An improved method of measuring the three-dimensional surface roughness of a structure A focused ion beam is used to mill a succession of cross-sections or "slices" of the feature of interest at pre-selected intervals over a pre-selected measurement distance As each cross-section is exposed, a scanning electron microscope is used to measure the relevant dimensions of the feature Data from these successive "slices" is then used to determine the three-dimensional surface roughness for the feature

Method of measuring three-dimensional surface roughness of a structure

Equipment extracts components of spatial frequency that need to be evaluated in manufacturing a device or in analyzing a material or process out of edge roughness on fine line patterns and displays them as indexes. The equipment acquires data of edge roughness over a sufficiently long area, integrates a components corresponding to a spatial frequency region being set on a power spectrum by the operator, and displays them on a length measuring SEM. Alternatively, the equipment divides the edge roughness data of the sufficiently long area, computes long-period roughness and short-period roughness that correspond to an arbitrary inspection area by performing statistical processing and fitting based on theoretical calculation, and displays them on the length measuring SEM.

Evaluation method of fine pattern feature, its equipment, and method of semiconductor device fabrication

A method for classification includes receiving an image of an area of a semiconductor wafer on which a pattern has been formed, the area containing a location of interest. At least one value for one or more attributes of the location of interest are computed based upon topographical features of the location of interest in a three-dimensional (3D) map of the area.

Defect classification using topographical attributes

Systems and methods are presented to enhance and isolate residual signals indicative of the speckle field based on measurements taken by optically based metrology systems. Structural irregularities such as roughness and topographical errors give rise to light scattered outside of the specularly reflected component of the diffracted light. The scattered light interferes constructively or destructively with the specular component in a high numerical aperture illumination and detection system to form a speckle field. Various methods of determining residual signals indicative of the speckle field are presented. Furthermore, various methods of determining structural irregularities based on analysis of the residual signals are presented. In various embodiments, illumination with a high degree of spatial coherence is provided over any of a wide range of angles of incidence, multiple polarization channels, and multiple wavelength channels. In addition, diffracted light is collected over a wide range of angles of detection.

Apparatus and method of measuring roughness and other parameters of a structure

A measurement tool apparatus for evaluating degradation of pattern features in a semiconductor device manufacturing process. The measurement tool apparatus detects variations in the patterns from SEM images thereof and extracts pattern edge points along the circumference of each pattern. The measurement tool apparatus compares the pattern edge points to corresponding edge points of an ideal shape so as to determine deviation of the patterns. Metrics are derived from analysis of the deviations. The measurement tool apparatus uses the metrics in calculating an index representative of the geometry of edge spokes of the pattern, an indicator of the orientation of the edge spokes, and/or anticipated effects of the edge spokes on device performance.

Apparatus for evaluating degradation of pattern features

A method for evaluating a feature, consisting of receiving an image of the feature and determining respective coordinates of a plurality of points on an edge of the feature in the image. A figure having a non-circular non-linear shape is fitted to the plurality of points, and respective distances between the plurality of points and the figure are determined. A roughness parameter for the feature is computed in response to the respective distances. The method finds application in the analysis of critical dimensions (CD) of integrated circuits and, particularly, in the measurement of the edge roughness of their features and components as imaged by means of eg. The electron scanning microscopy (SEM).

Colin David Chase

Papers

Advanced roughness metrology