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- Creators: Bakkaloglu, Bertan
- Creators: Liu, Huan
- Member of: ASU Electronic Theses and Dissertations
- Resource Type: Text
Description
ABSTRACT As the technology length shrinks down, achieving higher gain is becoming very difficult in deep sub-micron technologies. As the supply voltages drop, cascodes are very difficult to implement and cascade amplifiers are needed to achieve sufficient gain with required output swing. This sets the fundamental limit on the SNR and hence the maximum resolution that can be achieved by ADC. With the RSD algorithm and the range overlap, the sub ADC can tolerate large comparator offsets leaving the linearity and accuracy requirement for the DAC and residue gain stage. Typically, the multiplying DAC requires high gain wide bandwidth op-amp and the design of this high gain op-amp becomes challenging in the deep submicron technologies. This work presents `A 12 bit 25MSPS 1.2V pipelined ADC using split CLS technique' in IBM 130nm 8HP process using only CMOS devices for the application of Large Hadron Collider (LHC). CLS technique relaxes the gain requirement of op-amp and improves the signal-to-noise ratio without increase in power or input sampling capacitor with rail-to-rail swing. An op-amp sharing technique has been incorporated with split CLS technique which decreases the number of op-amps and hence the power further. Entire pipelined converter has been implemented as six 2.5 bit RSD stages and hence decreases the latency associated with the pipelined architecture - one of the main requirements for LHC along with the power requirement. Two different OTAs have been designed to use in the split-CLS technique. Bootstrap switches and pass gate switches are used in the circuit along with a low power dynamic kick-back compensated comparator.
ContributorsSwaminathan, Visu Vaithiyanathan (Author) / Barnaby, Hugh (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Christen, Jennifer Blain (Committee member) / Arizona State University (Publisher)
Created2012
Description
Visual Question Answering (VQA) is an increasingly important multi-modal task where models must answer textual questions based on visual image inputs. Numerous VQA datasets have been proposed to train and evaluate models. However, existing benchmarks exhibit a unilateral focus on textual distribution shifts rather than joint shifts across modalities. This is suboptimal for properly assessing model robustness and generalization. To address this gap, a novel multi-modal VQA benchmark dataset is introduced for the first time. This dataset combines both visual and textual distribution shifts across training and test sets. Using this challenging benchmark exposes vulnerabilities in existing models relying on spurious correlations and overfitting to dataset biases. The novel dataset advances the field by enabling more robust model training and rigorous evaluation of multi-modal distribution shift generalization. In addition, a new few-shot multi-modal prompt fusion model is proposed to better adapt models for downstream VQA tasks. The model incorporates a prompt encoder module and dual-path design to align and fuse image and text prompts. This represents a novel prompt learning approach tailored for multi-modal learning across vision and language. Together, the introduced benchmark dataset and prompt fusion model address key limitations around evaluating and improving VQA model robustness. The work expands the methodology for training models resilient to multi-modal distribution shifts.
ContributorsJyothi Unni, Suraj (Author) / Liu, Huan (Thesis advisor) / Davalcu, Hasan (Committee member) / Bryan, Chris (Committee member) / Arizona State University (Publisher)
Created2023
Description
Impedance is one of the fundamental properties of electrical components, materials, and waves. Therefore, impedance measurement and monitoring have a wide range of applications. The multi-port technique is a natural candidate for impedance measurement and monitoring due to its low overhead and ease of implementation for Built-in Self-Test (BIST) applications. The multi-port technique can measure complex reflection coefficients, thus impedance, by using scalar measurements provided by the power detectors. These power detectors are strategically placed on different points (ports) of a passive network to produce unique solution. Impedance measurement and monitoring is readily deployed on mobile phone radio-frequency (RF) front ends, and are combined with antenna tuners to boost the signal reception capabilities of phones. These sensors also can be used in self-healing circuits to improve their yield and performance under process, voltage, and temperature variations. Even though, this work is preliminary interested in low-overhead impedance measurement for RF circuit applications, the proposed methods can be used in a wide variety of metrology applications where impedance measurements are already used. Some examples of these applications include determining material properties, plasma generation, and moisture detection. Additionally, multi-port applications extend beyond the impedance measurement. There are applications where multi-ports are used as receivers for communication systems, RADARs, and remote sensing applications. The multi-port technique generally requires a careful design of the testing structure to produce a unique solution from power detector measurements. It also requires the use of nonlinear solvers during calibration, and depending on calibration procedure, measurement. The use of nonlinear solvers generates issues for convergence, computational complexity, and resources needed for carrying out calibrations and measurements in a timely manner. In this work, using periodic structures, a structure where a circuit block repeats itself, for multi-port measurements is proposed. The periodic structures introduce a new constraint that simplifies the multi-port theory and leads to an explicit calibration and measurement procedure. Unlike the existing calibration procedures which require at least five loads and various constraints on the load for explicit solution, the proposed method can use three loads for calibration. Multi-ports built with periodic structures will always produce a unique measurement result. This leads to increased bandwidth of operation and simplifies design procedure. The efficacy of the method demonstrated in two embodiments.
In the first embodiment, a multi-port is directly embedded into a matching network to measure impedance of the load.
In the second embodiment, periodic structures are used to compare two loads without requiring any calibration.
ContributorsAvci, Muslum Emir (Author) / Ozev, Sule (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Kitchen, Jennifer (Committee member) / Trichopoulos, Georgios (Committee member) / Arizona State University (Publisher)
Created2023
Description
This work presents two balanced power amplifier (PA) architectures, one at X-band and the other at K-band. The presented balanced PAs are designed for use in small satellite and cube satellite applications.The presented X-band PA employs wideband hybrid couplers to split input power to two commercial off-the-shelf (COTS) Gallium Nitride (GaN) monolithic microwave integrated circuit (MMIC) PAs and combine their output powers. The presented X-band balanced PA manufactured on a Rogers 4003C substrate yields increased small signal gain and saturated output power under continuous wave (CW) operation compared to the single MMIC PA used in the design under pulsed operation. The presented PA operates from 7.5 GHz to 11.5 GHz, has a maximum small signal gain of 36.3 dB, a maximum saturated power out of 40.0 dBm, and a maximum power added efficiency (PAE) of 38%.
Both a Wilkinson and a Gysel splitter and combiner are designed for use at K-band and their performance is compared. The presented K-band balanced PA uses Gysel power dividers and combiners with a GaN MMIC PA that is soon to be released in production.
ContributorsPearson, Katherine Elizabeth (Author) / Kitchen, Jennifer (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Ozev, Sule (Committee member) / Arizona State University (Publisher)
Created2023
Description
Social media platforms provide a rich environment for analyzing user behavior. Recently, deep learning-based methods have been a mainstream approach for social media analysis models involving complex patterns. However, these methods are susceptible to biases in the training data, such as participation inequality. Basically, a mere 1% of users generate the majority of the content on social networking sites, while the remaining users, though engaged to varying degrees, tend to be less active in content creation and largely silent. These silent users consume and listen to information that is propagated on the platform.However, their voice, attitude, and interests are not reflected in the online content, making the decision of the current methods predisposed towards the opinion of the active users. So models can mistake the loudest users for the majority. To make the silent majority heard is to reveal the true landscape of the platform. In this dissertation, to compensate for this bias in the data, which is related to user-level data scarcity, I introduce three pieces of research work. Two of these proposed solutions deal with the data on hand while the other tries to augment the current data. Specifically, the first proposed approach modifies the weight of users' activity/interaction in the input space, while the second approach involves re-weighting the loss based on the users' activity levels during the downstream task training. Lastly, the third approach uses large language models (LLMs) and learns the user's writing behavior to expand the current data. In other words, by utilizing LLMs as a sophisticated knowledge base, this method aims to augment the silent user's data.
ContributorsKarami, Mansooreh (Author) / Liu, Huan (Thesis advisor) / Sen, Arunabha (Committee member) / Davulcu, Hasan (Committee member) / Mancenido, Michelle V. (Committee member) / Arizona State University (Publisher)
Created2023