Device and Circuit Level EMI Induced Vulnerability: Modeling and Experiments

Electro-magnetic interference (EMI) commonly exists in electronic equipment containing semiconductor-based integrated circuits (ICs). Metal-oxide-semiconductor field-effect-transistors (MOSFETs) in the ICs may be disrupted under EMI conditions due to transient voltage-current surges, and their internal states may change undesirably. In this work, the vulnerabilities of silicon MOSFETs under EMI are studied at the device and the circuit levels, categorized as non-permanent upsets (``Soft Errors'') and permanent damages (``Hard Failures''). The Soft Errors, such as temporary bit errors and waveform distortions, may happen or be intensified under EMI, as the transient disruptions activate unwanted and highly non-linear changes inside MOSFETs, such as Impact Ionization and Snapback. The system may be corrected from the erroneous state when the EMI condition is removed. We simulate planar silicon n-type MOSFETs at the device level to study the physical mechanisms leading to or complicate the short-term, signal-level Soft Errors. We experimentally tested commercially available MOSFET devices. Not included in regular MOSFET models, exponential-like current increases as the terminal voltage increases are observed and explained using the device-level knowledge. We develop a compact Soft Error model, compatible with circuit simulators using lumped (or compact-model) components and closed-form expressions, such as SPICE, and calibrate it with our in-house experimental data using an in-house extraction technique based on the Genetic Algorithm. Example circuits are simulated using the extracted device model and under EMI-induced transient disruptions. The EMI voltage-current disruptions may also lead to permanent Hard Failures that cannot be repaired without replacement. One type of Hard Failures, the MOSFET gate dielectric (or ``oxide'') breakdown, can result in input-output relation changes and additional thermal runaway. We have fabricated individual MOSFET devices at the FabLab at the University of Maryland NanoCenter. We experimentally stress-test the fabricated devices and observe the rapid, permanent oxide breakdown. Then, we simulate a nano-scale FinFET device with ultra-thin gate oxide at the device level. Then, we apply the knowledge from our experiments to the simulated FinFET, producing a gate oxide breakdown Hard Failure circuit model. The proposed workflow enables the evaluation of EMI-induced vulnerabilities in circuit simulations before actual fabrication and experiments, which can help the early-stage prototyping process and reduce the development time

Chemical and Optical Properties of Secondary Aerosols

Aerosol particles in the atmosphere negatively impact air quality and have a strong influence on the Earth’s climate due to their direct and indirect radiative effects. Characterization of the chemical and optical properties of these aerosol particles are critical to mitigate global climate change and improve air quality. My dissertation focuses on exploring the chemical and optical properties of laboratory generated secondary aerosols, which constitute a large fraction of aerosols in the atmosphere.In the first study, secondary organic aerosols (SOA) were produced through photooxidation of volatile organic compounds (VOCs, 1-methylnaphthalene or longifolene) under variable ammonia (NH3), nitrogen oxides (NOx), and relative humidity (RH) conditions. I studied SOA chemical and optical properties of the resulting SOA. This study shows that the formation of chromophores in SOA is strongly affected by different environmental conditions. NOx, NH3 and elevated RH enhance the absorption of aromatic SOA. In the second study, I investigated the SOA formed from pure and mixtures of anthropogenic (phenol and 1-methylnaphthalene) and/or biogenic (longifolene) VOCs using continuous-flow, high-NOx photooxidation chamber experiments. SOA optical properties and chemical composition were explored. Additionally, the absorption of multi-VOC SOA was predicted based on the linear-combination assumption and compared with the measured absorption. This study highlights the presence of multiple VOCs could have non-linear effects on the chemical and optical properties of the mixture SOA. In the third study, I used a machine learning model to study the SOA mass spectra data to predict its mass absorption coefficient (MAC). The prediction results indicate the strong correlation between SOA mass spectra and absorption, and machine learning model could be us absorption potential, in the form of MAC, despite lack of molecular level information on chromophores. In the fourth study, secondary aerosols formed through oxidation of dimethyl sulfide (DMS), a dominant natural volatile organic compound released from the ocean, were explored. I studied the DMS aerosol chemical composition with a mass spectrometer. This study reveals the formation of important sulfur products from DMS under different atmospheric aging time and the presence of highly-oxygenated long-lived organic products from DMS oxidation, which has been less studied before

Development of an AI-powered iOS application for image recognition and poem generation

Human beings are sophisticated creatures with an innate need to express their emotions. Poetry, as an emotional medium, is an excellent way for people to express themselves. Given the recent spike in interest in Artificial Intelligence (AI) and the increasing smartphone ownership evidenced by statistics, this project set out to build an iOS application that can generate poetry from images and has the capability of saving and sharing the composed poem. Besides the poem generation iOS application, the author has also conducted extensive research into the topic of AI poetry generation and eventually trained a variety of AI models that can generate various types of poetry.Bachelor of Engineering (Electrical and Electronic Engineering

bus complex network

This is bus data for research.</p

Identification and location method of strip ingot for autonomous robot system using kmeans clustering and color segmentation

Abstract In order to improve the efficiency of autonomous robot sorting steel ingots, this paper proposes a two‐dimensional weighted equivalent clustering‐based progressive probabilistic hough transform (2D‐WEC‐PPHT) algorithm for the problem of identifying and locating strip ingots in automatic picking strip ingot palletizing. First, the steel ingot image is preprocessed and the PPHT linear detection method is used to extract the edge linear information of the steel ingot. Second, the angle and position information are normalized and weighted, and a two‐dimensional clustering distance calculation method is proposed for two‐dimensional clustering of the extracted line information. Besides, the clustering line clusters with close angles were processed into equivalent lines by mean fitting. Then, the average threshold method was used to segment the ingot between each two fitting lines, and the minimum rectangle was used to box the segmentation part. The centroid of the box‐selected rectangle is the positioning center of the ingot, and the long side angle of the box‐selected rectangle is the deviation angle information of the ingot. Finally, the experimental results show that the number of redundant lines detected by the 2D‐WEC‐PPHT is significantly less than that of traditional methods such as HT and PPHT. The positioning speed of ingot is faster than that of HT and PPHT, and the processing time is reduced from 20 s to 10 s. In the case of large proportion of old ingots with weak reflection, the recognition accuracy reached 93.5%, and the angular and position positioning accuracy were 2.270° and 11.675 mm, respectively. The recognition accuracy of new ingots with strong reflection reached 99.574%, which met the requirements of picking positioning accuracy