Search query: semiconductor deep learning

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abstract: Manual classification of particle defects on semiconductor wafers is labor intensive, which leads to slow solutions and longer learning curves on product failures while being prone to human error. This work explores the promise of deep learning for the classification of the chemical composition of these defects to reduce analysis time and inconsistencies due to human error, which in turn can result in systematic root cause analysis for sources of semiconductor defects. We investigate a deep convolutional neural network (CNN) for defect classification based on a combination of scanning electron microscopy (SEM) images and energy dispersive x ray (EDX) spectroscopy data. SEM images of sections of semiconductor wafers that contain particle defects are fed into a CNN in which the defects' EDX spectroscopy data is merged directly with the CNN's fully connected layer. The proposed CNN classifies the chemical composition of semiconductor wafer particle defects with an industrially pragmatic accuracy. We also demonstrate that merging spectral data with the CNN's fully connected layer significantly improves classification performance over CNNs that only take either SEM image data or EDX spectral data as an input. The impact of training data collection and augmentation on CNN performance is explored and the promise of transfer learning for improving training speed and testing accuracy is investigated.
author_names: Jared O'Leary

Kapil U Sawlani

Ali Mesbah
title: Deep Learning for Classification of the Chemical Composition of Particle Defects on Semiconductor Wafers
venue: IEEE Transactions on Semiconductor Manufacturing
year: 2020

abstract: Quality inspection in semiconductor manufacturing is of great importance in the modern industries. In the recent years, intelligent data driven condition monitoring methods have been successfully developed and applied in the industrial applications. However, despite the promising condition monitoring performance, the existing methods generally assume the training and testing data are from the same distribution. In practice, due to variations in manufacturing process, the collected data are usually subject to different distributions in different operating conditions, that significantly deteriorates the performance of the data driven methods. To address this issue, this paper proposes a deep learning based domain adaptation method for fault diagnosis in semiconductor manufacturing. The maximum mean discrepancy metric is optimized on the learned high level data representation in the deep neural network. Experimental results on a real world semiconductor manufacturing dataset suggest the proposed method offers an effective and generalized data driven fault diagnosis approach for quality inspection.
author_names: Moslem Azamfar

Xiang Li

Jay Lee
title: Deep Learning Based Domain Adaptation Method for Fault Diagnosis in Semiconductor Manufacturing
venue: IEEE Transactions on Semiconductor Manufacturing
year: 2020

abstract: Abstract An essential concept in technology management is the early detection of valuable patents. Traditional classification approaches have been utilized to identify effective patents based on the extracted patent topics and indices. However, they cannot consider detailed contextual information or relatively long word sequences in patent documents. In this study, we propose a patent grade evaluation framework based on a deep learning model that can capture the detailed semantic features of patent text. Therefore, this study adopts both a convolution neural network and bidirectional long short term memory with structured patent text data consisting of abstracts and claims for the classification of three levels of patent grades measured in terms of the average number of forward citations per annum. We further exploit the patent indices identified in the early stage as additional inputs to the model to increase the accuracy. Our model has realized over 75% precision and recall in identifying top grade semiconductor patents granted by the USPTO from 2000 to 2015. We anticipate that our deep learning based framework with patent text and indices will play a significant supporting role in mergers and acquisitions, investment decisions, and corporate planning through the early stage evaluation of a large number of patents.
author_names: Park Chung

So Young Sohn
title: Early detection of valuable patents using a deep learning model: Case of semiconductor industry
year: 2020

abstract: A deep learning artificial neural network (NN) combined with the particle swarm optimization (PSO) method has been proposed to inversely design the semiconductor laser with high accuracy and computational speed. This method is exempt from the single solution problem of tandem NN and can be highly useful to extract the possible problematic parameters in the failure analysis of a device. The light current curves and small signal responses have been tested against the benchmarks calculated by the traveling wave model to demonstrate the NN's robustness and efficiency in simulating the laser behavior for further use in the inverse design by PSO.
author_names: Zi-Yan Ma

Yu Li
title: Parameter extraction and inverse design of semiconductor lasers based on the deep learning and particle swarm optimization method.
venue: Optics express
year: 2020

abstract: Feature length extraction in cross sectional SEM (Scanning Electron Microscope) images of modern semiconductor devices is time consuming and laborious task. In this work, for the first time, automated measurement tool based on deep learning (DL) technology was developed; object detection model for determination of coordinate of each unit pattern and semantic segmentation model for obtaining the coordinate of boundary of each region (mask, substrate, and background) By combining results of these two models, typical features such as width and depth etc. are precisely and immediately extracted. We applied this tool to sample trench patterns and realized two orders faster extraction speed than manual operation.
author_names: Yutaka Okuyama

Takeshi Ohmori
title: Deep Learning based Automated Measurement Method for Cross sectional SEM Images in Semiconductor Devices
venue: Microscopy and Microanalysis
year: 2020

abstract: Abstract. Defects in semiconductor processes can limit yield, increase overall production cost, and also lead to time dependent critical component failures. Current state of the art optical and electron beam (EB) inspection systems rely on rule based techniques for defect detection and classification, which are usually rigid in their comparative processes. This rigidity limits overall capability and increases relative engineering time to classify nuisance defects. This is further challenged due to shrinkage of pattern dimensions for advanced nodes. We propose a deep learning based workflow that circumvents these challenges and enables accurate defect detection, classification, and localization in a unified framework. In particular, we train convolutional neural network based models using high resolution EB images of wafers patterned with various types of intentional defects and achieve robust defect detection and classification performance. Furthermore, we generate class activation maps to demonstrate defect localization capability of the model "without" explicitly training it with defect location information. To understand the underlying decision making process of these deep models, we analyze the learned filters in pixel space and Fourier space and interpret the various operations at different layers. We achieve high sensitivity (97% and specificity (100% along with rapid and accurate defect localization. We also test performance of the proposed workflow on images from two distinct patterns and find that in order to retain high accuracy a modest level of retraining is necessary.
author_names: Dhruv V Patel

Ravi Bonam

Assad A Oberai
title: Deep learning based detection, classification, and localization of defects in semiconductor processes
year: 2020

abstract: Recently, the transportation capability of the automated material handling system (AMHS) has emerged as a major barrier to the semiconductor fabrication facility (FAB) because it can limit the FAB production capacity. In this study, we propose a prediction method for a machine allocation problem of production scheduling in consideration with the AMHS's constraints. The proposed method dynamically targets a machine for the next process by identifying diverse production conditions. We use a deep neural network based dynamic scheduling method considering the overall production environment, which includes the remaining processing time, facility states, transportation time and traffic congestion, work in process distribution, and intermediate buffer states. To demonstrate the superiority and efficiency of the proposed method, we conducted experimental studies to compare the proposed model with the existing priority rule based and analytic models under the static and dynamic environments. From the results, we verify that the proposed dynamic scheduling system can enhance the performance of existing AMHS and reduce machine starvation and production losses.
author_names: Haejoong Kim

Dae-Eun Lim

Sangmin Lee
title: Deep Learning Based Dynamic Scheduling for Semiconductor Manufacturing With High Uncertainty of Automated Material Handling System Capability
venue: IEEE Transactions on Semiconductor Manufacturing
year: 2020

abstract: This thesis presents some methodological and experimental contributions to a deep learning based approach for the automatic classification of microscopic defects in silicon wafers with context information. Canonical image classification approaches have the limitation of utilizing only the information contained in the images. This work overcomes this limitation by using some context information about the defects to improve the current automatic classification system.
author_names: Simone Arena
title: A deep learning based approach for defect classification with context information in semiconductor manufacturing
year: 2020

abstract: In this paper, we present a system for the detection and classification of defects in semiconductor units. The proposed system consists of three stages: proposal generation stage, defect detection stage and refinement stage. In the proposal generation stage, changes on the target unit are detected using a novel change detection approach. In the second stage, a deep neural network is used to classify detected regions into either defective or non defective regions. Non defective regions are regions exhibiting allowable changes due to factors such as lighting conditions and subtle differences in manufacturing. The defect detection stage achieves up to 94.3% accuracy. In practice, defects that are smaller than a specified tolerance size are ignored by manufacturers. The tolerance size depends on the defect types and is determined based on risk factors. In order to ignore such defects, our approach includes a final refinement stage wherein the detected defects are categorized by a stacking based ensemble classifier into different classes. The refined system achieves up to 97.88% overall detection accuracy. The presented system is immediately applicable to different types of defects on die, epoxy and substrate. Inputs of the system can be either color or grayscale images.
author_names: Bashar M Haddad

Samuel F Dodge

Lina Karam

Nital S Patel

Martin W Braun
title: Locally Adaptive Statistical Background Modeling With Deep Learning Based False Positive Rejection for Defect Detection in Semiconductor Units
venue: IEEE Transactions on Semiconductor Manufacturing
year: 2020

abstract: Due to advances in semiconductor processing technologies, each slice of a semiconductor is becoming denser and more complex, which can increase the number of surface defects. These defects should be caught early and correctly classified in order help identify the causes of these defects in the process and eventually help to improve the yield. In today's semiconductor industry, visible surface defects are still being inspected manually, which may result in erroneous classification when the inspectors become tired or lose objectivity. This paper presents a vision based machine learning based method to classify visible surface defects on semiconductor wafers. The proposed method uses deep learning convolutional neural networks to identify and classify four types of surface defects: center, local, random, and scrape. Experiments were performed to determine its accuracy. The experimental results showed that this method alone, without additional refinement, could reach a top accuracy in the range of 98% to 99% Its performance in wafer defect classification shows superior performance compared to other machine learning methods investigated in the experiments.
author_names: Jong-Chih Chien

Mingtao Wu

Jiann-Der Lee
title: Inspection and Classification of Semiconductor Wafer Surface Defects Using CNN Deep Learning Networks
year: 2020