Jun Ye

Empirical forms for the electron/atom elastic scattering cross sections from 0.1 to 30 keV

Empirical forms have been found for the total and differential elastic scattering cross sections for electron/atom scattering. The cross sections are valid over the range 0.1–30 keV and across the periodic table. The empirical forms of the cross sections are derived from trends in tabulated Mott scattering cross sections. The form of the total cross section is similar to a previously published cross section and is based on the screened Rutherford cross section. The fit to the differential Mott cross sections is decomposed into two parts, one part being of the same mathematical form as the screened Rutherford cross section σR, and the second part being an isotropic distribution σI. These two mathematical forms were chosen because they give a straightforward generation of random scattering angles. The screened Rutherford part of the differential scattering cross section is first fitted to the half‐angle of the Mott cross sections. This fit of the differential screened Rutherford is in turn reduced to a fit ...

An exact algorithm for self-calibration of two-dimensional precision metrology stages

We describe an algorithm that can achieve exact self-calibration for high-precision two-dimensional (2-D) metrology stages. Previous attempts to solve this problem have often given nonexact or impractical solutions. Self-calibration is the procedure of calibrating a metrology stage by an artifact plate whose mark positions are not precisely known. By assuming rigidness of the artifact plate, this algorithm extracts the stage error map from comparison of three different measurement views of the plate. The algorithm employs the orthogonal Fourier series to expand the stage error map, which allows fast numerical computation. When there is no random measurement noise, this algorithm exactly calibrates the stage error at those sites sampled by the mark array. In the presence of random measurement noise, the algorithm introduces a calibration error of about the same size as the random measurement noise itself, which is the limit to be achieved by any self-calibration algorithm. The algorithm has been verified by computer simulation with and without random measurement noise. Other possible applications of this algorithm are also discussed.

Self-calibration in two dimensions: the experiment

A two-dimensional self-calibration experiment obtains Cartesian traceability for high-precision tools. The calibration procedure incorporates group theory principles to solve our industrys two-dimensional calibration problem. With group theory, a Cartesian system is obtainable through mathematics; thus, eliminating the need for any certified standards. The calibration algorithm was developed by Jun Ye at Stanford University and funded by the Semiconductor Research Corporation (SRC) with collaboration from Hewlett Packard (HP) and IBM. The data was collected from Leicas LMS2000 and LMS2020 systems.

Self inspection of integrated circuits pattern defects using support vector machines

At various steps of integrated circuit manufacturing, defects need to be inspected to ensure process integrity. It is desirable to detect the defects based on the captured optical image itself without comparing to any reference image. Such a methodology of defect inspection without any reference image is called defect self inspection. In this article, we investigate the defect self inspection using the support vector machines, a pattern classification methodology, and present detection sensitivity of this approach.

Dynamic self-inspection of integrated circuit pattern defects

At various steps of integrated circuit manufacturing, defects need to be inspected to ensure process integrity. Based on the observation that good patterns usually repeat many times while defective patterns are different from others, we here propose an inspection methodology called dynamic self-inspection (DSI). We present a generic and systematic algorithm of DSI based on relative characteristic code space and code vectors. We also present a few specific examples of its implementations, using Euclidian distance between edge traces as relative characteristics.

Self-inspection of IC pattern defects

At various steps of IC manufacturing, defects need to be inspected to ensure process integrity. In this article, we describe a new methodology for defect inspection: defect self-inspection, i.e., defect detection based on a captured optical image itself without comparing to a reference image, where the reference image is a database image or an optical image from another die. Through intuitive understanding of human intelligence in detecting defects, we propose a systematic methodology of self-inspection based on pattern code space and code vectors. We describe the general methodology and present its implementations for two common types of defects as specific examples: detection of edge intrusion and protrusion (also known as mouse-bite) and detection of particle or contamination (also known as pinhole/pin-dot).

Computational Scanning Electron Microscopy

Current methods for reconstructing surface topography from SEM images are either not suitable at nanometer scales, restricted to simple shapes described by a limited number of parameters or involve time consuming steps. We describe a reconstruction algorithm whereby an initial surface is iteratively updated. This algorithm performs a full three dimensional reconstruction at the resolution of the SEM image. It employs accelerated techniques to compute a simulated SEM image at each iteration so that the simulated image is obtained in less than 10% of the time taken by current reported Monte Carlo methods. The simulated SEM images are compared with actual SEM images and this comparison leads to further refinement of the reconstructed surface. Further acceleration can be achieved using approximate models for the generation of SE‐I, SE‐II and backscattered electrons.

A review of mask errors on a variety of pattern generators

The question of how mask dimensional errors impact yield is becoming increasingly critical to VLSI manufacturing. Both the magnitude and the spatial correlation of CD and registration errors are believed important to this issue. As part of a program to characterize these errors, we have had one identical test mask made on 6 different state-of-the-art mask pattern generators with widely different architectures. The test mask provides information on both registration and feature-size errors in both x- and y-directions, and does so over distances of several centimeters with spacing between measurements as small as 1 /spl mu/m. More than 100000 data points have been collected from these test plates using a LMS2000 optical metrology system, and are analyzed in the spatial frequency domain where error contributors as small as 1 nm can be identified. All systems were found to have similar characteristics in that most error contributors occur at a number of machine-specific spatial frequencies correlated to the particular architecture and printing strategy of the machine. Comparison of raster to vector machines show that vector machines tend to have more periodic error contributors, especially in the high spatial frequency range, which is consistent with the more complex writing fields used to achieve higher throughput. >

X-ray diffraction microscopy: Reconstruction with partial magnitude and spatial a priori information

The goal is to nondestructively reconstruct the structure of a fabricated integrated circuit using x-ray diffraction measurement. In particular, to detect any deviation from the specification of the circuit design. Determining phase is critical. However, additional information exists as the phase of the design specification is known. Further phase information is determined by oversampling and a better estimate of the correct phase is obtained through convex optimization. The authors illustrate this technique with a simulated one dimensional example and a simple two dimensional experimental sample.

Reconstruction of pattern images from scanning electron microscope images

As the characteristic feature sizes of integrated circuits continue to shrink, both research and manufacturing are increasingly reliant on the scanning electron microscope (SEM) images. In this article, we propose a robust algorithm that automatically extracts the full edge contours from the SEM images and then converts the SEM images to pattern images even when the SEM images are corrupted by strong noise and lossy compression. This algorithm is expected to have many applications. An example of application—defect self-inspection—based on SEM conversion using this algorithm, is also shown.