Various methods and systems for utilizing design data in combination with inspection data are provided. One computer-implemented method for binning defects detected on a wafer includes comparing portions of design data proximate positions of the defects in design data space. The method also includes determining if the design data in the portions is at least similar based on results of the comparing step. In addition, the method includes binning the defects in groups such that the portions of the design data proximate the positions of the defects in each of the groups are at least similar. The method further includes storing results of the binning step in a storage medium.

Methods and systems for utilizing design data in combination with inspection data

A method and apparatus for automated cell analysis of biological specimens automatically scans at a low magnification to acquire images which are analyzed to determine candidate cell objects of interest. The low magnification images are converted from a first color space to a second color space. The color space converted image is then low pass filtered and compared to a threshold to remove artifacts and background objects from the candidate object of interest pixels of the color converted image. The candidate object of interest pixels are morphologically processed to group candidate object of interest pixels together into groups which are compared to blob parameters to identify candidate objects of interest which correspond to cells or other structures relevant to medical diagnosis of the biological specimen. The location coordinates of the objects of interest are stored and additional images of the candidate cell objects are acquired at high magnification. The high magnification images are analyzed in the same manner as the low magnification images to confirm the candidate objects of interest which are objects of interest. A high magnification image of each confirmed object of interest is stored for later review and evaluation by a pathologist.

Method and apparatus for automated image analysis of biological specimens

The Invention includes a method and apparatus which analyzes an image of an object to detect and identify defects in the object. The present invention utilizes a scanning technique which converts a 2-D image of the object into a 1-D image, a transformation technique which extracts relevant features from the image, and a fuzzy inferencing technique which utilizes the features generated by the transformation technique to detect and identify defects. The present invention preferably includes an off-line learning process which selects the optimum transform coefficients for a given set of defects and stores the corresponding features in a rulebase. Preferably, the wavelet transform is used as the transformation technique to provide an analysis of the image which is localized in the frequency and time domains. The present invention may also include an on-line learning process when the present invention is incorporated into a manufacturing process for real time inspection of the object being manufactured. The on-line learning process attempts to nullify the effects of noise which may be associated with the image sensors being used to read the image by using a similarity function to maintain a predetermined level of fuzziness of the inference engine of the present invention.

Method and apparatus for analyzing an image to detect and identify defects

A low-profile inserter for an angled infusion set comprises an inserter housing having a bottom wall, a retainer slidably connected to the inserter housing for movement between retracted and extended positions in a direction substantially parallel with the bottom wall, and a base member connected to the inserter housing. The retainer is adapted to releasably receive a cannula assembly, including a cannula connected to a cannula housing. The base member has a lower surface that is adapted to contact a skin outer surface. The lower surface and bottom wall together form an acute angle. With this arrangement, the cannula can be inserted subcutaneously at the acute angle with respect to the skin outer surface.

Transcutaneous inserter for low-profile infusion sets

An automated defect inspection system has been invented and is used on patterned wafers, whole wafers, broken wafers, partial wafers, sawn wafers such as on film frames, JEDEC trays, Auer boats, die in gel or waffle packs, MCMs, etc. and is specifically intended and designed for second optical wafer inspection for such defects as metalization defects (such as scratches, voids, corrosion, and bridging), diffusion defects, passivation layer defects, scribing defects, glassivation defects, chips and cracks from sawing, solder bump defects, and bond pad area defects.

Automated wafer defect inspection system and a process of performing such inspection

An integrated visual defect detection and classification system. The invention includes adaptive defect detection and image labeling, defect feature measures, and a knowledge based inference shell/engine for classification based on fuzzy logic. The combination of these elements comprises a method and system for providing detection and analysis of product defects in many application domains, such as semiconductor and electronic packaging manufacturing.

Debra M. Parisi

Papers

Automated defect classification system