Application of Stereo Imaging to Atomic Force Microscopy

Metrological data from sample surfaces can be obtained by using a variety of profilometry methods. Atomic Force Microscopy (AFM), which relies on contact inter-atomic forces to extract topographical images of a sample, is one such method that can be used on a wide range of surface types, with possible nanometer range resolution. However, AFM images are commonly distorted by convolution, which reduces metrological accuracy. This type of distortion is more significant when the sample surface contains high aspect ratio features such as lines, steps or sharp edges - structures commonly found in semiconductor devices and applications. Aiming at mitigating these distortions and recovering metrology soundness, we introduce a novel image deconvolution scheme based on the principle of stereo imaging. Multiple images of a sample, taken at different angles, allow for separation of convolution artifacts from true topographic data. As a result, perfect sample reconstruction and probe shape estimation can be achieved in certain cases. Additionally, shadow zones, which are areas of the sample that cannot be probed by the AFM, are greatly reduced. Most importantly, this technique does not require a priori probe characterization. It also reduces the need for slender or sharper probes, which, on one hand, induce less convolution distortion but, on the other hand, are more prone to wear and damage, thus decreasing overall system reliability.Singapore-MIT Alliance (SMA

Robust Adaptive Control of Switched Systems

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Atomic Force Microscope: Modeling, Simulations, and Experiments

The quality of atomic force microscope (AFM) data strongly depends on scan and controller parameters. Data artifacts can result from poor dynamic response of the instrument. In order to achieve reliable data, dynamic interactions between AFM components need to be well understood and controlled. In this paper we present a summary of our work in this direction. It includes models for the probe-sample interaction, scanner lateral and longitudinal dynamics, scanner creep, and cantilever dynamics. The models were used to study the effect of scan parameters on the system dynamics. Simulation results for both frequency response and imaging were presented. Experimental results were given supporting the simulations and demonstrating the competence of the models. The results within will be used to develop algorithms that allow automated choice of key system parameters, guaranteeing reliable and artifact-free data for any given operating condition (sample, cantilever, environment). Consequently, expanding the AFM capabilities and permitting its use in a wider range of applications.Singapore-MIT Alliance (SMA

A Generalized Bayesian Approach to Model Calibration

In model development, model calibration and validation play complementary roles toward learning reliable models. In this article, we expand the Bayesian Validation Metric framework to a general calibration and validation framework by inverting the validation mathematics into a generalized Bayesian method for model calibration and regression. We perform Bayesian regression based on a user's definition of model-data agreement. This allows for model selection on any type of data distribution, unlike Bayesian and standard regression techniques, that "fail" in some cases. We show that our tool is capable of representing and combining least squares, likelihood-based, and Bayesian calibration techniques in a single framework while being able to generalize aspects of these methods. This tool also offers new insights into the interpretation of the predictive envelopes (also known as confidence bands) while giving the analyst more control over these envelopes. We demonstrate the validity of our method by providing three numerical examples to calibrate different models, including a model for energy dissipation in lap joints under impact loading. By calibrating models with respect to the validation metrics one desires a model to ultimately pass, reliability and safety metrics may be integrated into and automatically adopted by the model in the calibration phase

Impacts of Industrial Baseline Errors on Costs and Social Welfare in the Demand Side Management of Day-Ahead Wholesale Markets

Demand Side Management (DSM) has been recognized for its potential to counteract the intermittent nature of renewable energy, increase system efficiency, and reduce system costs. While the popular approach among academia adopts a social welfare maximization formulation, the industrial practice in the United States electricity market compensates customers according to their load reduction from a predefined electricity consumption baseline that would have occurred without DSM. This paper is an extension of a previous paper studying the differences between the industrial & academic approach to dispatching demands. In the previous paper, the comparison of the two models showed that while the social welfare model uses a stochastic net load composed of two terms, the industrial DSM model uses a stochastic net load composed of three terms including the additional baseline term. That work showed that the academic and industrial optimization method have the same dispatch result in the absence of baseline errors given the proper reconciliation of their respective cost functions. DSM participants, however, and very much unfortunately, are likely to manipulate the baseline in order to receive greater financial compensation. This paper now seeks to study the impacts of erroneous industrial baselines in a day-ahead wholesale market context. Using the same system configuration and mathematical formalism, the industrial model is compared to the social welfare model. The erroneous baseline is shown to result in a different and more importantly costlier dispatch. It is also likely to require more control activity in subsequent layers of enterprise control. Thus an erroneous baseline is likely to increase system costs and overestimate the potential for social welfare improvements

An Enterprise Control Assessment Method for Variable Energy Resource-Induced Power System Imbalances--Part II: Parametric Sensitivity Analysis

In recent years, renewable energy has developed to address energy security and climate change drivers. However, as energy resources, they possess a variable and uncertain nature that significantly complicates grid balancing operations. As a result, an extensive academic and industrial literature has developed to determine how much such variable energy resources (VERs) may be integrated and how to best mitigate their impacts. While certainly insightful with the context of their application, many integration studies have methodological limitations because they are case specific, address a single control function of the power grid balancing operations, and are often not validated by simulation. The prequel to this paper presented a holistic method for the assessment of power grid imbalances induced by VERs based upon the concept of enterprise control. This paper now systematically studies these power grid imbalances in terms of five independent variables: 1) day-ahead market time step; 2) real-time market time step; 3) VER normalized variability; 4) normalized day-ahead VER forecast error; and 5) normalized short-term VER forecast error. The systematic study elucidates the impacts of these variables and provides significant insights as to how planners should address these independent variables in the future

An Enterprise Control Assessment Method for Variable Energy Resource-Induced Power System Imbalances--Part I: Methodology

In recent years, an extensive academic and industrial literature has been developed to determine how much such variable energy resources (VERs) may be integrated and how to best mitigate their impacts. While certainly insightful within the context of their application, many integration studies have methodological limitations in that they are case specific, address a single control function of power grid balancing operations, and are often not validated by simulation. This paper presents a holistic method for the assessment of power grid imbalances induced by VERs based upon the concept of enterprise control. It consists within a single package a three-layer enterprise control simulator which includes most of the balancing operation functionality found in traditional power systems. The control layers include a resource scheduling layer composed of a security-constrained unit commitment, a balancing layer composed of a security-constrained economic dispatch, and a regulation layer. The proposed method is validated by a set of numerical simulations. The sequel to this paper submitted to the same issue provides a set of extensive results that demonstrate how power grid balancing operations systematically address VER integration

Design of a Novel In-Pipe Reliable Leak Detector

Leakage is the major factor for unaccounted losses in every pipe network around the world (oil, gas, or water). In most cases, the deleterious effects associated with the occurrence of leaks may present serious economical and health problems. Therefore, leaks must be quickly detected, located, and repaired. Unfortunately, most state-of-the-art leak detection systems have limited applicability, are neither reliable nor robust, while others depend on the user experience. In this paper, we present a new in-pipe leak detection system. It performs autonomous leak detection in pipes and, thus, eliminates the need for the user experience. This paper focuses on the detection module and its main characteristics. Detection in based on the presence of a pressure gradient in the neighborhood of the leak. Moreover, the proposed detector can sense leaks at any angle around the circumference of the pipe with only two sensors. We validate the concepts by building a prototype and evaluate the system’s performance under real conditions in an experimental laboratory setup.Center for Clean Water and Clean Energy at MIT and KFUPM (Project R7-DMN-08)Alexander S. Onassis Public Benefit Foundatio