Statistical Methods for Semiconductor Manufacturing

In this thesis techniques for non-parametric modeling, machine learning, filtering and prediction and run-to-run control for semiconductor manufacturing are described. In particular, algorithms have been developed for two major applications area: - Virtual Metrology (VM) systems; - Predictive Maintenance (PdM) systems. Both technologies have proliferated in the past recent years in the semiconductor industries, called fabs, in order to increment productivity and decrease costs. VM systems aim of predicting quantities on the wafer, the main and basic product of the semiconductor industry, that may be physically measurable or not. These quantities are usually ’costly’ to be measured in economic or temporal terms: the prediction is based on process variables and/or logistic information on the production that, instead, are always available and that can be used for modeling without further costs. PdM systems, on the other hand, aim at predicting when a maintenance action has to be performed. This approach to maintenance management, based like VM on statistical methods and on the availability of process/logistic data, is in contrast with other classical approaches: - Run-to-Failure (R2F), where there are no interventions performed on the machine/process until a new breaking or specification violation happens in the production; - Preventive Maintenance (PvM), where the maintenances are scheduled in advance based on temporal intervals or on production iterations. Both aforementioned approaches are not optimal, because they do not assure that breakings and wasting of wafers will not happen and, in the case of PvM, they may lead to unnecessary maintenances without completely exploiting the lifetime of the machine or of the process. The main goal of this thesis is to prove through several applications and feasibility studies that the use of statistical modeling algorithms and control systems can improve the efficiency, yield and profits of a manufacturing environment like the semiconductor one, where lots of data are recorded and can be employed to build mathematical models. We present several original contributions, both in the form of applications and methods. The introduction of this thesis will be an overview on the semiconductor fabrication process: the most common practices on Advanced Process Control (APC) systems and the major issues for engineers and statisticians working in this area will be presented. Furthermore we will illustrate the methods and mathematical models used in the applications. We will then discuss in details the following applications: - A VM system for the estimation of the thickness deposited on the wafer by the Chemical Vapor Deposition (CVD) process, that exploits Fault Detection and Classification (FDC) data is presented. In this tool a new clustering algorithm based on Information Theory (IT) elements have been proposed. In addition, the Least Angle Regression (LARS) algorithm has been applied for the first time to VM problems. - A new VM module for multi-step (CVD, Etching and Litography) line is proposed, where Multi-Task Learning techniques have been employed. - A new Machine Learning algorithm based on Kernel Methods for the estimation of scalar outputs from time series inputs is illustrated. - Run-to-Run control algorithms that employ both the presence of physical measures and statistical ones (coming from a VM system) is shown; this tool is based on IT elements. - A PdM module based on filtering and prediction techniques (Kalman Filter, Monte Carlo methods) is developed for the prediction of maintenance interventions in the Epitaxy process. - A PdM system based on Elastic Nets for the maintenance predictions in Ion Implantation tool is described. Several of the aforementioned works have been developed in collaborations with major European semiconductor companies in the framework of the European project UE FP7 IMPROVE (Implementing Manufacturing science solutions to increase equiPment pROductiVity and fab pErformance); such collaborations will be specified during the thesis, underlying the practical aspects of the implementation of the proposed technologies in a real industrial environment

Automated and intelligent hacking detection system

Dissertação de mestrado integrado em Informatics EngineeringThe Controller Area Network (CAN) is the backbone of automotive networking, connecting many Electronic ControlUnits (ECUs) that control virtually every vehicle function from fuel injection to parking sensors. It possesses,however, no security functionality such as message encryption or authentication by default. Attackers can easily inject or modify packets in the network, causing vehicle malfunction and endangering the driver and passengers. There is an increasing number of ECUs in modern vehicles, primarily driven by the consumer’s expectation of more features and comfort in their vehicles as well as ever-stricter government regulations on efficiency and emissions. Combined with vehicle connectivity to the exterior via Bluetooth, Wi-Fi, or cellular, this raises the risk of attacks. Traditional networks, such as Internet Protocol (IP), typically have an Intrusion Detection System (IDS) analysing traffic and signalling when an attack occurs. The system here proposed is an adaptation of the traditional IDS into the CAN bus using a One Class Support Vector Machine (OCSVM) trained with live, attack-free traffic. The system is capable of reliably detecting a variety of attacks, both known and unknown, without needing to understand payload syntax, which is largely proprietary and vehicle/model dependent. This allows it to be installed in any vehicle in a plug-and-play fashion while maintaining a large degree of accuracy with very few false positives.A Controller Area Network (CAN) é a principal tecnologia de comunicação interna automóvel, ligando muitas Electronic Control Units (ECUs) que controlam virtualmente todas as funções do veículo desde injeção de combustível até aos sensores de estacionamento. No entanto, não possui por defeito funcionalidades de segurança como cifragem ou autenticação. É possível aos atacantes facilmente injetarem ou modificarem pacotes na rede causando estragos e colocando em perigo tanto o condutor como os passageiros. Existe um número cada vez maior de ECUs nos veículos modernos, impulsionado principalmente pelas expectativas do consumidores quanto ao aumento do conforto nos seus veículos, e pelos cada vez mais exigentes regulamentos de eficiência e emissões. Isto, associada à conexão ao exterior através de tecnologias como o Bluetooth, Wi-Fi, ou redes móveis, aumenta o risco de ataques. Redes tradicionais, como a rede Internet Protocol (IP), tipicamente possuem um Intrusion Detection Systems (IDSs) que analiza o tráfego e assinala a presença de um ataque. O sistema aqui proposto é uma adaptação do IDS tradicional à rede CAN utilizando uma One Class Support Vector Machine (OCSVM) treinada com tráfego real e livre de ataques. O sistema é capaz de detetar com fiabilidade uma variedade de ataques, tanto conhecidos como desconhecidos, sem a necessidade de entender a sintaxe do campo de dados das mensagens, que é maioritariamente proprietária. Isto permite ao sistema ser instalado em qualquer veículo num modo plug-and-play enquanto mantém um elevado nível de desempenho com muito poucos falsos positivos

Sensor-Based Monitoring and Inspection of Surface Morphology in Ultraprecision Manufacturing Processes

This research proposes approaches for monitoring and inspection of surface morphology with respect to two ultraprecision/nanomanufacturing processes, namely, ultraprecision machining (UPM) and chemical mechanical planarization (CMP). The methods illustrated in this dissertation are motivated from the compelling need for in situ process monitoring in nanomanufacturing and invoke concepts from diverse scientific backgrounds, such as artificial neural networks, Bayesian learning, and algebraic graph theory. From an engineering perspective, this work has the following contributions:1. A combined neural network and Bayesian learning approach for early detection of UPM process anomalies by integrating data from multiple heterogeneous in situ sensors (force, vibration, and acoustic emission) is developed. The approach captures process drifts in UPM of aluminum 6061 discs within 15 milliseconds of their inception and is therefore valuable for minimizing yield losses.2. CMP process dynamics are mathematically represented using a deterministic multi-scale hierarchical nonlinear differential equation model. This process-machine inter-action (PMI) model is evocative of the various physio-mechanical aspects in CMP and closely emulates experimentally acquired vibration signal patterns, including complex nonlinear dynamics manifest in the process. By combining the PMI model predictions with features gathered from wirelessly acquired CMP vibration signal patterns, CMP process anomalies, such as pad wear, and drifts in polishing were identified in their nascent stage with high fidelity (R2 ~ 75%).3. An algebraic graph theoretic approach for quantifying nano-surface morphology from optical micrograph images is developed. The approach enables a parsimonious representation of the topological relationships between heterogeneous nano-surface fea-tures, which are enshrined in graph theoretic entities, namely, the similarity, degree, and Laplacian matrices. Topological invariant measures (e.g., Fiedler number, Kirchoff index) extracted from these matrices are shown to be sensitive to evolving nano-surface morphology. For instance, we observed that prominent nanoscale morphological changes on CMP processed Cu wafers, although discernible visually, could not be tractably quantified using statistical metrology parameters, such as arithmetic average roughness (Sa), root mean square roughness (Sq), etc. In contrast, CMP induced nanoscale surface variations were captured on invoking graph theoretic topological invariants. Consequently, the graph theoretic approach can enable timely, non-contact, and in situ metrology of semiconductor wafers by obviating the need for reticent profile mapping techniques (e.g., AFM, SEM, etc.), and thereby prevent the propagation of yield losses over long production runs.Industrial Engineering & Managemen

Advanced Information Systems and Technologies

This book comprises the proceedings of the V International Scientific Conference "Advanced Information Systems and Technologies, AIST-2017". The proceeding papers cover issues related to system analysis and modeling, project management, information system engineering, intelligent data processing computer networking and telecomunications. They will be useful for students, graduate students, researchers who interested in computer science