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Description
Flow measurement has always been one of the most critical processes in many industrial and clinical applications. The dynamic behavior of flow helps to define the state of a process. An industrial example would be that in an aircraft, where the rate of airflow passing the aircraft is used to determine the speed of the plane. A clinical example would be that the flow of a patient's breath which could help determine the state of the patient's lungs. This project is focused on the flow-meter that are used for airflow measurement in human lungs. In order to do these measurements, resistive-type flow-meters are commonly used in respiratory measurement systems. This method consists of passing the respiratory flow through a fluid resistive component, while measuring the resulting pressure drop, which is linearly related to volumetric flow rate. These types of flow-meters typically have a low frequency response but are adequate for most applications, including spirometry and respiration monitoring. In the case of lung parameter estimation methods, such as the Quick Obstruction Method, it becomes important to have a higher frequency response in the flow-meter so that the high frequency components in the flow are measurable. The following three types of flow-meters were: a. Capillary type b. Screen Pneumotach type c. Square Edge orifice type To measure the frequency response, a sinusoidal flow is generated with a small speaker and passed through the flow-meter that is connected to a large, rigid container. True flow is proportional to the derivative of the pressure inside the container. True flow is then compared with the measured flow, which is proportional to the pressure drop across the flow-meter. In order to do the characterization, two LabVIEW data acquisition programs have been developed, one for transducer calibration, and another one that records flow and pressure data for frequency response testing of the flow-meter. In addition, a model that explains the behavior exhibited by the flow-meter has been proposed and simulated. This model contains a fluid resistor and inductor in series. The final step in this project was to approximate the frequency response data to the developed model expressed as a transfer function.
ContributorsHu, Jianchen (Author) / Macia, Narciso (Thesis advisor) / Pollat, Scott (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2013
Description
Complex electronic systems include multiple power domains and drastically varying dynamic power consumption patterns, requiring the use of multiple power conversion and regulation units. High frequency switching converters have been gaining prominence in the DC-DC converter market due to smaller solution size (higher power density) and higher efficiency. As the filter components become smaller in value and size, they are unfortunately also subject to higher process variations and worse degradation profiles jeopardizing stable operation of the power supply. This dissertation presents techniques to track changes in the dynamic loop characteristics of the DC-DC converters without disturbing the normal mode of operation. A digital pseudo-noise (PN) based stimulus is used to excite the DC-DC system at various circuit nodes to calculate the corresponding closed-loop impulse response. The test signal energy is spread over a wide bandwidth and the signal analysis is achieved by correlating the PN input sequence with the disturbed output generated, thereby
accumulating the desired behavior over time. A mixed-signal cross-correlation circuit is used to derive on-chip impulse responses, with smaller memory and lower computational requirement in comparison to a digital correlator approach. Model reference based parametric and non-parametric techniques are discussed to analyze the impulse response results in both time and frequency domain. The proposed techniques can extract open-loop phase margin and closed-loop unity-gain frequency within 5.2% and 4.1% error, respectively, for the load current range of 30-200mA. Converter parameters such as natural frequency (ω_n ), quality factor (Q), and center frequency (ω_c ) can be estimated within 3.6%, 4.7%, and 3.8% error respectively, over load inductance of 4.7-10.3µH, and filter capacitance of 200-400nF. A 5-MHz switching frequency, 5-8.125V input voltage range, voltage-mode controlled DC-DC buck converter is designed for the proposed built-in self-test (BIST) analysis. The converter output voltage range is 3.3-5V and the supported maximum
load current is 450mA. The peak efficiency of the converter is 87.93%. The proposed converter is fabricated on a 0.6µm 6-layer-metal Silicon-On-Insulator (SOI) technology with a die area of 9mm^2 . The area impact due to the system identification blocks including related I/O structures is 3.8% and they consume 530µA quiescent current during operation.
accumulating the desired behavior over time. A mixed-signal cross-correlation circuit is used to derive on-chip impulse responses, with smaller memory and lower computational requirement in comparison to a digital correlator approach. Model reference based parametric and non-parametric techniques are discussed to analyze the impulse response results in both time and frequency domain. The proposed techniques can extract open-loop phase margin and closed-loop unity-gain frequency within 5.2% and 4.1% error, respectively, for the load current range of 30-200mA. Converter parameters such as natural frequency (ω_n ), quality factor (Q), and center frequency (ω_c ) can be estimated within 3.6%, 4.7%, and 3.8% error respectively, over load inductance of 4.7-10.3µH, and filter capacitance of 200-400nF. A 5-MHz switching frequency, 5-8.125V input voltage range, voltage-mode controlled DC-DC buck converter is designed for the proposed built-in self-test (BIST) analysis. The converter output voltage range is 3.3-5V and the supported maximum
load current is 450mA. The peak efficiency of the converter is 87.93%. The proposed converter is fabricated on a 0.6µm 6-layer-metal Silicon-On-Insulator (SOI) technology with a die area of 9mm^2 . The area impact due to the system identification blocks including related I/O structures is 3.8% and they consume 530µA quiescent current during operation.
ContributorsBeohar, Navankur (Author) / Bakkaloglu, Bertan (Thesis advisor) / Ozev, Sule (Committee member) / Ayyanar, Raja (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2017
Description
Hardware implementation of deep neural networks is earning significant importance nowadays. Deep neural networks are mathematical models that use learning algorithms inspired by the brain. Numerous deep learning algorithms such as multi-layer perceptrons (MLP) have demonstrated human-level recognition accuracy in image and speech classification tasks. Multiple layers of processing elements called neurons with several connections between them called synapses are used to build these networks. Hence, it involves operations that exhibit a high level of parallelism making it computationally and memory intensive. Constrained by computing resources and memory, most of the applications require a neural network which utilizes less energy. Energy efficient implementation of these computationally intense algorithms on neuromorphic hardware demands a lot of architectural optimizations. One of these optimizations would be the reduction in the network size using compression and several studies investigated compression by introducing element-wise or row-/column-/block-wise sparsity via pruning and regularization. Additionally, numerous recent works have concentrated on reducing the precision of activations and weights with some reducing to a single bit. However, combining various sparsity structures with binarized or very-low-precision (2-3 bit) neural networks have not been comprehensively explored. Output activations in these deep neural network algorithms are habitually non-binary making it difficult to exploit sparsity. On the other hand, biologically realistic models like spiking neural networks (SNN) closely mimic the operations in biological nervous systems and explore new avenues for brain-like cognitive computing. These networks deal with binary spikes, and they can exploit the input-dependent sparsity or redundancy to dynamically scale the amount of computation in turn leading to energy-efficient hardware implementation. This work discusses configurable spiking neuromorphic architecture that supports multiple hidden layers exploiting hardware reuse. It also presents design techniques for minimum-area/-energy DNN hardware with minimal degradation in accuracy. Area, performance and energy results of these DNN and SNN hardware is reported for the MNIST dataset. The Neuromorphic hardware designed for SNN algorithm in 28nm CMOS demonstrates high classification accuracy (>98% on MNIST) and low energy (51.4 - 773 (nJ) per classification). The optimized DNN hardware designed in 40nm CMOS that combines 8X structured compression and 3-bit weight precision showed 98.4% accuracy at 33 (nJ) per classification.
ContributorsKolala Venkataramanaiah, Shreyas (Author) / Seo, Jae-Sun (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Cao, Yu (Committee member) / Arizona State University (Publisher)
Created2018
Description
The modern era of consumer electronics is dominated by compact, portable, affordable smartphones and wearable computing devices. Power management integrated circuits (PMICs) play a crucial role in on-chip power management, extending battery life and efficiency of integrated analog, radio-frequency (RF), and mixed-signal cores. Low-dropout (LDO) regulators are commonly used to provide clean supply for low voltage integrated circuits, where point-of-load regulation is important. In System-On-Chip (SoC) applications, digital circuits can change their mode of operation regularly at a very high speed, imposing various load transient conditions for the regulator. These quick changes of load create a glitch in LDO output voltage, which hamper performance of the digital circuits unfavorably. For an LDO designer, minimizing output voltage variation and speeding up voltage glitch settling is an important task.
The presented research introduces two fully integrated LDO voltage regulators for SoC applications. N-type Metal-Oxide-Semiconductor (NMOS) power transistor based operation achieves high bandwidth owing to the source follower configuration of the regulation loop. A low input impedance and high output impedance error amplifier ensures wide regulation loop bandwidth and high gain. Current-reused dynamic biasing technique has been employed to increase slew-rate at the gate of power transistor during full-load variations, by a factor of two. Three design variations for a 1-1.8 V, 50 mA NMOS LDO voltage regulator have been implemented in a 180 nm Mixed-mode/RF process. The whole LDO core consumes 0.130 mA of nominal quiescent ground current at 50 mA load and occupies 0.21 mm x mm. LDO has a dropout voltage of 200 mV and is able to recover in 30 ns from a 65 mV of undershoot for 0-50 pF of on-chip load capacitance.
The presented research introduces two fully integrated LDO voltage regulators for SoC applications. N-type Metal-Oxide-Semiconductor (NMOS) power transistor based operation achieves high bandwidth owing to the source follower configuration of the regulation loop. A low input impedance and high output impedance error amplifier ensures wide regulation loop bandwidth and high gain. Current-reused dynamic biasing technique has been employed to increase slew-rate at the gate of power transistor during full-load variations, by a factor of two. Three design variations for a 1-1.8 V, 50 mA NMOS LDO voltage regulator have been implemented in a 180 nm Mixed-mode/RF process. The whole LDO core consumes 0.130 mA of nominal quiescent ground current at 50 mA load and occupies 0.21 mm x mm. LDO has a dropout voltage of 200 mV and is able to recover in 30 ns from a 65 mV of undershoot for 0-50 pF of on-chip load capacitance.
ContributorsDesai, Chirag (Author) / Kiaei, Sayfe (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2016
Description
Static random-access memories (SRAM) are integral part of design systems as caches and data memories that and occupy one-third of design space. The work presents an embedded low power SRAM on a triple well process that allows body-biasing control. In addition to the normal mode operation, the design is embedded with Physical Unclonable Function (PUF) [Suh07] and Sense Amplifier Test (SA Test) mode. With PUF mode structures, the fabrication and environmental mismatches in bit cells are used to generate unique identification bits. These bits are fixed and known as preferred state of an SRAM bit cell. The direct access test structure is a measurement unit for offset voltage analysis of sense amplifiers. These designs are manufactured using a foundry bulk CMOS 55 nm low-power (LP) process. The details about SRAM bit-cell and peripheral circuit design is discussed in detail, for certain cases the circuit simulation analysis is performed with random variations embedded in SPICE models. Further, post-silicon testing results are discussed for normal operation of SRAMs and the special test modes. The silicon and circuit simulation results for various tests are presented.
ContributorsDosi, Ankita (Author) / Clark, Lawrence (Thesis advisor) / Seo, Jae-Sun (Committee member) / Brunhaver, John (Committee member) / Arizona State University (Publisher)
Created2017