Search

It is important to remove image noise properly to measure critical dimension (CD) and roughness values from scanning electron microscope (SEM) images. In order to reduce image noise, the number of electron beam (EB) scans, or frame number, is increased. However, this excess EB irradiation damages the objects being measured and changes their size. In this paper, a new image analysis method is introduced to remove image noise without a typical noise filter. In this method, each image frame is used in a four dimensional array, and several artificial images are generated and edge coordinates are calculated. As a result of this new method, we can separate the line width roughness (LWR) components into process roughness and image noise, and analyze images with lower number of frames with minimal EB damage. The impact of image noise on the accuracy of CD extraction is explained in the section on analysis of variance (ANOVA). The variation is separated as wafer to wafer (WTW), field to field (FTF), die to die (DTD), pattern to pattern (PTP), line width roughness (LWR), and stochastic pattern noise (SPN; which is random variation per a pattern) at this ANOVA. Roughness component from image noise is included in SPN. It is possible to remove the image noise component from SPN by applying this new image analysis method, and it is also possible to discuss the SPN from shot noise of exposure tool or variation of resist material component. ANOVA can put an end to discussion of measurement length of line pattern to know the state of low frequency roughness. LWR component of long wavelength is distributed to PTP and SPN when short patterns are measured. It is important to remove image nose properly and to compare the statistical analysis processed SPN value.

This paper presents an overview of research in progress in which the principal aim is the achievement of more natural and expressive modes of on-line communication with complexly structured data bases. A natural-language compiler has been constructed that accepts sentences in a user-extendable English subset, produces surface and deep-structure syntactic analyses, and uses a network of concepts to construct semantic interpretations formalized as computable procedures. The procedures are evaluated by a data management system that updates, modifies, and searches data bases that can be formalized as finite models of states of affairs. The system has been designed and programmed to handle large vocabularies and large collections of facts efficiently. Plans for extending the research vehicle to interface with a deductive inference component and a voice input-output effort are briefly described.