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Single crystal silicon wafers are the fundamental elements of semiconductor manufacturing industry. The wafers produced by Czochralski (CZ) process are very high quality single crystalline materials with known defects that are formed during the crystal growth or modified by further processing. While defects can be unfavorable for yield for some manufactured electrical devices, a group of defects like oxide precipitates can have both positive and negative impacts on the final device. The spatial distribution of these defects may be found by scattering techniques. However, due to limitations of scattering (i.e. light wavelength), many crystal defects are either poorly classified or not detected. Therefore a high throughput and accurate characterization of their shape and dimension is essential for reviewing the defects and proper classification. While scanning electron microscopy (SEM) can provide high resolution twodimensional images, atomic force microscopy (AFM) is essential for obtaining three-dimensional information of the defects of interest (DOI) as it is known to provide the highest vertical resolution among all techniques [1]. However AFM’s low throughput, limited tip life, and laborious efforts for locating the DOI have been the limitations of this technique for defect review for 300 mm wafers. To address these limitations of AFM, automatic defect review AFM has been introduced recently [2], and is utilized in this work for studying DOI on 300 mm silicon wafer. In this work, we carefully etched a 300 mm silicon wafer with a gaseous acid in a reducing atmosphere at a temperature and for a sufficient duration to decorate and grow the crystal defects to a size capable of being detected as light scattering defects [3]. The etched defects form a shallow structure and their distribution and relative size are inspected by laser light scattering (LLS). However, several groups of defects couldn’t be properly sized by the LLS due to the very shallow depth and low light scattering. Likewise, SEM cannot be used effectively for post-inspection defect review and classification of these very shallow types of defects. To verify and obtain accurate shape and three-dimensional information of those defects, automatic defect review AFM (ADR AFM) is utilized for accurate locating and imaging of DOI. In ADR AFM, non-contact mode imaging is used for non-destructive characterization and preserving tip sharpness for data repeatability and reproducibility. Locating DOI and imaging are performed automatically with a throughput of many defects per hour. Topography images of DOI has been collected and compared with SEM images. The ADR AFM has been shown as a non-destructive metrology tool for defect review and obtaining three-dimensional topography information.

Defects on a reticle are inspected, reviewed, and repaired by different tools. They are located by automated optical inspection (AOI); however, if the characteristic size of defects is similar to that of light and electron beam wavelengths, they are often unclassified or misclassified by AOI. Atomic force microscopes (AFM) along with electron microscopes are used for investigating defects located by AOI to distinguish false defects from real defects and effectively classify them. Both AFM and electron microscopes provide high resolution images. However, electron microscopy is known to be destructive and have less accuracy in 3rd dimension measurement compared to AFM [1]. On the other hand, AFM is known to have low throughput and limited tip life in addition to requiring significant effort to finding the defects. These limitations emanate from having to perform multiple large scans to find the defect locations, to compensate for stage coordinate inaccuracies, and to correct the mismatch between the AFM and the AOI tools. In this work we introduce automatic defect review (ADR) AFM for defect study and classification of EUV mask reticles that overcomes the aforementioned limitations of traditional AFM. This metrology solution is based on an AFM configuration with decoupled Z and XY scanners that makes it possible to collect large survey images with minimum out of plane motion. To minimize the stage errors and mismatch between the AFM and the AOI coordinates, the coordinates of fiducial markers are used for coarse alignment. In addition, fine alignment of the coordinates is performed using enhanced optical vision on marks on the reticle. The ADR AFM is used to study a series of phase defects identified by an AOI tool on a reticle. Locating the defects, imaging, and defect classification are performed using the ADR automation software and with the throughput of several defects per hour. In order to preserve tip life and data consistency, AFM imaging is performed in non-contact mode. The ADR AFM provides high throughput, high resolution, and non-destructive means for obtaining 3D information for defect review and classification. Therefore this technology can be used for in-line defect review and classification for mask repair.

While feature size in lithography process continuously becomes smaller, defect sizes on blank wafers become more comparable to device sizes. Defects with nm-scale characteristic size could be misclassified by automated optical inspection (AOI) and require post-processing for proper classification. Atomic force microscope (AFM) is known to provide high lateral and the highest vertical resolution by mechanical probing among all techniques. However, its low throughput and tip life in addition to the laborious efforts for finding the defects have been the major limitations of this technique. In this paper we introduce automatic defect review (ADR) AFM as a post-inspection metrology tool for defect study and classification for 300 mm blank wafers and to overcome the limitations stated above. The ADR AFM provides high throughput, high resolution, and non-destructive means for obtaining 3D information for nm-scale defect review and classification.

We characterized the roughness and side wall morphology of lithographically produced nanostructures of resistmultilayer materials using the recently developed three-dimensional atomic force microscopy (3D-AFM), which has an independent Z scanner intentionally tilted to a certain angle access the sidewall. In order to produce different degrees of Line Edge Roughness (LER) in a given photoresist sample, we systematically varied the Aerial Image Contrast (AIC) at a constant dose for optically imaged resists. We describe herein the effects of AIC on KrF resists that were observed by using 3D-AFM and Critical Dimension-Scanning Electron Microscopy (CD-SEM). High-resolution sidewall images and line profiles obtained by the 3D-AFM technique demonstrate its advantages to characterize the shape and roughness of device patterns throughout the development and pattern transfer process. Taken together, we demonstrate that AFM imaging can identify a trend in Sidewall Roughness (SWR) as a function of AIC effects on photoresist sample, and CDSEM imaging provided supporting evidence to establish the LER trend.

As the feature size of the semiconductor device is becoming increasingly smaller and the transistor has become three-dimensional (e.g. Fin-FET structure), a simple Line Edge Roughness (LER) is no longer sufficient for characterizing these devices. Sidewall Roughness (SWR) is now the more proper metric for these metrology applications. However, current metrology technologies, such as SEM and OCD, provide limited information on the sidewall of such small structures. The subject of this study is the sidewall roughness measurement with a three-dimensional Atomic Force Microscopy (AFM) using tilted Z scanner. This 3D AFM is based on a decoupled XY and Z scanning configuration, in which the Z scanner can be intentionally tilted to the side. A sharp conical tip is typically used for imaging, which provides high resolution capability on both the flat surfaces (top and bottom) and the steep sidewalls.

As the feature size in the lithography process continuously shrinks, accurate critical dimension (CD) measurement becomes more important. A new 3-dimensional (3D) metrology atomic force microscope (AFM) has been designed on a decoupled XY and Z scanner platform for CD and sidewall characterization. In this decoupled scanner configuration, the sample XY scanner moves the sample and is independent from the Z scanner which only moves the tip. The independent Z scanner allows the tip to be intentionally tilted to easily access the sidewall. This technique has been used to measure photoresist line patterns. The tilted scanner design allows CD measurement at the top, middle, and bottom of lines as well as roughness measurement along the sidewall. The method builds upon the standard AFM tip design resulting in a technique that a) maintains the same resolution as traditional AFM, b) can be used with sharpened tips for increased image resolution, and c) does not suffer from corner inaccessibility from large radius of curvature tips.

This paper presents a new approach for the classification of SAR targets that combines maximally decimated directional filter banks with higher-order neural networks (HONNs). HONNs are neural networks that permit the input signals to be multiplied together in addition to the more common operations such as weighting, summing, and pointwise nonlinearities of typical neural nets. HONNs have long been proposed as image classifiers whose performance can be made invariant to geometric transformations of the input imagery by using a method for decreasing dimensionality such as coarse coding. Most past image classifiers using HONNs have been tuned for carefully thresholded binary images, which generally cannot be derived from low-contrast imagery such as SAR without a significant loss of information. As an alternative, we use a novel HONN implementation that accepts gray-level input pixels using directional filter banks. In order to do this, a new modified tree-structured directional filter bank structure is proposed in this paper, where each of the subbands has directional visual information from a given input. The performance of the proposed approach is demonstrated with imagery taken from the public MSTAR database.

The work described in this paper addresses the use of the four-dimensional continuous wavelet transform (CWT) for automatic target recognition (ATR) and detection. This transform is an overcomplete representation with four coordinates: two spatial, t1 and t2; a rotational coordinate, (theta) ; and a scale coordinate, a. Two central ideas are discussed in connection with the transform's application to target recognition. The first is cross-scale reconstruction, which refers to exploiting the dominate presence of target features across scales. The second is utilizing the non-spatial coordinate space as a working environment for feature extraction and classification. This aspect is unique to the multidimensional wavelet transform, emanating from the inherent redundancy in the transform representation. Some conclusions are drawn in the last section regarding the utility of the CWT for ATR, and the transform's potential as an analysis tool.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The scanning tunneling microscope(STM) has the capability of mapping the topography of a surface with unprecedented resolution in both vertical and lateral directions. STM has been used to obtain real space images of individual atoms on a surface. In this paper we review the principles and technical aspects of STM, and give some examples of its applications both in air and vacuum. These include images of graphite, silicon, and indium on silicon. Several techniques of digital image processing developed to facilitate the analysis of STM data are also described.