Advanced Micro Devices Corporation

Drilling Info, Inc.

Enverus, Inc.

Alan Bruce Berezin

Reuben Quintanilla

Mark C Robinson

Jason Wayne Simmons

Justin Reed Birmingham

Samuel V Scarpino

Christopher Smith

Iraj Emami

Richard W Jarvis

An energy deposit determining system and method are provided that determines a productive lease or well. The system and method may be used for oil wells, gas wells, oil and gas explorations and/or mineral leases.

Energy deposit discovery system and method

A test structure which includes alternating grounded and floating conductive lines may be used to test the formation of conductive features on an integrated circuit topography. A number of intentional partial defects may be formed at predetermined locations along the test structure. During irradiation of the conductive lines from an electron source, the grounded conductive lines will appear darker than the floating conductive lines. If a short occurs between the conductive lines, due to an extra material defect, the portion of the floating line in the vicinity of the defect will also appear darkened. If an open appears along a grounded line, the non-grounded portion of the grounded line will be glowing. The grounded conductive lines are preferably grounded through a depletion-mode transistor. By applying a voltage to the transistor, the grounded line may be disconnected from ground, allowing electrical testing of the test structure.

Semiconductor test structure with intentional partial defects and method of use

Method and system for declustering semiconductor defect data in cooperation with wafer scanning tools. Classification codes are assigned to defect data stored in wafer scan records by first determining the local density of the defects within a preselected area of the wafer substrate and by determining the average density of all of the defects on the substrate. A search area is defined around a defect of interest, the search area having a radius proportional to the ratio of the local density to the average density. The defects are marked within the search area, and for each marked defect, a new search area is defined and additional defects are marked. At least one of the marked defects is then assigned with a 'cluster' classification code and the remaining defects within the search areas are assigned with a 'discardable' classification code. By increasing the search radius linearly as the density ratio increases, the system automatically and more accurately removes noise in defect data caused by wafer scratches and other defect clusters.

Method and system for declusturing semiconductor defect data

Method and a system for performing die yield prediction in cooperation with wafer scanning tools. A program analyzes data associated with defects on a wafer substrate, the substrate including multiple layers and multiple die. Files are read that contain defect data for selected layers of the substrate. The defect data includes defect type and defect size information. The defect data is then stacked to identify the layer of first occurrence of each defect and the number, i.e., count, of layers upon which it was redetected. A kill factor is then assigned to each of the defects according to a set of rules, each such rule specifying defect parameters that include layer of first occurrence, redetect count, defect size, and defect type. Failure probabilities, indicative of yield, are then computed for the defects according to the assigned kill factors. The failure probabilites are utilized to calculate the estimated die loss for selected wafers by layer and defect type.

Method and system for automated die yield prediction in semiconductor

Method and system for declustering semiconductor defect data

Method and a system for performing semiconductor manufacturing defect analysis in cooperation with wafer scanning tools. The system analyzes data associated with defects on a substrate. First, a subset of defects is selected from subpopulations of the defects according to preclassify rules, such that classification codes indicative of defect type may then be assigned to the selected defects based upon review thereof. Classification codes indicative of defect type are then randomly assigned to a number of unselected ones of the defects in each of the subpopulations, the number, for each defect type, being weighted according to the expected occurence of the defect type extrapolated from the selected defects of the same defect type.

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Austin, TX, United States of America

Alan Bruce Berezin