Query: Dynamics-inspired feature extraction in semiconductor manufacturing processes

Candidate papers:

abstract: Abstract The ability to exploit data driven process control and decision making frameworks is rapidly becoming critical to success in semiconductor manufacturing. At the same time, advances in manufacturing equipment sensors has seen dramatic increases in sampling rates in recent years, which has led to the ability to capture transients effects in signals with higher fidelity than previously possible. It is known that data driven process control and decision making methodologies rely on the process of extraction of useful information from raw data signals. To that end, the current manuscript presents a novel methodology for extraction of information from data in the form of a feature set that faithfully and reliably depicts both the transient and stationary portions of the signals. The solution proposed is an automated dynamics inspired approach that looks to segment a signal into steady state and transient components before summarizing each segment into a set of relevant signatures. The steady state segments are summarized through a set of statistics and each transient is reduced to a set of parameters relating to the underlying system dynamics, such as settling time, rise time, overshoots, etc. The impactful novel information content of the resulting dynamics inspired feature set is evaluated by application to chamber matching, product defect level prediction and product quality characteristic prediction in etch and deposition processes executed in various tools across several modern 300 mm fabs.
author_names: Asad Arsalan Ul Haq

Dragan Djurdjanovic
title: Dynamics inspired feature extraction in semiconductor manufacturing processes
venue: Journal of Industrial Information Integration
year: 2019

abstract: Wafer maps contain information about various defect patterns on the wafer surface and automatic classification of these defects plays a vital role to find their root causes. Semiconductor engineers apply various methods for wafer defect classification such as manual visual inspection or machine learning based algorithms by manually extracting useful features. However, these methods are unreliable, and their classification performance is also poor. Therefore, this paper proposes a deep learning based convolutional neural network for automatic wafer defect identification (CNN WDI) We applied a data augmentation technique to overcome the class imbalance issue. The proposed model uses convolution layers to extract valuable features instead of manual feature extraction. Moreover, state of the art regularization methods such as batch normalization and spatial dropout are used to improve the classification performance of the CNN WDI model. The experimental results comparison using a real wafer dataset shows that our model outperformed all previously proposed machine learning based wafer defect classification models. The average classification accuracy of the CNN WDI model with nine different wafer map defects is 96.2% which is an increment of 6.4% from the last highest average accuracy using the same dataset.
author_names: Muhammad Saqlain

Qasim Abbas

Jong Yun Lee
title: A Deep Convolutional Neural Network for Wafer Defect Identification on an Imbalanced Dataset in Semiconductor Manufacturing Processes
venue: IEEE Transactions on Semiconductor Manufacturing
year: 2020

abstract: Abstract: Management and analytics for inline manufacturing process data has become a critical but increasingly complex task in the semiconductor fabrication industry. The importance of new methods for construction of informative features has been accentuated by advancements in the data collection technology employed in this industry, which has recently increased sampling rates for inline data from values below 1 Hz to frequencies in excess of 10Hz. In this paper, a new feature construction method is proposed that aims to extract as much of the information accessible with these increased sampling rates as possible, while simultaneously minimizing redundancy and user involvement. The proposed method results in a meaningful dynamics inspired feature set, which provides insight into the underlying process and equipment dynamics. The advantages offered by this feature set are established using data collected at several modern 300mm fabs, for chamber and tool matching tasks, as well as wafer defect level prediction.
author_names: Asad Ul Haq

Keren Wang

Dragan Djurdjanovic
title: Feature Construction for Dense Inline Data in Semiconductor Manufacturing Processes
year: 2016

abstract: In semiconductor manufacturing processes, it is important to quickly identify any signs of the occurrence of defects. We applied a time series clustering method to the signal data of processing equipment and obtained information related to the occurrence of defects. By using the information as the feature values of a prediction model, we were able to predict defects more accurately than by using only conventional feature values.
author_names: Daisuke Hamaguchi

Tomonari Masada

Takumi Eguchi
title: Feature Extraction from Equipment Sensor Signals with Time Series Clustering and Its Application to Defect Prediction
venue: 2020 International Symposium on Semiconductor Manufacturing (ISSM)
year: 2020

abstract: Reliable feature extraction, condition monitoring, and fault modeling are critical to understanding equipment degradation and implementing the proper maintenance decisions in manufacturing processes. Semiconductor manufacturing machines are highly sophisticated systems, consisting of multiple interacting components operating in highly variable operating conditions. This complicates performance monitoring since equipment condition must often be inferred through concurrent interpretation of multiple sensor readings originating from potentially very different subsystems of the tool. This paper presents an integrated approach to feature extraction, condition monitoring, and fault modeling applied to a set of standard built in sensors on a modern 300 mm technology industrial Plasma Enhanced Chemical Vapor Deposition (PECVD) tool. Linear Discriminant Analysis was utilized to determine the set of dynamic features that are the most sensitive to different tool conditions brought about by chamber cleaning or various faults. Gaussian Mixture Models of the dynamic feature distributions were used to statistically quantify changes of these features as the condition of the tool changed. In addition, four highly detrimental faults were analyzed to demonstrate the fault modeling methodology. Data collected over 8 months from a PECVD tool being operated by a major microelectronics manufacturer was used to verify the methodology. Top sensitive features from various faults observed in this period were examined and physical connections to the chamber condition were interpreted through their behavior.
author_names: Alexander Bleakie

Dragan Djurdjanovic
title: Feature extraction, condition monitoring, and fault modeling in semiconductor manufacturing systems
venue: Comput. Ind.
year: 2013

abstract: A variety of statistical and data mining techniques have been developed for the fault detection (FD) modeling of semiconductor manufacturing processes over the past three decades. However, few studies have analyzed which models are adequate for different types of fault data. In this paper, we define a FD model as an algorithm combining feature extraction, feature selection, and classification. We prepare six process data scenarios and collect data by simulating an etching tool. In total, 117 possible algorithm combinations are tested as FD models for the six datasets. With these test results, we conduct statistical analyses from two perspectives: 1) the algorithm perspective and 2) FD model perspective. From the algorithm perspective, we compare the performance of competing algorithms in the three model building steps using multiple comparison methods and discuss the advantages and disadvantages of individual algorithms. From the model perspective, we determine which algorithm combinations are recommended for FD models of the semiconductor process and explain why some combinations do not exhibit the expected performance. In both analyses, we interpret some results using 3 D plots.
author_names: Taehyung Lee

Chang Ouk Kim
title: Statistical Comparison of Fault Detection Models for Semiconductor Manufacturing Processes
venue: IEEE Transactions on Semiconductor Manufacturing
year: 2015

abstract: As semiconductor manufacturing processes become more complex and sophisticated, manufacturers demand high quality, efficient process monitoring and fault detection (FD) Segmentation feature extraction (SFE) has been shown to be successful in FD when the anomaly patterns are well described by feature extraction, but not in cases of complex anomaly patterns. This paper proposes a novel approach that combines feature extraction based and full trace analysis (FTA) capabilities in FD. First, a novel multivariate trace segmentation is applied to segment trace data based on defined patterns across key sensors, and then the pattern features and parameters are extracted within each segment. Second, full trace analysis generates adaptive guardbands on key sensors. These guardbands detect "anomaly" throughout the whole trace with a certain confidence interval. Third, a multivariate fault detection model is developed that combines SFE and FTA capabilities. Furthermore, multivariate data generation is developed to exhaustively validate combination and isolation of the proposed capabilities. Experimental results indicate that FTA can better capture the anomaly features with complex/atypical patterns, and the combined capability of SFE FTA exhibits better performance than each method applied individually.
author_names: Fei Li

Haoshu Cai

James R Moyne

Jimmy Iskandar

Michael Armacost

Jay Lee
title: Combining Feature Extraction Based and Full Trace Analysis Capabilities in Fault Detection: Methods and Comparative Analysis
venue: 2021 32nd Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)
year: 2021

abstract: Feature design and selection is challenging because of huge data volume and high mix production systems. Most engineers still rely on human experts to suggest the specific sensor channel and specific time frames of data from which to design the features. This study proposes a novel approach for important sensor screening to prioritize the useful sensor channels for FDC model development in semiconductor manufacturing. The proposed method can be used as a pre processing step prior to feature extraction, and the selected sensor channels can be leveraged by process engineers for finer feature design. In this research, firstly, time series alignment kernels (TSAKs) are proposed to handle multivariate trace data. Then, the proposed method combines 5 different time series alignment kernels (TSAKs) with a feature selection algorithm, minimum Redundancy Maximum Relevance (mRMR) to identify the important sensor channels. Furthermore, a TSAK+Kernel Principal Component Analysis (KPCA) algorithm is proposed for a visualization tool. Lastly, the TSAK+Support Vector Machine (SVM) is employed for results validation. In this study, validation of the proposed method is based on both open source datasets and the proprietary datasets from a real production line.
author_names: Feng Zhu

Xiaodong Jia

Marcella Miller

Xinong Li

Fei Li

Yinglu Wang

Jay Lee
title: Methodology for Important Sensor Screening for Fault Detection and Classification in Semiconductor Manufacturing
venue: IEEE Transactions on Semiconductor Manufacturing
year: 2021

abstract: Big data related to manufacturing applications has the traits such as great quantity, multi sources, low value density, high complexity, and dynamic state. Traditional feature extraction methods are incapable of meeting real time demands. Therefore, a robust incremental on line feature extraction method based on PCA (Principal Component Analysis) RIPCA (Robust Incremental Principal Component Analysis) is proposed. RIPCA adopts a sliding window to update new coming data stream and to filter outliers. The proposed method could ensure the accuracy of data analysis and meet real time demands of big data processing for manufacturing applications. A test data set based on a semiconductor manufacturing process containing 1567 records with 590 features is used to demonstrate the availability of the proposed method. Experimental results show that the method can effectively extract features of the data stream in real time with high accuracy.
author_names: Xianguang Kong

Jiantao Chang

Meng Niu

Xiaoyu Huang

Jihu Wang

Shing I Chang
title: Research on real time feature extraction method for complex manufacturing big data
year: 2018

abstract: Nowadays, more attention has been placed on cost reductions and yield enhancement in the semiconductor industry. During the manufacturing process, a considerable amount of sensor data called status variables identification (SVID) is collected by sensors embedded in advanced machines. This data is a valuable source for data driven automatic fault detection and diagnosis at an early manufacturing stage to maintain competitive advantages. However, wafer processing times vary slightly from wafer to wafer, resulting in variable length signal data. The conventional approaches use much condensed data called fault detection and classification (FDC) data made by manually designed feature extraction. Or, recent deep learning approaches assume that all wafers have the same processing time, which is impotent to the variable length SVID. To detect and diagnose faults directly from the variable length SVID, we propose a self attentive convolutional neural network. In experiments using real world data from a semiconductor manufacturer, the proposed model outperformed other deep learning models with less training time and showed robustness at different sequence lengths. Compared to FDC data, SVID data showed better fault detection performance. Without manually investigating the lengthy sensor signals, abnormal sensor value patterns were found at the time specified by the model.
author_names: Eunji Kim

Sungzoon Cho

Byeongeon Lee

Myoungsu Cho
title: Fault Detection and Diagnosis Using Self Attentive Convolutional Neural Networks for Variable Length Sensor Data in Semiconductor Manufacturing
venue: IEEE Transactions on Semiconductor Manufacturing
year: 2019