This paper presents a method to reconstruct 3D surfaces of silicon wafers from 2D images of printed circuits taken with a scanning electron microscope. Our reconstruction method combines the physical model of the optical acquisition system with prior knowledge about the shapes of the patterns in the circuit; the result is a shape-from-shading technique with a shape prior. The reconstruction of the surface is formulated as an optimization problem with an objective functional that combines a data-fidelity term on the microscopic image with two prior terms on the surface. The data term models the acquisition system through the irradiance equation characteristic of the microscope; the first prior is a smoothness penalty on the reconstructed sur...
The effect of non-flatness of semiconductor wafers on characteristics of manufactured devices is sho...
With the continuous effort of the electronic industry in miniaturizing device size, the task of insp...
In this paper we introduce several novel random neural network [Gelenbe89, Gelenbe90, Gelenbe93, Gel...
This paper presents a method to reconstruct 3D surfaces of silicon wafers from 2D images of printed ...
Abstract—This paper presents a method to reconstruct 3D surfaces of silicon wafers from 2D images of...
We propose a shape-from-shading method to reconstruct surfaces of silicon wafers from images of prin...
The paper discusses the approach of using single detector system to classify the photo resist surfac...
In this paper, we propose and investigate several solutions to a difficult inverse problem which a...
In this paper, we propose and investigate several solutions to a difficult inverse problem which a...
In this paper we apply neural network techniques and physically based models to determine the surfac...
The ability to make three-dimensional measurements of surface topography is important to the control...
Accurate metrology techniques for semiconductor devices are indispensable for controlling the manufa...
The design of biomaterial surfaces relies heavily on the ability to accurately measure and visualize...
As the electronic industry advances rapidly, the shrunk dimension of the device leads to more string...
This article develops algorithms for the characterization and the visualization of micro-scale featu...
The effect of non-flatness of semiconductor wafers on characteristics of manufactured devices is sho...
With the continuous effort of the electronic industry in miniaturizing device size, the task of insp...
In this paper we introduce several novel random neural network [Gelenbe89, Gelenbe90, Gelenbe93, Gel...
This paper presents a method to reconstruct 3D surfaces of silicon wafers from 2D images of printed ...
Abstract—This paper presents a method to reconstruct 3D surfaces of silicon wafers from 2D images of...
We propose a shape-from-shading method to reconstruct surfaces of silicon wafers from images of prin...
The paper discusses the approach of using single detector system to classify the photo resist surfac...
In this paper, we propose and investigate several solutions to a difficult inverse problem which a...
In this paper, we propose and investigate several solutions to a difficult inverse problem which a...
In this paper we apply neural network techniques and physically based models to determine the surfac...
The ability to make three-dimensional measurements of surface topography is important to the control...
Accurate metrology techniques for semiconductor devices are indispensable for controlling the manufa...
The design of biomaterial surfaces relies heavily on the ability to accurately measure and visualize...
As the electronic industry advances rapidly, the shrunk dimension of the device leads to more string...
This article develops algorithms for the characterization and the visualization of micro-scale featu...
The effect of non-flatness of semiconductor wafers on characteristics of manufactured devices is sho...
With the continuous effort of the electronic industry in miniaturizing device size, the task of insp...
In this paper we introduce several novel random neural network [Gelenbe89, Gelenbe90, Gelenbe93, Gel...