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- Genre: Masters Thesis
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Description
Efficiency of components is an ever increasing area of importance to portable applications, where a finite battery means finite operating time. Higher efficiency devices need to be designed that don't compromise on the performance that the consumer has come to expect. Class D amplifiers deliver on the goal of increased efficiency, but at the cost of distortion. Class AB amplifiers have low efficiency, but high linearity. By modulating the supply voltage of a Class AB amplifier to make a Class H amplifier, the efficiency can increase while still maintaining the Class AB level of linearity. A 92dB Power Supply Rejection Ratio (PSRR) Class AB amplifier and a Class H amplifier were designed in a 0.24um process for portable audio applications. Using a multiphase buck converter increased the efficiency of the Class H amplifier while still maintaining a fast response time to respond to audio frequencies. The Class H amplifier had an efficiency above the Class AB amplifier by 5-7% from 5-30mW of output power without affecting the total harmonic distortion (THD) at the design specifications. The Class H amplifier design met all design specifications and showed performance comparable to the designed Class AB amplifier across 1kHz-20kHz and 0.01mW-30mW. The Class H design was able to output 30mW into 16Ohms without any increase in THD. This design shows that Class H amplifiers merit more research into their potential for increasing efficiency of audio amplifiers and that even simple designs can give significant increases in efficiency without compromising linearity.
ContributorsPeterson, Cory (Author) / Bakkaloglu, Bertan (Thesis advisor) / Barnaby, Hugh (Committee member) / Kiaei, Sayfe (Committee member) / Arizona State University (Publisher)
Created2013
Description
There is an ever growing need for larger memories which are reliable and fast. New technologies to implement non-volatile memories which are large, fast, compact and cost-efficient are being studied extensively. One of the most promising technologies being developed is the resistive RAM (ReRAM). In ReRAM the resistance of the device varies with the voltage applied across it. Programmable metallization cells (PMC) is one of the devices belonging to this category of non-volatile memories.
In order to advance the development of these devices, there is a need to develop simulation models which replicate the behavior of these devices in circuits. In this thesis, a verilogA model for the PMC has been developed. The behavior of the model has been tested using DC and transient simulations. Experimental data obtained from testing PMC devices fabricated at Arizona State University have been compared to results obtained from simulation.
A basic memory cell known as the 1T 1R cell built using the PMC has also been simulated and verified. These memory cells have the potential to be building blocks of large scale memories. I believe that the verilogA model developed in this thesis will prove to be a powerful tool for researchers and circuit developers looking to develop non-volatile memories using alternative technologies.
In order to advance the development of these devices, there is a need to develop simulation models which replicate the behavior of these devices in circuits. In this thesis, a verilogA model for the PMC has been developed. The behavior of the model has been tested using DC and transient simulations. Experimental data obtained from testing PMC devices fabricated at Arizona State University have been compared to results obtained from simulation.
A basic memory cell known as the 1T 1R cell built using the PMC has also been simulated and verified. These memory cells have the potential to be building blocks of large scale memories. I believe that the verilogA model developed in this thesis will prove to be a powerful tool for researchers and circuit developers looking to develop non-volatile memories using alternative technologies.
ContributorsBharadwaj, Vineeth (Author) / Barnaby, Hugh (Thesis advisor) / Kozicki, Michael (Committee member) / Mikkola, Esko (Committee member) / Arizona State University (Publisher)
Created2014
Description
Nonvolatile memory (NVM) technologies have been an integral part of electronic systems for the past 30 years. The ideal non-volatile memory have minimal physical size, energy usage, and cost while having maximal speed, capacity, retention time, and radiation hardness. A promising candidate for next-generation memory is ion-conducting bridging RAM which is referred to as programmable metallization cell (PMC), conductive bridge RAM (CBRAM), or electrochemical metallization memory (ECM), which is likely to surpass flash memory in all the ideal memory characteristics. A comprehensive physics-based model is needed to completely understand PMC operation and assist in design optimization.
To advance the PMC modeling effort, this thesis presents a precise physical model parameterizing materials associated with both ion-rich and ion-poor layers of the PMC's solid electrolyte, so that captures the static electrical behavior of the PMC in both its low-resistance on-state (LRS) and high resistance off-state (HRS). The experimental data is measured from a chalcogenide glass PMC designed and manufactured at ASU. The static on- and off-state resistance of a PMC device composed of a layered (Ag-rich/Ag-poor) Ge30Se70 ChG film is characterized and modeled using three dimensional simulation code written in Silvaco Atlas finite element analysis software. Calibrating the model to experimental data enables the extraction of device parameters such as material bandgaps, workfunctions, density of states, carrier mobilities, dielectric constants, and affinities.
The sensitivity of our modeled PMC to the variation of its prominent achieved material parameters is examined on the HRS and LRS impedance behavior.
The obtained accurate set of material parameters for both Ag-rich and Ag-poor ChG systems and process variation verification on electrical characteristics enables greater fidelity in PMC device simulation, which significantly enhances our ability to understand the underlying physics of ChG-based resistive switching memory.
To advance the PMC modeling effort, this thesis presents a precise physical model parameterizing materials associated with both ion-rich and ion-poor layers of the PMC's solid electrolyte, so that captures the static electrical behavior of the PMC in both its low-resistance on-state (LRS) and high resistance off-state (HRS). The experimental data is measured from a chalcogenide glass PMC designed and manufactured at ASU. The static on- and off-state resistance of a PMC device composed of a layered (Ag-rich/Ag-poor) Ge30Se70 ChG film is characterized and modeled using three dimensional simulation code written in Silvaco Atlas finite element analysis software. Calibrating the model to experimental data enables the extraction of device parameters such as material bandgaps, workfunctions, density of states, carrier mobilities, dielectric constants, and affinities.
The sensitivity of our modeled PMC to the variation of its prominent achieved material parameters is examined on the HRS and LRS impedance behavior.
The obtained accurate set of material parameters for both Ag-rich and Ag-poor ChG systems and process variation verification on electrical characteristics enables greater fidelity in PMC device simulation, which significantly enhances our ability to understand the underlying physics of ChG-based resistive switching memory.
ContributorsRajabi, Saba (Author) / Barnaby, Hugh (Thesis advisor) / Kozicki, Michael (Committee member) / Vasileska, Dragica (Committee member) / Arizona State University (Publisher)
Created2014
Description
Programmable metallization cell (PMC) technology employs the mechanisms of metal ion transport in solid electrolytes (SE) and electrochemical redox reactions in order to form metallic electrodeposits. When a positive bias is applied to an anode opposite to a cathode, atoms at the anode are oxidized to ions and dissolve into the SE. Under the influence of the electric field, the ions move to the cathode and become reduced to form the electrodeposits. These electrodeposits are filamentary in nature and persistent, and since they are metallic can alter the physical characteristics of the material on which they are formed. PMCs can be used as next generation memories, radio frequency (RF) switches and physical unclonable functions (PUFs).
The morphology of the filaments is impacted by the biasing conditions. Under a relatively high applied electric field, they form as dendritic elements with a low fractal dimension (FD), whereas a low electric field leads to high FD features. Ion depletion effects in the SE due to low ion diffusivity/mobility also influences the morphology by limiting the ion supply into the growing electrodeposit.
Ion transport in SE is due to hopping transitions driven by drift and diffusion force. A physical model of ion hopping with Brownian motion has been proposed, in which the ion transitions are random when time window is larger than characteristic time. The random growth process of filaments in PMC adds entropy to the electrodeposition, which leads to random features in the dendritic patterns. Such patterns has extremely high information capacity due to the fractal nature of the electrodeposits.
In this project, lateral-growth PMCs were fabricated, whose LRS resistance is less than 10Ω, which can be used as RF switches. Also, an array of radial-growth PMCs was fabricated, on which multiple dendrites, all with different shapes, could be grown simultaneously. Those patterns can be used as secure keys in PUFs and authentication can be performed by optical scanning.
A kinetic Monte Carlo (KMC) model is developed to simulate the ion transportation in SE under electric field. The simulation results matched experimental data well that validated the ion hopping model.
The morphology of the filaments is impacted by the biasing conditions. Under a relatively high applied electric field, they form as dendritic elements with a low fractal dimension (FD), whereas a low electric field leads to high FD features. Ion depletion effects in the SE due to low ion diffusivity/mobility also influences the morphology by limiting the ion supply into the growing electrodeposit.
Ion transport in SE is due to hopping transitions driven by drift and diffusion force. A physical model of ion hopping with Brownian motion has been proposed, in which the ion transitions are random when time window is larger than characteristic time. The random growth process of filaments in PMC adds entropy to the electrodeposition, which leads to random features in the dendritic patterns. Such patterns has extremely high information capacity due to the fractal nature of the electrodeposits.
In this project, lateral-growth PMCs were fabricated, whose LRS resistance is less than 10Ω, which can be used as RF switches. Also, an array of radial-growth PMCs was fabricated, on which multiple dendrites, all with different shapes, could be grown simultaneously. Those patterns can be used as secure keys in PUFs and authentication can be performed by optical scanning.
A kinetic Monte Carlo (KMC) model is developed to simulate the ion transportation in SE under electric field. The simulation results matched experimental data well that validated the ion hopping model.
ContributorsYu, Weijie (Author) / Kozicki, Michael N (Thesis advisor) / Barnaby, Hugh (Thesis advisor) / Diaz, Rodolfo (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2015
Description
Static CMOS logic has remained the dominant design style of digital systems for
more than four decades due to its robustness and near zero standby current. Static
CMOS logic circuits consist of a network of combinational logic cells and clocked sequential
elements, such as latches and flip-flops that are used for sequencing computations
over time. The majority of the digital design techniques to reduce power, area, and
leakage over the past four decades have focused almost entirely on optimizing the
combinational logic. This work explores alternate architectures for the flip-flops for
improving the overall circuit performance, power and area. It consists of three main
sections.
First, is the design of a multi-input configurable flip-flop structure with embedded
logic. A conventional D-type flip-flop may be viewed as realizing an identity function,
in which the output is simply the value of the input sampled at the clock edge. In
contrast, the proposed multi-input flip-flop, named PNAND, can be configured to
realize one of a family of Boolean functions called threshold functions. In essence,
the PNAND is a circuit implementation of the well-known binary perceptron. Unlike
other reconfigurable circuits, a PNAND can be configured by simply changing the
assignment of signals to its inputs. Using a standard cell library of such gates, a technology
mapping algorithm can be applied to transform a given netlist into one with
an optimal mixture of conventional logic gates and threshold gates. This approach
was used to fabricate a 32-bit Wallace Tree multiplier and a 32-bit booth multiplier
in 65nm LP technology. Simulation and chip measurements show more than 30%
improvement in dynamic power and more than 20% reduction in core area.
The functional yield of the PNAND reduces with geometry and voltage scaling.
The second part of this research investigates the use of two mechanisms to improve
the robustness of the PNAND circuit architecture. One is the use of forward and reverse body biases to change the device threshold and the other is the use of RRAM
devices for low voltage operation.
The third part of this research focused on the design of flip-flops with non-volatile
storage. Spin-transfer torque magnetic tunnel junctions (STT-MTJ) are integrated
with both conventional D-flipflop and the PNAND circuits to implement non-volatile
logic (NVL). These non-volatile storage enhanced flip-flops are able to save the state of
system locally when a power interruption occurs. However, manufacturing variations
in the STT-MTJs and in the CMOS transistors significantly reduce the yield, leading
to an overly pessimistic design and consequently, higher energy consumption. A
detailed analysis of the design trade-offs in the driver circuitry for performing backup
and restore, and a novel method to design the energy optimal driver for a given yield is
presented. Efficient designs of two nonvolatile flip-flop (NVFF) circuits are presented,
in which the backup time is determined on a per-chip basis, resulting in minimizing
the energy wastage and satisfying the yield constraint. To achieve a yield of 98%,
the conventional approach would have to expend nearly 5X more energy than the
minimum required, whereas the proposed tunable approach expends only 26% more
energy than the minimum. A non-volatile threshold gate architecture NV-TLFF are
designed with the same backup and restore circuitry in 65nm technology. The embedded
logic in NV-TLFF compensates performance overhead of NVL. This leads to the
possibility of zero-overhead non-volatile datapath circuits. An 8-bit multiply-and-
accumulate (MAC) unit is designed to demonstrate the performance benefits of the
proposed architecture. Based on the results of HSPICE simulations, the MAC circuit
with the proposed NV-TLFF cells is shown to consume at least 20% less power and
area as compared to the circuit designed with conventional DFFs, without sacrificing
any performance.
more than four decades due to its robustness and near zero standby current. Static
CMOS logic circuits consist of a network of combinational logic cells and clocked sequential
elements, such as latches and flip-flops that are used for sequencing computations
over time. The majority of the digital design techniques to reduce power, area, and
leakage over the past four decades have focused almost entirely on optimizing the
combinational logic. This work explores alternate architectures for the flip-flops for
improving the overall circuit performance, power and area. It consists of three main
sections.
First, is the design of a multi-input configurable flip-flop structure with embedded
logic. A conventional D-type flip-flop may be viewed as realizing an identity function,
in which the output is simply the value of the input sampled at the clock edge. In
contrast, the proposed multi-input flip-flop, named PNAND, can be configured to
realize one of a family of Boolean functions called threshold functions. In essence,
the PNAND is a circuit implementation of the well-known binary perceptron. Unlike
other reconfigurable circuits, a PNAND can be configured by simply changing the
assignment of signals to its inputs. Using a standard cell library of such gates, a technology
mapping algorithm can be applied to transform a given netlist into one with
an optimal mixture of conventional logic gates and threshold gates. This approach
was used to fabricate a 32-bit Wallace Tree multiplier and a 32-bit booth multiplier
in 65nm LP technology. Simulation and chip measurements show more than 30%
improvement in dynamic power and more than 20% reduction in core area.
The functional yield of the PNAND reduces with geometry and voltage scaling.
The second part of this research investigates the use of two mechanisms to improve
the robustness of the PNAND circuit architecture. One is the use of forward and reverse body biases to change the device threshold and the other is the use of RRAM
devices for low voltage operation.
The third part of this research focused on the design of flip-flops with non-volatile
storage. Spin-transfer torque magnetic tunnel junctions (STT-MTJ) are integrated
with both conventional D-flipflop and the PNAND circuits to implement non-volatile
logic (NVL). These non-volatile storage enhanced flip-flops are able to save the state of
system locally when a power interruption occurs. However, manufacturing variations
in the STT-MTJs and in the CMOS transistors significantly reduce the yield, leading
to an overly pessimistic design and consequently, higher energy consumption. A
detailed analysis of the design trade-offs in the driver circuitry for performing backup
and restore, and a novel method to design the energy optimal driver for a given yield is
presented. Efficient designs of two nonvolatile flip-flop (NVFF) circuits are presented,
in which the backup time is determined on a per-chip basis, resulting in minimizing
the energy wastage and satisfying the yield constraint. To achieve a yield of 98%,
the conventional approach would have to expend nearly 5X more energy than the
minimum required, whereas the proposed tunable approach expends only 26% more
energy than the minimum. A non-volatile threshold gate architecture NV-TLFF are
designed with the same backup and restore circuitry in 65nm technology. The embedded
logic in NV-TLFF compensates performance overhead of NVL. This leads to the
possibility of zero-overhead non-volatile datapath circuits. An 8-bit multiply-and-
accumulate (MAC) unit is designed to demonstrate the performance benefits of the
proposed architecture. Based on the results of HSPICE simulations, the MAC circuit
with the proposed NV-TLFF cells is shown to consume at least 20% less power and
area as compared to the circuit designed with conventional DFFs, without sacrificing
any performance.
ContributorsYang, Jinghua (Author) / Vrudhula, Sarma (Thesis advisor) / Barnaby, Hugh (Committee member) / Cao, Yu (Committee member) / Seo, Jae-Sun (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
Non-volatile memory (NVM) has become a staple in the everyday life of consumers. NVM manifests inside cell phones, laptops, and most recently, wearable tech such as smart watches. NAND Flash has been an excellent solution to conditions requiring fast, compact NVM. Current technology nodes are nearing the physical limits of scaling, preventing flash from improving. To combat the limitations of flash and to appease consumer demand for progressively faster and denser NVM, new technologies are needed. One possible candidate for the replacement of NAND Flash is programmable metallization cells (PMC). PMC are a type of resistive memory, meaning that they do not rely on charge storage to maintain a logic state. Depending on their application, it is possible that devices containing NVM will be exposed to harsh radiation environments. As part of the process for developing a novel memory technology, it is important to characterize the effects irradiation has on the functionality of the devices.
This thesis characterizes the effects that ionizing γ-ray irradiation has on the retention of the programmed resistive state of a PMC. The PMC devices tested used Ge30Se70 doped with Ag as the solid electrolyte layer and were fabricated by the thesis author in a Class 100 clean room. Individual device tiles were wire bonded into ceramic packages and tested in a biased and floating contact scenario.
The first scenario presented shows that PMC devices are capable of retaining their programmed state up to the maximum exposed total ionizing dose (TID) of 3.1 Mrad(Si). In this first scenario, the contacts of the PMC devices were left floating during exposure. The second scenario tested shows that the PMC devices are capable of retaining their state until the maximum TID of 10.1 Mrad(Si) was reached. The contacts in the second scenario were biased, with a 50 mV read voltage applied to the anode contact. Analysis of the results show that Ge30Se70 PMC are ionizing radiation tolerant and can retain a programmed state to a higher TID than NAND Flash memory.
This thesis characterizes the effects that ionizing γ-ray irradiation has on the retention of the programmed resistive state of a PMC. The PMC devices tested used Ge30Se70 doped with Ag as the solid electrolyte layer and were fabricated by the thesis author in a Class 100 clean room. Individual device tiles were wire bonded into ceramic packages and tested in a biased and floating contact scenario.
The first scenario presented shows that PMC devices are capable of retaining their programmed state up to the maximum exposed total ionizing dose (TID) of 3.1 Mrad(Si). In this first scenario, the contacts of the PMC devices were left floating during exposure. The second scenario tested shows that the PMC devices are capable of retaining their state until the maximum TID of 10.1 Mrad(Si) was reached. The contacts in the second scenario were biased, with a 50 mV read voltage applied to the anode contact. Analysis of the results show that Ge30Se70 PMC are ionizing radiation tolerant and can retain a programmed state to a higher TID than NAND Flash memory.
ContributorsTaggart, Jennifer Lynn (Author) / Barnaby, Hugh (Thesis advisor) / Kozicki, Michael (Committee member) / Holbert, Keith E. (Committee member) / Arizona State University (Publisher)
Created2015