Ryo Nakagaki

A VQ-based blind image restoration algorithm

In this paper, learning-based algorithms for image restoration and blind image restoration are proposed. Such algorithms deviate from the traditional approaches in this area, by utilizing priors that are learned from similar images. Original images and their degraded versions by the known degradation operator (restoration problem) are utilized for designing the VQ codebooks. The codevectors are designed using the blurred images. For each such vector, the high frequency information obtained from the original images is also available. During restoration, the high frequency information of a given degraded image is estimated from its low frequency information based on the codebooks. For the blind restoration problem, a number of codebooks are designed corresponding to various versions of the blurring function. Given a noisy and blurred image, one of the codebooks is chosen based on a similarity measure, therefore providing the identification of the blur. To make the restoration process computationally efficient, the principal component analysis (PCA) and VQ-nearest neighbor approaches are utilized. Simulation results are presented to demonstrate the effectiveness of the proposed algorithms.

Dose and focus estimation using top-down SEM images

As design rules shrink and process windows become smaller, strict process control is becoming increasingly important. The two primary process parameters in the photolithography process, exposure dose and focus, require strict control in order to maintain the photoresist profile. This paper presents the second stage of an approach towards monitoring the semiconductor photolithogprhay process by using critical dimension-scanning electron microscopy. In the former paper, we propsed a method that quantifies the photoresist pattern profile variation caused by dose or focus variation. In this paper, a new method for estimating the variation in exposure dose and focus is presented. Top-down SEM imagse are intrinsically limited in the inability to observe the re-entrant profile. This limitation has been overcome through the use of two tyeps of common patterns: island patterns and window patterns. Island patterns, such as isolated line patterns, have a tapered profile for negative defocus, while window patterns, such as isolated spaces patterns, have an inverse tapered profile for negative defocus. Using both types of patterns allows the focus deviation to be monitored, whether positive or negative defocus. The behavior of the two types of patterns is considered here based on photolithography simulation, and a new algorithm for estimating the exposure dose and focsu variation is proposed.

Automatic recognition of defect areas on a semiconductor wafer using multiple scanning electron microscope images

A technique for high-precision and automatic recognition of defect areas on a semiconductor wafer using scanning electron microscope (SEM) images is proposed. The proposed technique inputs multiple SEM images formed by selectively detecting secondary electrons and backscattered electrons emitted from the specimen by irradiating with primary electrons, and defect areas are then automatically recognized by comparison with reference images. The number of detected secondary electrons and backscattered electrons is highly dependent on the surface roughness of the defect areas, namely the height and depth of defects; therefore, a surface-roughness analysis from input images is conducted and the result is used to determine the mixing proportion for multiple difference images. The proposed technique aims to obtain high recognition accuracy for process wafers that contain various kinds of defects with a wide variety of height and depth. The technique provides effective pre-processing for automating the classification of defects, and is expected to contribute to improvements to the efficacy of process monitoring and yield management in the fabrication of semiconductor devices. Experimental results with two process wafers (involving 200 defect samples, each of which belongs to one of the nine defect classes) have confirmed that the proposed technique is capable of automatic recognition of defect areas with an accuracy of 98.9%.

Automatic recognition of circuit patterns on semiconductor wafers from multiple scanning electron microscope images

A technique is proposed for high-precision, automatic recognition of circuit patterns on a semiconductor wafer from multiple scanning electron microscope (SEM) images. This technique uses multiple SEM images obtained by selective detection of secondary and backscattered electrons emitted from a wafer surface irradiated with primary electrons. It automatically detects circuit patterns in these images. The appearances of circuit patterns in SEM images vary widely depending on the structure, the material and the pattern layout. The proposed technique can cope with such a large variation in pattern appearance by adaptively selecting two recognition methods based on pattern structure and pattern density. Other information, such as the images to be processed and the contrast between pattern and non-pattern regions, is also utilized for recognition. The technique provides effective preprocessing for automating defect classification. It is expected to improve the efficacy of process monitoring and yield management in semiconductor device fabrication. Experimental results for five wafers (from which 421 circuit pattern images were obtained) demonstrate that the proposed technique can automatically recognize circuit patterns with an accuracy of 99.8%.

A VQ-based image restoration algorithm

In this paper, we develop a novel VQ-based image restoration algorithm. The mapping between high frequency information in the original images and low frequency information in the corresponding degraded ones is established and stored in the VQ codebooks. Prototype images are used, which belong to the same class of images. During restoration, the high frequency information of a given degraded image is estimated from its low frequency information based on the designed codebook. To make the restoration process computationally efficient, the principal component analysis (PCA) and VQ-nearest neighborhood approaches are utilized. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

Fuzzy selective voting classifier with defect extraction based on comparison within an image

Semiconductor visual inspection is necessary for production yield control. Defect classification is a key procedure in determing defect sources. Auttomization of this procedure is required in order to achieve efficient and high-yield production. In the present paper, an automatic defect classification (ADC) algorithm for a semiconductor inspection is proposed. The ADC algorithm consists of the following three parts; 1) A defect extraction algorithm to achieve high-sensitivity defect extraction even in regions in which the brightness is unstable due to optical interference at a thin layer. 2) An appearance feature calculation from a color image inside the defect region extracted from 1). 3) A unique training type classifier called the fuzzy selective voting classifier (FSVC), which calculates the weight for each appearance feature in order to achieve accurate classification even when the discriminancy of each feature is different. The performance of the developed ADC algorithm has been evaluated using defect acquired from an actual production line. The accuracy of the classification was 85.9% and the false rejection rate was 93%.

A VQ-based blur identification algorithm

The estimation of the point spread function (PSF) of the degradation system is often a necessary first step in the restoration of blurred images. A novel vector quantization (VQ)-based blur identification algorithm is presented. A number of codebooks are designed corresponding to various versions of the blurring function. Prototype images blurred by each candidate blur are used. Only the non-flat regions for specific frequency bands are represented by the entries in the codebooks. Given a noisy and blurred image, one of the codebooks is chosen based on a similarity measure, therefore providing the identification of the blur. Simulations are performed for various blurring functions and noise levels. The results demonstrate the effectiveness of the proposed algorithms.