabilistic computational lithography and inspection to scan an entire EUV advanced node layout, predicting the location, type and probability of stochastic printing failures.
Patterning, a major process in semiconductor manufacturing, aims to transfer the design layout to the wafer. Accordingly, the "process proximity correction" method was developed to overcome the difference in after-cleaninginspected CD (critical dimension) between patterns of similar shapes. However, its physical model is often limited in the predictive performance. Therefore, recent studies have introduced ML (machine learning) technology to supplement model accuracy, but this approach often has an inherent risk of overfitting depending on the type of sampled pattern. In this study, we present a newly invented flow capable of stable etch-process-aware ML modeling by model reconstruction and large amounts of measurement data. The new modeling flow can also be performed within a reasonable runtime through efficient feature extraction. Based on the new model and its related layout targeting platform, intensive improvements were made to CD targeting and spread; for a given layout, in comparison with delicate rule-based modification, the CD targeting accuracy was improved by 4 times and approaches the limit of metrology error.
Most important factors in OPC model building will be sampling data for model calibration.
We will demonstrate that how CD-AFM data can be used in OPC modeling and will show possibility to get a more predictive model by using CD-AFM data.
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.