Matching Items (84)
Filtering by
- All Subjects: Electrical Engineering
Description
This is a two-part thesis.Part-I:
This work investigated the long-term reliability of a statistically significant number of two different commercial module-level power electronics (MLPE) devices using two input power profiles at high temperatures to estimate their reliability and service life in field-use conditions. Microinverters underwent a period of 15,000 accelerated stress hours, whereas the power optimizers underwent a period of 6,400 accelerated stress hours. None of the MLPE devices failed during the accelerated test; however, the optimizers degraded by about 1% in output efficiency. Based on their accelerated stress temperatures, the estimated field equivalent service life approximated using the Arrhenius model ranges between 24-48 years for microinverters and 39-73 years for optimizers, with a reliability of 74% and a lower one-sided confidence level of 95%. Furthermore, using the Weibull distribution model, the reliability and service lifetimes of MLPE devices are statistically analyzed. MLPE lifetimes estimated using Weibull slope and shape parameters with a 95% lower one-sided confidence level indicate a similar, or possibly exceeding, the 25-year lifetime of the associated photovoltaic (PV) modules. Part–II:This study investigated the impact of the hotspot stress test on glass-backsheet and glass-glass modules. Before the hotspot testing, both modules were pre-stressed using 600 thermal cycles (TC600) to represent decades of field-exposed modules experiencing hotspot effects in field-use conditions. The glass-glass module reached a hotspot temperature of nearly 200°C, whereas the glass-backsheet module's maximum hotspot temperature was almost 150°C. After the hotspot experiment, electroluminescence imaging showed that most of the cells in the glass-glass module appeared to have experienced significant damage. In contrast, the stressed cells in the glass-backsheet module appeared to have experienced insignificant damage. After the sequential stress testing (hotspot testing after TC600), the glass-glass module degraded by nearly 8.3% in maximum power, whereas the glass-backsheet module experienced 1.3% degradation. This study also incorporated hotspot endurance in fresh (without being subjected to prior TC600) glass-glass and glass-backsheet modules. The test outcome demonstrated that both module types exhibited marginal maximum power loss.
ContributorsAfridi, Muhammad Zain Ul Abideen (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Kiaei, Sayfe (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Flicker, Jack (Committee member) / Arizona State University (Publisher)
Created2023
Description
A photovoltaic (PV) module is a series and parallel connection of multiple PV cells; defects in any cell can cause module power to drop. Similarly, a photovoltaic system is a series and parallel connection of multiple modules, and any low-performing module in the PV system can decrease the system output power. Defects in a solar cell include, but not limited to, the presence of cracks, potential induced degradation (PID), delamination, corrosion, and solder bond degradation. State-of-the-art characterization techniques to identify the defective cells in a module and defective module in a string are i) Current-voltage (IV) curve tracing, ii) Electroluminescence (EL) imaging, and iii) Infrared (IR) imaging. Shortcomings of these techniques include i) unsafe connection and disconnection need to be made with high voltage electrical cables, and ii) labor and time intensive disconnection of the photovoltaic strings from the system.This work presents a non-contact characterization technique to address the above two shortcomings. This technique uses a non-contact electrostatic voltmeter (ESV) along with a probe sensor to measure the surface potential of individual solar cells in a commercial module and the modules in a string in both off-grid and grid-connected systems. Unlike the EL approach, the ESV setup directly measures the surface potential by sensing the electric field lines that are present on the surface of the solar cell.
The off-grid testing of ESV on individual cells and multicells in crystalline silicon (c-Si) modules and on individual cells in cadmium telluride (CdTe) modules and individual modules in a CdTe string showed less than 2% difference in open circuit voltage compared to the voltmeter values. In addition, surface potential mapping of the defective cracked cells in a multicell module using ESV identified the dark, grey, and bright areas of EL images precisely at the exact locations shown by the EL characterization.
The on-grid testing of ESV measured the individual module voltages at maximum power point (Vmpp) and quantitatively identified the exact PID-affected module in the entire system. In addition, the poor-performing non-PID modules of a grid-connected PV system were also identified using the ESV technique.
ContributorsRaza, Hamza Ahmad (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Kiaei, Sayfe (Committee member) / Bakkaloglu, Bertan (Committee member) / Hacke, Peter (Committee member) / Arizona State University (Publisher)
Created2023
Description
The Deep Neural Network (DNN) is one type of a neuromorphic computing approach that has gained substantial interest today. To achieve continuous improvement in accuracy, the depth, and the size of the deep neural network needs to significantly increase. As the scale of the neural network increases, it poses a severe challenge to its hardware implementation with conventional Computer Processing Unit (CPU) and Graphic Processing Unit (GPU) from the perspective of power, computation, and memory. To address this challenge, domain specific specialized digital neural network accelerators based on Field Programmable Gate Array (FPGAs) and Application Specific Integrated Circuits (ASICs) have been developed. However, limitations still exist in terms of on-chip memory capacity, and off-chip memory access. As an alternative, Resistive Random Access Memories (RRAMs), have been proposed to store weights on chip with higher density and enabling fast analog computation with low power consumption. Conductive Bridge Random Access Memories (CBRAMs) is a subset of RRAMs, whose conductance states is defined by the existence and modulation of a conductive metal filament. Ag-Chalcogenide based Conductive Bridge RAM (CBRAM) devices have demonstrated multiple resistive states making them potential candidates for use as analog synapses in neuromorphic hardware. In this work the use of Ag-Ge30Se70 device as an analog synaptic device has been explored. Ag-Ge30Se70 CBRAM crossbar array was fabricated. The fabricated crossbar devices were subjected to different pulsing schemes and conductance linearity response was analyzed. An improved linear response of the devices from a non-linearity factor of 6.65 to 1 for potentiation and -2.25 to -0.95 for depression with non-identical pulse application is observed. The effect of improved linearity was quantified by simulating the devices in an artificial neural network. Simulations for area, latency, and power consumption of the CBRAM device in a neural accelerator was conducted. Further, the changes caused by Total Ionizing Dose (TID) in the conductance of the analog response of Ag-Ge30Se70 Conductive Bridge Random Access Memory (CBRAM)-based synapses are studied. The effect of irradiation was further analyzed by simulating the devices in an artificial neural network. Material characterization was performed to understand the change in conductance observed due to TID.
ContributorsApsangi, Priyanka (Author) / Barnaby, Hugh (Thesis advisor) / Kozicki, Michael (Committee member) / Sanchez Esqueda, Ivan (Committee member) / Marinella, Matthew (Committee member) / Arizona State University (Publisher)
Created2022
Description
Recently, the implementation of neuromorphic accelerator hardware has gradually changed from traditional Von Neumann architectures to non-Von Neumann architectures due to the “memory wall” and “power wall”. Near-memory computing (NMC) and In- memory computing (IMC) are two common types of non-Von Neumann approaches. NMC can help reduce data movements, yet it cannot fully address the challenge of improving computational efficiency as the neural network size grows. IMC has been proposed as a superior alternative. This architecture performs computation inside the memory array using stackable synaptic devices to improve the latency and the energy efficiency of neural network accelerators. Both volatile and non-volatile computational memory devices can achieve IMC. Fully complementary metal-oxide semiconductor (CMOS) in-memory computing cells can be realized by adding additional transistors in standard static random access memory (SRAM) bit-cell. The SRAM-based designs investigated in this dissertation perform bit-wise logical operation to obtain XNOR-and-accumulate computation (XAC) for deep neural networks (DNNs). Hybrid in-memory computing architectures combine CMOS with embedded non-volatile memory (eNVM). Resistive random access memory (RRAM) is one class of eNVM ideally suited for hybrid IMC. In a neural network, RRAM with programmable multi-level resistance/conductance states can naturally emulate weight transitions in the synaptic elements of neural networks. In this dissertation, the operation and effects of ionizing radiation effects on both fully CMOS and hybrid IMCs are investigated. The fully CMOS architectures preform SRAM-based XAC computations. The hybrid architectures use multi-state RRAM synapse with CMOS neurons to perform multiply-and-accumulate computation (MAC). In the SRAM XAC array, an 8×8 XNOR IMC array is modeled with flipped-well enhanced-gate super low threshold voltage (EGSLVT) metal-oxide semiconductor field-effect transistors (MOSFETs) from the GlobalFoundries 22nm fully depleted silicon on insulator (FDSOI) process. The impact of total ionizing dose (TID) on the XAC synaptic array is analyzed by using radiation-aware models to mimic TID-induced voltage shifts in MOSFETs. In multi- state RRAM MAC array, 4-state conductance has been programmed in hafnium-oxide (HfOx) RRAM 1-transistor-1-resistor (1T1R) array. The impact of total ionizing dose on the multi-state behavior of HfOx RRAM is evaluated by irradiating a 64kb 1T1R array with 90nm CMOS peripheral circuitry under Co-60 γ-ray irradiation.
ContributorsHan, Xu (Author) / Barnaby, Hugh (Thesis advisor) / Kozicki, Michael (Committee member) / Marinella, Matthew (Committee member) / Esqueda, Ivan (Committee member) / Arizona State University (Publisher)
Created2022
Description
Bipolar commercial-off-the-shelf (COTS) circuits are increasingly used in spacemissions due to the low cost per part. In space environments these devices are exposed to
ionizing radiation that degrades their performance. Testing to evaluate the performance of
these devices is a costly and lengthy process. As such methods that can help predict a
COTS part’s performance help alleviate these downsides. A modeling software for
predicting total ionizing dose (TID), enhanced low dose rate sensitivity (ELDRS), and
hydrogen gas on bipolar parts is introduced and expanded upon. The model is then
developed in several key ways that expand it’s features and usability in this field. A
physics based methodology of simulating interface traps (NIT) to expand the previously
experimental only database is detailed. This new methodology is also compared to
experimental data and used to establish a link between hydrogen concentration in the
oxide and packaged hydrogen gas. Links are established between Technology Computer
Aided Design (TCAD), circuit simulation, and experimental data. These links are then
used to establish a better foundation for the model. New methodologies are added to the
modeling software so that it is possible to simulate transient based characteristics like
slew rate.
ContributorsRoark, Samuel (Author) / Barnaby, Hugh (Thesis advisor) / Sanchez Esqueda, Ivan (Committee member) / Bakkaloglu, Bertan (Committee member) / Arizona State University (Publisher)
Created2022
Description
Proton beam therapy has been proven to be effective for cancer treatment. Protons allow for complete energy deposition to occur inside patients, rendering this a superior treatment compared to other types of radiotherapy based on photons or electrons. This same characteristic makes quality assurance critical driving the need for detectors capable of direct beam positioning and fluence measurement. This work showcases a flexible and scalable data acquisition system for a multi-channel and segmented readout parallel plate ionization chamber instrument for proton beam fluence and positioning detection. Utilizing readily available, modern, off-the-shelf hardware components, including an FPGA with an embedded CPU in the same package, a data acquisition system for the detector was designed. The undemanding detector signal bandwidth allows the absence of ASICs and their associated costs and lead times in the system. The data acquisition system is showcased experimentally for a 96-readout channel detector demonstrating sub millisecond beam characteristics and beam reconstruction. The system demonstrated scalability up to 1064-readout channels, the limiting factor being FPGA I/O availability as well as amplification and sampling power consumption.
ContributorsAcuna Briceno, Rafael Andres (Author) / Barnaby, Hugh (Thesis advisor) / Brunhaver, John (Committee member) / Blyth, David (Committee member) / Arizona State University (Publisher)
Created2021
Description
VCO as a ubiquitous circuit in many systems is highly demanding for the phase noises. Lowering the noise migrated from the power supply has been the trending topics for many years. Considering the Ring Oscillator(RO) based VCO is more sensitive to the supply noise, it is more significant to find out a useful technique to reduce the supply noise. Among the conventional supply noise reduction techniques such as filtering, channel length adjusting for the transistors, and the current noise mutual canceling, the new feature of the 28nm UTBB-FD-SOI process launched by the ST semiconductor offered a new method to reduce the noise, which is realized by allowing the circuit designer to dynamically control the threshold voltage. In this thesis, a new structure of the linear coarse-fine VCO with 1V supply voltage is designed for the ring typed VCO. The structure is also designed to be flexible to tune the frequency coverage by the fine and coarse tunable on-board resistors. The thesis has given the model of the phase noise reduction method. The model has also been proved to be meaningful with the newly designed VCO circuit. For instances, given 1μV/√Hz white noise coupled on the supply, the 3GHz VCO can have a more than 7dBc/Hz phase noise lowering at the 10MHz frequency offset.
ContributorsTang, Miao (Author) / Barnaby, Hugh (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Mikkola, Esko (Committee member) / Arizona State University (Publisher)
Created2018
Description
The rapid improvement in computation capability has made deep convolutional neural networks (CNNs) a great success in recent years on many computer vision tasks with significantly improved accuracy. During the inference phase, many applications demand low latency processing of one image with strict power consumption requirement, which reduces the efficiency of GPU and other general-purpose platform, bringing opportunities for specific acceleration hardware, e.g. FPGA, by customizing the digital circuit specific for the deep learning algorithm inference. However, deploying CNNs on portable and embedded systems is still challenging due to large data volume, intensive computation, varying algorithm structures, and frequent memory accesses. This dissertation proposes a complete design methodology and framework to accelerate the inference process of various CNN algorithms on FPGA hardware with high performance, efficiency and flexibility.
As convolution contributes most operations in CNNs, the convolution acceleration scheme significantly affects the efficiency and performance of a hardware CNN accelerator. Convolution involves multiply and accumulate (MAC) operations with four levels of loops. Without fully studying the convolution loop optimization before the hardware design phase, the resulting accelerator can hardly exploit the data reuse and manage data movement efficiently. This work overcomes these barriers by quantitatively analyzing and optimizing the design objectives (e.g. memory access) of the CNN accelerator based on multiple design variables. An efficient dataflow and hardware architecture of CNN acceleration are proposed to minimize the data communication while maximizing the resource utilization to achieve high performance.
Although great performance and efficiency can be achieved by customizing the FPGA hardware for each CNN model, significant efforts and expertise are required leading to long development time, which makes it difficult to catch up with the rapid development of CNN algorithms. In this work, we present an RTL-level CNN compiler that automatically generates customized FPGA hardware for the inference tasks of various CNNs, in order to enable high-level fast prototyping of CNNs from software to FPGA and still keep the benefits of low-level hardware optimization. First, a general-purpose library of RTL modules is developed to model different operations at each layer. The integration and dataflow of physical modules are predefined in the top-level system template and reconfigured during compilation for a given CNN algorithm. The runtime control of layer-by-layer sequential computation is managed by the proposed execution schedule so that even highly irregular and complex network topology, e.g. GoogLeNet and ResNet, can be compiled. The proposed methodology is demonstrated with various CNN algorithms, e.g. NiN, VGG, GoogLeNet and ResNet, on two different standalone FPGAs achieving state-of-the art performance.
Based on the optimized acceleration strategy, there are still a lot of design options, e.g. the degree and dimension of computation parallelism, the size of on-chip buffers, and the external memory bandwidth, which impact the utilization of computation resources and data communication efficiency, and finally affect the performance and energy consumption of the accelerator. The large design space of the accelerator makes it impractical to explore the optimal design choice during the real implementation phase. Therefore, a performance model is proposed in this work to quantitatively estimate the accelerator performance and resource utilization. By this means, the performance bottleneck and design bound can be identified and the optimal design option can be explored early in the design phase.
As convolution contributes most operations in CNNs, the convolution acceleration scheme significantly affects the efficiency and performance of a hardware CNN accelerator. Convolution involves multiply and accumulate (MAC) operations with four levels of loops. Without fully studying the convolution loop optimization before the hardware design phase, the resulting accelerator can hardly exploit the data reuse and manage data movement efficiently. This work overcomes these barriers by quantitatively analyzing and optimizing the design objectives (e.g. memory access) of the CNN accelerator based on multiple design variables. An efficient dataflow and hardware architecture of CNN acceleration are proposed to minimize the data communication while maximizing the resource utilization to achieve high performance.
Although great performance and efficiency can be achieved by customizing the FPGA hardware for each CNN model, significant efforts and expertise are required leading to long development time, which makes it difficult to catch up with the rapid development of CNN algorithms. In this work, we present an RTL-level CNN compiler that automatically generates customized FPGA hardware for the inference tasks of various CNNs, in order to enable high-level fast prototyping of CNNs from software to FPGA and still keep the benefits of low-level hardware optimization. First, a general-purpose library of RTL modules is developed to model different operations at each layer. The integration and dataflow of physical modules are predefined in the top-level system template and reconfigured during compilation for a given CNN algorithm. The runtime control of layer-by-layer sequential computation is managed by the proposed execution schedule so that even highly irregular and complex network topology, e.g. GoogLeNet and ResNet, can be compiled. The proposed methodology is demonstrated with various CNN algorithms, e.g. NiN, VGG, GoogLeNet and ResNet, on two different standalone FPGAs achieving state-of-the art performance.
Based on the optimized acceleration strategy, there are still a lot of design options, e.g. the degree and dimension of computation parallelism, the size of on-chip buffers, and the external memory bandwidth, which impact the utilization of computation resources and data communication efficiency, and finally affect the performance and energy consumption of the accelerator. The large design space of the accelerator makes it impractical to explore the optimal design choice during the real implementation phase. Therefore, a performance model is proposed in this work to quantitatively estimate the accelerator performance and resource utilization. By this means, the performance bottleneck and design bound can be identified and the optimal design option can be explored early in the design phase.
ContributorsMa, Yufei (Author) / Vrudhula, Sarma (Thesis advisor) / Seo, Jae-Sun (Thesis advisor) / Cao, Yu (Committee member) / Barnaby, Hugh (Committee member) / Arizona State University (Publisher)
Created2018
Description
The continuing advancement of modulation standards with newer generations of cellular technology, promises ever increasing data rate and bandwidth efficiency. However, these modulation schemes present high peak to average power ratio (PAPR) even after applying crest factor reduction. Being the most power-hungry component in the radio frequency (RF) transmitter, power amplifiers (PA) for infrastructure applications, need to operate efficiently at the presence of these high PAPR signals while maintaining reasonable linearity performance which could be improved by moderate digital pre-distortion (DPD) techniques. This strict requirement of operating efficiently at average power level while being capable of delivering the peak power, made the load modulated PAs such as Doherty PA, Outphasing PA, various Envelope Tracking PAs, Polar transmitters and most recently the load modulated balanced PA, the prime candidates for such application. However, due to its simpler architecture and ability to deliver RF power efficiently with good linearity performance has made Doherty PA (DPA) the most popular solution and has been deployed almost exclusively for wireless infrastructure application all over the world.
Although DPAs has been very successful at amplifying the high PAPR signals, most recent advancements in cellular technology has opted for higher PAPR based signals at wider bandwidth. This lead to increased research and development work to innovate advanced Doherty architectures which are more efficient at back-off (BO) power levels compared to traditional DPAs. In this dissertation, three such advanced Doherty architectures and/or techniques are proposed to achieve high efficiency at further BO power level compared to traditional architecture using symmetrical devices for carrier and peaking PAs. Gallium Nitride (GaN) based high-electron-mobility (HEMT) technology has been used to design and fabricate the DPAs to validate the proposed advanced techniques for higher efficiency with good linearity performance at BO power levels.
Although DPAs has been very successful at amplifying the high PAPR signals, most recent advancements in cellular technology has opted for higher PAPR based signals at wider bandwidth. This lead to increased research and development work to innovate advanced Doherty architectures which are more efficient at back-off (BO) power levels compared to traditional DPAs. In this dissertation, three such advanced Doherty architectures and/or techniques are proposed to achieve high efficiency at further BO power level compared to traditional architecture using symmetrical devices for carrier and peaking PAs. Gallium Nitride (GaN) based high-electron-mobility (HEMT) technology has been used to design and fabricate the DPAs to validate the proposed advanced techniques for higher efficiency with good linearity performance at BO power levels.
ContributorsRuhul Hasin, Muhammad (Author) / Kitchen, Jennifer (Thesis advisor) / Aberle, James T., 1961- (Committee member) / Bakkaloglu, Bertan (Committee member) / Kiaei, Sayfe (Committee member) / Arizona State University (Publisher)
Created2018
Description
Semiconductor memory is a key component of the computing systems. Beyond the conventional memory and data storage applications, in this dissertation, both mainstream and eNVM memory technologies are explored for radiation environment, hardware security system and machine learning applications.
In the radiation environment, e.g. aerospace, the memory devices face different energetic particles. The strike of these energetic particles can generate electron-hole pairs (directly or indirectly) as they pass through the semiconductor device, resulting in photo-induced current, and may change the memory state. First, the trend of radiation effects of the mainstream memory technologies with technology node scaling is reviewed. Then, single event effects of the oxide based resistive switching random memory (RRAM), one of eNVM technologies, is investigated from the circuit-level to the system level.
Physical Unclonable Function (PUF) has been widely investigated as a promising hardware security primitive, which employs the inherent randomness in a physical system (e.g. the intrinsic semiconductor manufacturing variability). In the dissertation, two RRAM-based PUF implementations are proposed for cryptographic key generation (weak PUF) and device authentication (strong PUF), respectively. The performance of the RRAM PUFs are evaluated with experiment and simulation. The impact of non-ideal circuit effects on the performance of the PUFs is also investigated and optimization strategies are proposed to solve the non-ideal effects. Besides, the security resistance against modeling and machine learning attacks is analyzed as well.
Deep neural networks (DNNs) have shown remarkable improvements in various intelligent applications such as image classification, speech classification and object localization and detection. Increasing efforts have been devoted to develop hardware accelerators. In this dissertation, two types of compute-in-memory (CIM) based hardware accelerator designs with SRAM and eNVM technologies are proposed for two binary neural networks, i.e. hybrid BNN (HBNN) and XNOR-BNN, respectively, which are explored for the hardware resource-limited platforms, e.g. edge devices.. These designs feature with high the throughput, scalability, low latency and high energy efficiency. Finally, we have successfully taped-out and validated the proposed designs with SRAM technology in TSMC 65 nm.
Overall, this dissertation paves the paths for memory technologies’ new applications towards the secure and energy-efficient artificial intelligence system.
In the radiation environment, e.g. aerospace, the memory devices face different energetic particles. The strike of these energetic particles can generate electron-hole pairs (directly or indirectly) as they pass through the semiconductor device, resulting in photo-induced current, and may change the memory state. First, the trend of radiation effects of the mainstream memory technologies with technology node scaling is reviewed. Then, single event effects of the oxide based resistive switching random memory (RRAM), one of eNVM technologies, is investigated from the circuit-level to the system level.
Physical Unclonable Function (PUF) has been widely investigated as a promising hardware security primitive, which employs the inherent randomness in a physical system (e.g. the intrinsic semiconductor manufacturing variability). In the dissertation, two RRAM-based PUF implementations are proposed for cryptographic key generation (weak PUF) and device authentication (strong PUF), respectively. The performance of the RRAM PUFs are evaluated with experiment and simulation. The impact of non-ideal circuit effects on the performance of the PUFs is also investigated and optimization strategies are proposed to solve the non-ideal effects. Besides, the security resistance against modeling and machine learning attacks is analyzed as well.
Deep neural networks (DNNs) have shown remarkable improvements in various intelligent applications such as image classification, speech classification and object localization and detection. Increasing efforts have been devoted to develop hardware accelerators. In this dissertation, two types of compute-in-memory (CIM) based hardware accelerator designs with SRAM and eNVM technologies are proposed for two binary neural networks, i.e. hybrid BNN (HBNN) and XNOR-BNN, respectively, which are explored for the hardware resource-limited platforms, e.g. edge devices.. These designs feature with high the throughput, scalability, low latency and high energy efficiency. Finally, we have successfully taped-out and validated the proposed designs with SRAM technology in TSMC 65 nm.
Overall, this dissertation paves the paths for memory technologies’ new applications towards the secure and energy-efficient artificial intelligence system.
ContributorsLiu, Rui (Author) / Yu, Shimeng (Thesis advisor, Committee member) / Cao, Yu (Committee member) / Barnaby, Hugh (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2018