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

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

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

The k-means algorithm: A comprehensive survey and performance evaluation

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of research conducted on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms including their recent developments are discussed, where their effectiveness is investigated based on the experimental analysis of a variety of datasets. The detailed experimental analysis along with a thorough comparison among different k-means clustering algorithms differentiates our work compared to other existing survey papers. Furthermore, it outlines a clear and thorough understanding of the k-means algorithm along with its different research directions

ANN Modelling to Optimize Manufacturing Process

Neural network (NN) model is an efficient and accurate tool for simulating manufacturing processes. Various authors adopted artificial neural networks (ANNs) to optimize multiresponse parameters in manufacturing processes. In most cases the adoption of ANN allows to predict the mechanical proprieties of processed products on the basis of given technological parameters. Therefore the implementation of ANN is hugely beneficial in industrial applications in order to save cost and material resources. In this chapter, following an introduction on the application of the ANN to the manufacturing process, it will be described an important study that has been published on international journals and that has investigated the use of the ANNs for the monitoring, controlling and optimization of the process. Experimental observations were collected in order to train the network and establish numerical relationships between process-related factors and mechanical features of the welded joints. Finally, an evaluation of time-costs parameters of the process, using the control of the ANN model, is conducted in order to identify the costs and the benefits of the prediction model adopted

A Domain Adaptation approach for sequence modeling through Deep Learning in semiconductor manufacturing: adversarial training setup with Temporal Convolutional Network and Long-Short Term Memory models

Lo scopo di questo lavoro è sviluppare delle strategie di Deep Learning per estendere la ricerca precedente (gentner2021dbam,gentner2020enhancing) riguardante una mansione relativa all'ambito di Virtual Metrology in una produzione di semiconduttori. Questo vuole essere un confronto tra il modello sviluppato in precedenza e alcuni modelli più adatti ai dati di serie temporali in esame. La strategia utilizzata in questo problema si basa sul Domain Adaptation attraverso un'impostazione di Adversarial Training, un approccio recente che permette una miglior gestione in assenza di grandi quantità di dati e rende i modelli precedentemente addestrati riutilizzabili in scenari simili. Questa ricerca è incoraggiata per gli effetti che può portare in questo campo. Migliorare ulteriormente le prestazioni dei modelli utilizzati significa risparmiare sui costi di produzione, poiché permetterebbe di limitare ulteriormente test di conformità che portano al danneggiamento dei prodotti. Ciò comporta anche un possibile risparmio di materiali, aspetto importante dal punto di vista ecologico. Infine, è sempre più necessario considerare l'uso di modelli che utilizzano meno dati o sono più adatti a processi simili, come nel caso delle tecniche di Domain Adaptation. Questo è importante perché, come nell'applicazione considerata, i dati sono costosi da ottenere.The aim of this work is to develop Deep Learning strategies to extend previous research (gentner2021dbam,gentner2020enhancing) concerning a task related to Virtual Metrology field in a semiconductor manufacturing. This intends to compare the previously developed model and other models more suited to the time series data under examination. The strategy used in this problem is based on Domain Adaptation through an adversarial training setup; the approach is recent and promising, allowing for better management in the absence of large amounts of data and making previously trained models reusable in similar scenarios. This research is encouraged for its effects in this field; further improving the performance of the models used means savings on production costs, since it would limit conformity tests which lead to product damage. This also leads to a possible saving of materials, an important aspect from an ecological point of view. Finally, it is increasingly necessary to consider models that use less data or are more suitable for similar processes, as in the case of Domain Adaptation techniques. The latter aspect is relevant because the data is expensive to obtain in the considered application

Process diagnostics of industrial plasma systems

This thesis presents new techniques to investigate and understand the source of process variability in plasma etching. In a semiconductor factory thousands of wafers are processed every month in multiple chambers. Whi le great effort is made to create reproducible process conditions, common and special cause variation remain a big challenge for the semiconductor industry. Process conditions are never identical from wafer to wafer and chamber to chamber. When high-frequency RF power, employed to create a plasma, is coupled into a chamber, the electrical characteristics of each chamber assembly is different. This electrical difference is as a result of mechanical differences of chamber components and how they are assembled. RF losses of the current affect the power deposition in the plasma and affect the process outcome. As each chamber processes more and more wafers, by-products buildup on the chamber walls impacting the process repeatability and influencing the processing chemistry. The surface roughness of the electrode and other chamber materials impact the rate at which the by-products deposit, which may also affect the process repeatability both chemically and electrically. These sources of variation contribute to inconsistent processing conditions experienced by the wafers. The work in this thesis focuses on the measurement of this process variability using intrusive and nonintrusive sensors to measure the plasma parameters as accurately as possible. Statistical approaches are used to build correlations between etch rate variability and the sensor measurements. The main finding of the thesis concludes that the combination of appropriate process measurement with sensors and statistical algorithms provide a very powerful tool to a process engineer in diagnosing process variability

2022 Review of Data-Driven Plasma Science

Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required

Identification of chemical species using artificial intelligence to interpret optical emission spectra

The nonlinear modeling capabilities of artificial neural networks (ANN’s) are renowned in the field of artificial intelligence (Al) for capturing knowledge that can be very difficult to understand otherwise. Their ability to be trained on representative data within a particular problem domain and generalise over a set of data make them efficient predictive models. One problem domain that contains complex data that would benefit from the predictive capabilities of ANN’s is that of optical emission spectra (OES). OES is an important diagnostic for monitoring plasma species within plasma processing. Normally, OES spectral interpretation requires significant prior expertise from a spectroscopist. One way of alleviating this intensive demand in order to quickly interpret OES spectra is to interpret the data using an intelligent pattern recognition technique like ANN’s. This thesis investigates and presents MLP ANN models that can successfully classify chemical species within OES spectral patterns. The primary contribution of the thesis is the creation of deployable ANN species models that can predict OES spectral line sizes directly from six controllable input process parameters; and the implementation of a novel rule extraction procedure to relate the real multi-output values of the spectral line sizes to individual input process parameters. Not only are the trained species models excellent in their predictive capability, but they also provide the foundation for extracting comprehensible rules. A secondary contribution made by this thesis is to present an adapted fuzzy rule extraction system that attaches a quantitative measure of confidence to individual rules. The most significant contribution to the field of Al that is generated from the work presented in the thesis is the fact that the rule extraction procedure utilises predictive ANN species models that employ real continuously valued multi-output data. This is an improvement on rule extraction from trained networks that normally focus on discrete binary output