Query: FDC in semiconductor manufacturing

Candidate papers:

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

abstract: Many studies using sensor signals have been conducted in the field of fault detection and classification (FDC) for semiconductor manufacturing processes. This is because sensor signals generated in the semiconductor production process provide important information for predicting quality and yield of the finished product. However, as the process becomes more sophisticated and refined, normal and abnormal data with similar shape appears. They only show delicate differences and it is difficult to classify them using general classification algorithms. The purpose of this research is to present a preprocessing methodology for improving classification performance. The methodology consists of four steps based on signal segmentation and clustering methods. The experimental results illustrate the better performance of the proposed procedure.
author_names: Kyuchang Chang

Jun-Geol Baek
title: Univiariate Signal Preprocessing Methodology for Fault Detection in Semiconductor Manufacturing Process
venue: 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC)
year: 2019

abstract: Run to Run (R2R) control has become a common process regulating approach in the semiconductor industry. Conventionally, key process parameters are regulated with respect to the measured metrology data. However, wafer quality can be affected by complex factors related to equipment condition. The steady progress of information technologies enable us to collect and handle larger amounts of data, open new perspectives to explore the interactions between FDC (Fault Detection and Classification) data and product quality, i.e. wafer metrology, which should be considered simultaneously for developing improved R2R controllers. In this paper, the equipment condition is explicitly modeled and integrated into the core of a R2R controller, in order to accommodate this critical aspect of the system in deriving the control law, in order to reduce process variability in more effective way. Therefore, a new R2R control framework is proposed, which is called: Controller with Real time Equipment Condition (CREC) The effectiveness of the new control strategy is demonstrated and validated using the case study of a Chemical Mechanical Polishing (CMP) process, in collaboration with our industrial partner.
author_names: Wei-Ting Yang

Jakey Blue

Agnes Roussy

Marco S Reis

Jacques Pinaton
title: Advanced run to run controller in semiconductor manufacturing with real time equipment condition: APC: Advanced process control; AM: Advanced metrology
venue: 2018 29th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)
year: 2018

abstract: Many studies on the prediction of manufacturing results using sensor signals have been conducted in the field of fault detection and classification (FDC) for semiconductor manufacturing processes. However, fault diagnosis used to find clues as to root causes remains a challenging area. In particular, process monitoring using neural networks has been employed to only a limited extent because it is a black box model, making the relationships between input data and output results difficult to interpret in actual manufacturing settings, despite its high classification performance. In this paper, we propose a convolutional neural network (CNN) model, named FDC CNN, in which a receptive field tailored to multivariate sensor signals slides along the time axis, to extract fault features. This approach enables the association of the output of the first convolutional layer with the structural meaning of the raw data, making it possible to locate the variable and time information that represents process faults. In an experiment on a chemical vapor deposition process, the proposed method outperformed other deep learning models.
author_names: Ki Bum Lee

Sejune Cheon

Chang Ouk Kim
title: A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes
venue: IEEE Transactions on Semiconductor Manufacturing
year: 2017

abstract: Fault detection and classification (FDC) is important for semiconductor manufacturing to monitor equipment's condition and examine the potential cause of the fault. Each equipment in the semiconductor manufacturing process is often accompanied by a large amount of sensor readings, also called status variable identification (SVID) Identifying the key SVIDs accurately can make it easier for engineers to monitor the process and maintain the stability of the process and wafer productive yields. This article proposes using the random forests algorithm to analyze the importance of SVIDs of equipment sensors, automatically filters the key SVID by using <inline formula> <tex math notation="LaTeX"{k} /tex math>/inline formula> means, and integrates various machine learning methods to verify the key SVIDs and identify key processing time and steps. Upon the key parameters are identified, the key processing time and steps are investigated subsequently. The ensemble models constructed on <inline formula> <tex math notation="LaTeX"{k} /tex math>/inline formula> nearest neighbors <inline formula> <tex math notation="LaTeX"{k} /tex math>/inline formula>NNs) and naive Bayes classifiers are presented for classifying wafers as normal or abnormal. Data visualization of multidimensional key SVIDs is performed by using <inline formula> <tex math notation="LaTeX"{t} /tex math>/inline formula> distributed stochastic neighbor embedding <inline formula> <tex math notation="LaTeX"{t} /tex math>/inline formula> SNE) to create a graphical aid in FDC for the process engineer. An empirical study is conducted to validate the proposed data driven framework for fault detection and diagnostic. The experimental results demonstrate that the proposed framework can detect abnormality effectively with highly imbalanced classes and also gain insightful information about the key SVIDs and corresponding key processing time and steps. <italic>Note to Practitioners</italic> The challenges of equipment sensor data analytics in semiconductor manufacturing include building the classifier to detect wafer abnormality correctly, identification of key status variable identifications (SVIDs) and processing time and steps of abnormality, and data visualization of the abnormality in a high dimensional feature space. This article proposes a data driven framework for fault detection and classification (FDC) during the wafer fabrication process by incorporating several useful machine learning approaches. Experimental results demonstrate that the proposed data driven framework can supply quality fault detection performances and provide valuable information regarding the critical SVIDs and associated key processing time for fault diagnostic. The engineers can utilize the extracted fault patterns to perform a prognosis of the aging effect on process tools or modules for health management.
author_names: Shu-Kai S Fan

Chia-Yu Hsu

Du-ming Tsai

Fei He

Chun-Chung Cheng
title: Data Driven Approach for Fault Detection and Diagnostic in Semiconductor Manufacturing
venue: IEEE Transactions on Automation Science and Engineering
year: 2020

abstract: Nowadays, there are more attentions on cost control and yield enhancement in the semiconductor industry. Many manufacturers have the ability to collect the physical data called Status Variables Identification (SVID) by sensors embedded in the advanced machines during the manufacturing process. To maintain the competitive advantages, process monitoring and quick response to yield problem are pivotal in detecting the cause of the faults with the help of the sensor data. To state the physical nature of certain SVID, we usually transform SVID into Fault Detection and Classification parameters (FDC parameters) using statistical indicators. The data containing FDC parameters is called FDC data. This study aims to develop a multivariate analysis model to find out the crucial factors which may lead to process excursion among a large amount of FDC data. We proposed a 2 phase multivariate analysis framework: (1) the Least Absolute Shrinkage and Selection Operator (LASSO) is applied for key operation screening. (2) And Random Forest (RF) is used to rank the FDC parameters based on the key operations. Based on the results, domain engineers can quickly take actions responding to low yield problems.
author_names: Ying-Jen Chen

Bo-Cheng Wang

Jei-Zheng Wu

Yi-Chia Wu

Chen-Fu Chien
title: Big data analytic for multivariate fault detection and classification in semiconductor manufacturing
venue: 2017 13th IEEE Conference on Automation Science and Engineering (CASE)
year: 2017

abstract: With the evolutions in sensing technologies and the increasing use of advanced process control techniques, terabytes of data are recorded today from manufacturing equipment during the process of semiconductor devices. These large amounts of data are then operated by FDC systems to assess the overall condition of the equipment. In this paper, we consider the Exponential Hybrid wise Multiway Principal Components Analysis (E HMPCA) a PCA derived model that include an Exponentially Weighted Moving Average component, for the condition monitoring of a Chemical Vapor Deposition tool in STMicroelectronics Rousset 8" fab. In order to work directly on temporal signal from equipment sensors, the application of Dynamic Time Warping for data synchronization is also presented. A real occurred failure case is used to highlight the benefits of this approach on detection efficiency improvement and monitoring complexity reduction.
author_names: Alexis Thieullen

Mustapha Ouladsine

Jacques Pinaton
title: Application of PCA for efficient multivariate FDC of semiconductor manufacturing equipment
venue: ASMC 2013 SEMI Advanced Semiconductor Manufacturing Conference
year: 2013

abstract: As the high tech production system gets more complex, Equipment Condition Diagnosis (ECD) in semiconductor manufacturing for Fault Detection and Classification (FDC) is becoming more and more challenging than ever. This paper uses well known machine learning techniques such as Support Vector Machine (SVM) K Means clustering and Self Organizing Map (SOM) to develop an efficient ECD model. The process normality is checked by SVM following by decomposing the process dynamics via K Means. The abnormal observations are then projected into normal models built by Principal Component Analysis (PCA) Finally, by calculating the contribution values of out of control observations, different fault fingerprints with corresponding fault root are extracted again by K Means. The impact of clustering techniques is investigated by comparing K Means, SOM, and hierarchical clustering. An empirical study was conducted in collaboration with the leading semiconductor company in France to validate the methodology. The result shows that the proposed approach can effectively detect abnormal observations as well as automatically classify the fault fingerprints to give evident guidelines in explaining the detected faults.
author_names: Hamideh Rostami

Jakey Blue

Claude Yugma
title: Equipment Condition Diagnosis and Fault Fingerprint Extraction in Semiconductor Manufacturing
venue: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)
year: 2016

abstract: Tool behavior modeling and diagnosis is a big challenge in modern semiconductor fabrication, in particular, with high product mix and complicated technology nodes. Tool condition monitoring has been long conducted by implementing the Fault Detection and Classification (FDC) system and analyzing the large amount of real time sensor data collected during the process. The tool condition hierarchy developed in the previous work proposed that the excursions can be firstly detected by an overall condition indicator and then intuitively traced down to the level of sensor groups. In this paper, a Run to Run (R2R) variation monitoring technique is developed in order to correlate the tool excursions with individual sensors, instead of sensor groups, and thus to close the diagnostic gap in the hierarchy. Therefore, the tool condition can be efficiently monitored by one overall indicator and the detected tool faults can be systematically diagnosed at the sensor level.
author_names: Jakey Blue

Agnes Roussy

Jacques Pinaton
title: Run to Run sensor variation monitoring for process fault diagnosis in semiconductor manufacturing
venue: 2016 Winter Simulation Conference (WSC)
year: 2016

abstract: In the field of semiconductor manufacturing, people usually focus on the data of well designed databases, such as values from tool sensors, inline metrology data, or WAT data. These data are well structured and easily handled by engineers for further analysis. For example, integration engineers can compared CD metrology data to WAT data to catch out the root cause of abnormal device current, or equipment engineers can check FDC data to judge PM success or not.
author_names: Yan-Hsiu Liu

Ji Fu Kung

James Lin

Y B Hsu
title: Using text mining to handle unstructured data in semiconductor manufacturing Yan Hsiu Liu
venue: 2015 Joint e Manufacturing and Design Collaboration Symposium (eMDC) 2015 International Symposium on Semiconductor Manufacturing (ISSM)
year: 2015