Unsupervised Feature Extraction Techniques for Plasma Semiconductor Etch Processes

As feature sizes on semiconductor chips continue to shrink plasma etching is becoming a more and more critical process in achieving low cost high-volume manufacturing. Due to the highly complex physics of plasma and chemical reactions between plasma species, control of plasma etch processes is one of the most di±cult challenges facing the integrated circuit industry. This is largely due to the di±culty with monitoring plasmas. Optical Emission Spectroscopy (OES) technology can be used to produce rich plasma chemical information in real time and is increasingly being considered in semiconductor manufacturing for process monitoring and control of plasma etch processes. However, OES data is complex and inherently highly redundant, necessitating the development of advanced algorithms for e®ective feature extraction. In this thesis, three new unsupervised feature extraction algorithms have been proposed for OES data analysis and the algorithm properties have been explored with the aid of both arti¯cial and industrial benchmark data sets. The ¯rst algorithm, AWSPCA (AdaptiveWeighting Sparse Principal Component Analysis), is developed for dimension reduction with respect to variations in the analysed variables. The algorithm gener- ates sparse principle components while retaining orthogonality and grouping correlated variables together. The second algorithm, MSC (Max Separation Clustering), is devel- oped for clustering variables with distinctive patterns and providing e®ective pattern representation by a small number of representative variables. The third algorithm, SLHC (Single Linkage Hierarchical Clustering), is developed to achieve a complete and detailed visualisation of the correlation between variables and across clusters in an OES data set. The developed algorithms open up opportunities for using OES data for accurate pro- cess control applications. For example, MSC enables the selection of relevant OES variables for better modeling and control of plasma etching processes. SLHC makes it possible to understand and interpret patterns in OES spectra and how they relate to the plasma chemistry. This in turns can help engineers to achieve an in-depth under- standing of underlying plasma processes

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.Framework of the IQONIC Project; European Union’s Horizon 2020 Research and Innovation Program

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes

Deep CNN-Based Automated Optical Inspection for Aerospace Components

ABSTRACT The defect detection problem is of outmost importance in high-tech industries such as aerospace manufacturing and is widely employed using automated industrial quality control systems. In the aerospace manufacturing industry, composite materials are extensively applied as structural components in civilian and military aircraft. To ensure the quality of the product and high reliability, manual inspection and traditional automatic optical inspection have been employed to identify the defects throughout production and maintenance. These inspection techniques have several limitations such as tedious, time- consuming, inconsistent, subjective, labor intensive, expensive, etc. To make the operation effective and efficient, modern automated optical inspection needs to be preferred. In this dissertation work, automatic defect detection techniques are tested on three levels using a novel aerospace composite materials image dataset (ACMID). First, classical machine learning models, namely, Support Vector Machine and Random Forest, are employed for both datasets. Second, deep CNN-based models, such as improved ResNet50 and MobileNetV2 architectures are trained on ACMID datasets. Third, an efficient defect detection technique that combines the features of deep learning and classical machine learning model is proposed for ACMID dataset. To assess the aerospace composite components, all the models are trained and tested on ACMID datasets with distinct sizes. In addition, this work investigates the scenario when defective and non-defective samples are scarce and imbalanced. To overcome the problems of imbalanced and scarce datasets, oversampling techniques and data augmentation using improved deep convolutional generative adversarial networks (DCGAN) are considered. Furthermore, the proposed models are also validated using one of the benchmark steel surface defects (SSD) dataset

Numerical modeling study of a neutron depth profiling (NDP) system for the Missouri S&T reactor

”For decades, Neutron Depth Profiling has been used for the non-destructive analysis and quantification of boron in electronic materials and lithium in lithium ion batteries. NDP is one of the few non-destructive analytical techniques capable of measuring the depth profiles of light elements to depths of several microns with nanometer spatial resolution. The technique, however, is applicable only to a handful of light elements with large neutron absorption cross sections. This work discusses the possibility of coupling Particle Induced X-ray Emission spectroscopy with Neutron Depth Profiling to yield additional information about the depth profiles of other elements within a material. The technical feasibility of developing such a system at the Missouri University of Science and Technology Reactor (MSTR) beam port is discussed. This work uses a combination of experimental neutron flux measurements with Monte Carlo radiation transport calculations to simulate a proposed NDP-PIXE apparatus at MSTR. In addition, the possibility of implementing an Artificial Neural Network to perform automated data analysis of NDP is presented. It was found that the performance of the Artificial Neural Network is at least as accurate as traditional processing approaches using stopping tables but with the added advantage that the Artificial Neural Network method requires fewer geometric approximations and accounts for all charged particle transport physics implicitly”--Abstract, page iii

Virtual metrology for plasma etch processes.

Plasma processes can present dicult control challenges due to time-varying dynamics and a lack of relevant and/or regular measurements. Virtual metrology (VM) is the use of mathematical models with accessible measurements from an operating process to estimate variables of interest. This thesis addresses the challenge of virtual metrology for plasma processes, with a particular focus on semiconductor plasma etch. Introductory material covering the essentials of plasma physics, plasma etching, plasma measurement techniques, and black-box modelling techniques is rst presented for readers not familiar with these subjects. A comprehensive literature review is then completed to detail the state of the art in modelling and VM research for plasma etch processes. To demonstrate the versatility of VM, a temperature monitoring system utilising a state-space model and Luenberger observer is designed for the variable specic impulse magnetoplasma rocket (VASIMR) engine, a plasma-based space propulsion system. The temperature monitoring system uses optical emission spectroscopy (OES) measurements from the VASIMR engine plasma to correct temperature estimates in the presence of modelling error and inaccurate initial conditions. Temperature estimates within 2% of the real values are achieved using this scheme. An extensive examination of the implementation of a wafer-to-wafer VM scheme to estimate plasma etch rate for an industrial plasma etch process is presented. The VM models estimate etch rate using measurements from the processing tool and a plasma impedance monitor (PIM). A selection of modelling techniques are considered for VM modelling, and Gaussian process regression (GPR) is applied for the rst time for VM of plasma etch rate. Models with global and local scope are compared, and modelling schemes that attempt to cater for the etch process dynamics are proposed. GPR-based windowed models produce the most accurate estimates, achieving mean absolute percentage errors (MAPEs) of approximately 1:15%. The consistency of the results presented suggests that this level of accuracy represents the best accuracy achievable for the plasma etch system at the current frequency of metrology. Finally, a real-time VM and model predictive control (MPC) scheme for control of plasma electron density in an industrial etch chamber is designed and tested. The VM scheme uses PIM measurements to estimate electron density in real time. A predictive functional control (PFC) scheme is implemented to cater for a time delay in the VM system. The controller achieves time constants of less than one second, no overshoot, and excellent disturbance rejection properties. The PFC scheme is further expanded by adapting the internal model in the controller in real time in response to changes in the process operating point

Quantitative analysis of spectroscopic Low Energy Electron Microscopy data: High-dynamic range imaging, drift correction and cluster analysis

For many complex materials systems, low-energy electron microscopy (LEEM) offers detailed insights into morphology and crystallography by naturally combining real-space and reciprocal-space information. Its unique strength, however, is that all measurements can easily be performed energy-dependently. Consequently, one should treat LEEM measurements as multi-dimensional, spectroscopic datasets rather than as images to fully harvest this potential. Here we describe a measurement and data analysis approach to obtain such quantitative spectroscopic LEEM datasets with high lateral resolution. The employed detector correction and adjustment techniques enable measurement of true reflectivity values over four orders of magnitudes of intensity. Moreover, we show a drift correction algorithm, tailored for LEEM datasets with inverting contrast, that yields sub-pixel accuracy without special computational demands. Finally, we apply dimension reduction techniques to summarize the key spectroscopic features of datasets with hundreds of images into two single images that can easily be presented and interpreted intuitively. We use cluster analysis to automatically identify different materials within the field of view and to calculate average spectra per material. We demonstrate these methods by analyzing bright-field and dark-field datasets of few-layer graphene grown on silicon carbide and provide a high-performance Python implementation

Virtual metrology for plasma etch processes.

Plasma processes can present dicult control challenges due to time-varying dynamics and a lack of relevant and/or regular measurements. Virtual metrology (VM) is the use of mathematical models with accessible measurements from an operating process to estimate variables of interest. This thesis addresses the challenge of virtual metrology for plasma processes, with a particular focus on semiconductor plasma etch. Introductory material covering the essentials of plasma physics, plasma etching, plasma measurement techniques, and black-box modelling techniques is rst presented for readers not familiar with these subjects. A comprehensive literature review is then completed to detail the state of the art in modelling and VM research for plasma etch processes. To demonstrate the versatility of VM, a temperature monitoring system utilising a state-space model and Luenberger observer is designed for the variable specic impulse magnetoplasma rocket (VASIMR) engine, a plasma-based space propulsion system. The temperature monitoring system uses optical emission spectroscopy (OES) measurements from the VASIMR engine plasma to correct temperature estimates in the presence of modelling error and inaccurate initial conditions. Temperature estimates within 2% of the real values are achieved using this scheme. An extensive examination of the implementation of a wafer-to-wafer VM scheme to estimate plasma etch rate for an industrial plasma etch process is presented. The VM models estimate etch rate using measurements from the processing tool and a plasma impedance monitor (PIM). A selection of modelling techniques are considered for VM modelling, and Gaussian process regression (GPR) is applied for the rst time for VM of plasma etch rate. Models with global and local scope are compared, and modelling schemes that attempt to cater for the etch process dynamics are proposed. GPR-based windowed models produce the most accurate estimates, achieving mean absolute percentage errors (MAPEs) of approximately 1:15%. The consistency of the results presented suggests that this level of accuracy represents the best accuracy achievable for the plasma etch system at the current frequency of metrology. Finally, a real-time VM and model predictive control (MPC) scheme for control of plasma electron density in an industrial etch chamber is designed and tested. The VM scheme uses PIM measurements to estimate electron density in real time. A predictive functional control (PFC) scheme is implemented to cater for a time delay in the VM system. The controller achieves time constants of less than one second, no overshoot, and excellent disturbance rejection properties. The PFC scheme is further expanded by adapting the internal model in the controller in real time in response to changes in the process operating point

Computational methods for the analysis of mass spectrometry imaging data

A powerful enhancement to MS-based detection is the addition of spatial information to the chemical data; an approach called mass spectrometry imaging (MSI). MSI enables two- and three-dimensional overviews of hundreds of molecular species over a wide mass range in complex biological samples. In this work, we present two computational methods and a workflow that address three different aspects of MSI data analysis: correction of mass shifts, unsupervised exploration of the data and importance of preprocessing and chemometrics to extract meaningful information from the data. We introduce a new lock mass-free recalibration procedure that enables to significantly reduce these mass shift effects in MSI data. Our method exploits similarities amongst peaklist pairs and takes advantage of the spatial context in three different ways, to perform mass correction in an iterative manner. As an extension of this work, we also present a Java-based tool, MSICorrect, that implements our recalibration approach and also allows data visualization. In the next part, an unsupervised approach to rank ion intensity maps based on the abundance of their spatial pattern is presented. Our method provides a score to every ion intensity map based on the abundance of spatial pattern present in it and then ranks all the maps using it. To know which masses exhibit similar spatial distribution, our method uses spatial-similarity based grouping to provide lists of masses that exhibit similar distribution patterns. In the last part, we demonstrate the application of a data preprocessing and multivariate analysis pipeline to a real-world biological dataset. We demonstrate this by applying the full pipeline to a high-resolution MSI dataset acquired from the leaf surface of Black cottonwood (Populus trichocarpa). Application of the pipeline helped in highlighting and visualizing the chemical specificity on the leaf surface

Frontiers of Cancer Diagnostics: From Photoacoustic Chemical Imaging to Cellular Morphodynamics

While terrific progress has been made over the last century, cancer continues to be a prevalent, lethal disease and is responsible for millions of deaths each year. The advent of personalized medicine has brought great strides in the treatment of cancer, as clinicians are able to select therapeutic courses that have been tailored to patients specific set of biomarkers. This selection, in principle, maximizes the chances of cancer remission while minimizing overall patient harm. In this spirit, we have focused on developing diagnostic techniques for two separate cancer biomarkers: tumor potassium concentration, and cell morphology. We first developed an ionophore-based potassium sensing nanoparticle. The sensor works on the principle of Donnan exclusion in which the overall charge of the carrier remains constant. The hydrophobic interior of the nanoparticle holds a pH-sensitive dye and a potassium ionophore. As the potassium concentrations rise, the ionophore chelates potassium from the solution which results in a proton being removed from the pH dye to maintain charge neutrality. The deprotonation event can be calibrated for quantitative measurement and this sensor was developed for use in diverse imaging modes, which include UV-VIS absorption, fluorescence, and photoacoustics. At physiological pH and in the presence of interfering ions, we were able to quantitatively measure potassium concentrations using each of these readouts. We modified the potassium sensor to enable in vivo measurements of potassium. This formulation makes use of a solvatochromic dye that transitions from the particle's interior to its surface as potassium is chelated, and thus avoids inherent pH-cross sensitivity. Using photoacoustic chemical imaging, we are able to quantitatively measure the potassium concentration in the tumor microenvironment. As predicted, it was shown that the TME is hyperkalemic, having a potassium concentration of 29mM. The results of the in vivo photoacoustic analysis were verified with ICP-MS measurements of TME potassium. Finally, we combined cell magneto-rotation and machine learning to develop a technique to measure the metastatic potential of a cancer cell population. This technique aims at avoiding the use of expensive and difficult to produce biological labels. By magnetically activating cells, we are able to suspend them in an oscillating magnetic field where they are free to explore their morphological shape space. By collecting fluorescence images of these cells, we are able to train a classifier to recognize cells of a given type. A proof of concept for the technique is provided here, where MCF-7 and MDA-MB-231 cells, both breast cancer but of different metastatic potential, were classified. A random forest classifier trained on cell images was able to correctly identify the cell type with 86.9% accuracy.PHDBiophysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163147/1/folzja_1.pd