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Description
Scaling of the classical planar MOSFET below 20 nm gate length is facing not only technological difficulties but also limitations imposed by short channel effects, gate and junction leakage current due to quantum tunneling, high body doping induced threshold voltage variation, and carrier mobility degradation. Non-classical multiple-gate structures such as double-gate (DG) FinFETs and surrounding gate field-effect-transistors (SGFETs) have good electrostatic integrity and are an alternative to planar MOSFETs for below 20 nm technology nodes. Circuit design with these devices need compact models for SPICE simulation. In this work physics based compact models for the common-gate symmetric DG-FinFET, independent-gate asymmetric DG-FinFET, and SGFET are developed. Despite the complex device structure and boundary conditions for the Poisson-Boltzmann equation, the core structure of the DG-FinFET and SGFET models, are maintained similar to the surface potential based compact models for planar MOSFETs such as SP and PSP. TCAD simulations show differences between the transient behavior and the capacitance-voltage characteristics of bulk and SOI FinFETs if the gate-voltage swing includes the accumulation region. This effect can be captured by a compact model of FinFETs only if it includes the contribution of both types of carriers in the Poisson-Boltzmann equation. An accurate implicit input voltage equation valid in all regions of operation is proposed for common-gate symmetric DG-FinFETs with intrinsic or lightly doped bodies. A closed-form algorithm is developed for solving the new input voltage equation including ambipolar effects. The algorithm is verified for both the surface potential and its derivatives and includes a previously published analytical approximation for surface potential as a special case when ambipolar effects can be neglected. The symmetric linearization method for common-gate symmetric DG-FinFETs is developed in a form free of the charge-sheet approximation present in its original formulation for bulk MOSFETs. The accuracy of the proposed technique is verified by comparison with exact results. An alternative and computationally efficient description of the boundary between the trigonometric and hyperbolic solutions of the Poisson-Boltzmann equation for the independent-gate asymmetric DG-FinFET is developed in terms of the Lambert W function. Efficient numerical algorithm is proposed for solving the input voltage equation. Analytical expressions for terminal charges of an independent-gate asymmetric DG-FinFET are derived. The new charge model is C-infinity continuous, valid for weak as well as for strong inversion condition of both the channels and does not involve the charge-sheet approximation. This is accomplished by developing the symmetric linearization method in a form that does not require identical boundary conditions at the two Si-SiO2 interfaces and allows for volume inversion in the DG-FinFET. Verification of the model is performed with both numerical computations and 2D TCAD simulations under a wide range of biasing conditions. The model is implemented in a standard circuit simulator through Verilog-A code. Simulation examples for both digital and analog circuits verify good model convergence and demonstrate the capabilities of new circuit topologies that can be implemented using independent-gate asymmetric DG-FinFETs.
ContributorsDessai, Gajanan (Author) / Gildenblat, Gennady (Committee member) / McAndrew, Colin (Committee member) / Cao, Yu (Committee member) / Barnaby, Hugh (Committee member) / Arizona State University (Publisher)
Created2012
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
Micro-electro-mechanical systems (MEMS) film bulk acoustic resonator (FBAR) demonstrates label-free biosensing capabilities and is considered to be a promising alternative of quartz crystal microbalance (QCM). FBARs achieve great success in vacuum, or in the air, but find limited applications in liquid media because squeeze damping significantly degrades quality factor (Q) and results in poor frequency resolution. A transmission-line model shows that by confining the liquid in a thickness comparable to the acoustic wavelength of the resonator, Q can be considerably improved. The devices exhibit damped oscillatory patterns of Q as the liquid thickness varies. Q assumes its maxima and minima when the channel thickness is an odd and even multiple of the quarter-wavelength of the resonance, respectively. Microfluidic channels are integrated with longitudinal-mode FBARs (L-FBARs) to realize this design; a tenfold improvement of Q over fully-immersed devices is experimentally verified. Microfluidic integrated FBAR sensors have been demonstrated for detecting protein binding in liquid and monitoring the Vroman effect (the competitive protein adsorption behavior), showing their potential as a promising bio-analytical tool. A contour-mode FBAR (C-FBAR) is developed to further improve Q and to alleviate the need for complex integration of microfluidic channels. The C-FBAR consists of a suspended piezoelectric ring made of aluminum nitride and is excited in the fundamental radial-extensional mode. By replacing the squeeze damping with shear damping, high Qs (189 in water and 77 in human whole blood) are obtained in semi-infinite depth liquids. The C-FBAR sensors are characterized by aptamer - thrombin binding pairs and aqueous glycerine solutions for mass and viscosity sensing schemes, respectively. The C-FBAR sensor demonstrates accurate viscosity measurement from 1 to 10 centipoise, and can be deployed to monitor in-vitro blood coagulation processes in real time. Results show that its resonant frequency decreases as the viscosity of the blood increases during the fibrin generation process after the coagulation cascade. The coagulation time and the start/end of the fibrin generation are quantitatively determined, showing the C-FBAR can be a low-cost, portable yet reliable tool for hemostasis diagnostics.
ContributorsXu, Wencheng (Author) / Chae, Junseok (Thesis advisor) / Phillips, Stephen (Committee member) / Cao, Yu (Committee member) / Kozicki, Michael (Committee member) / Arizona State University (Publisher)
Created2011
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
With the progression of different industries moving away from employing secretaries for business professionals and professors, there exists a void in the area of personal assistance. This problem has existing solutions readily available to replace this service, i.e. secretary or personal assistant, tend to range from expensive and useful to inexpensive and not efficient. This leaves a low cost niche into the market of a virtual office assistant or manager to display messages and to help direct people in obtaining contact information. The development of a low cost solution revolves around the software needed to solve the various problems an accessible and user friendly Virtual Interface in which the owner of the Virtual Office Manager/Assistant can communicate to colleagues who are at standby outside of the owner's office and vice versa. This interface will be allowing the owner to describe the status pertaining to their absence or any other message sent to the interface. For example, the status of the owner's work commute can be described with a simple "Running Late" phrase or a message like "Busy come back in 10 minutes". In addition, any individual with an interest to these entries will have the opportunity to respond back because the device will provide contact information. When idle, the device will show supplemental information such as the owner's calendar and name. The scope of this will be the development and testing of solutions to achieve these goals.
ContributorsOffenberger, Spencer Eliot (Author) / Kozicki, Michael (Thesis director) / Goryll, Michael (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
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
Description
Over decades, scientists have been scaling devices to increasingly smaller feature sizes for ever better performance of complementary metal-oxide semiconductor (CMOS) technology to meet requirements on speed, complexity, circuit density, power consumption and ultimately cost required by many advanced applications. However, going to these ultra-scaled CMOS devices also brings some drawbacks. Aging due to bias-temperature-instability (BTI) and Hot carrier injection (HCI) is the dominant cause of functional failure in large scale logic circuits. The aging phenomena, on top of process variations, translate into complexity and reduced design margin for circuits. Such issues call for “Design for Reliability”. In order to increase the overall design efficiency, it is important to (i) study the impact of aging on circuit level along with the transistor level understanding (ii) calibrate the theoretical findings with measurement data (iii) implementing tools that analyze the impact of BTI and HCI reliability on circuit timing into VLSI design process at each stage. In this work, post silicon measurements of a 28nm HK-MG technology are done to study the effect of aging on Frequency Degradation of digital circuits. A novel voltage controlled ring oscillator (VCO) structure, developed by NIMO research group is used to determine the effect of aging mechanisms like NBTI, PBTI and SILC on circuit parameters. Accelerated aging mechanism is proposed to avoid the time consuming measurement process and extrapolation of data to the end of life thus instead of predicting the circuit behavior, one can measure it, within a short period of time. Finally, to bridge the gap between device level models and circuit level aging analysis, a System Level Reliability Analysis Flow (SyRA) developed by NIMO group, is implemented for a TSMC 65nm industrial level design to achieve one-step reliability prediction for digital design.
ContributorsBansal, Ankita (Author) / Cao, Yu (Thesis advisor) / Seo, Jae sun (Committee member) / Barnaby, Hugh (Committee member) / Arizona State University (Publisher)
Created2016
Description
Characterization of standard cells is one of the crucial steps in the IC design. Scaling of CMOS technology has lead to timing un-certainties such as that of cross coupling noise due to interconnect parasitic, skew variation due to voltage jitter and proximity effect of multiple inputs switching (MIS). Due to increased operating frequency and process variation, the probability of MIS occurrence and setup / hold failure within a clock cycle is high. The delay variation due to temporal proximity of MIS is significant for multiple input gates in the standard cell library. The shortest paths are affected by MIS due to the lack of averaging effect. Thus, sensitive designs such as that of SRAM row and column decoder circuits have high probability for MIS impact. The traditional static timing analysis (STA) assumes single input switching (SIS) scenario which is not adequate enough to capture gate delay accurately, as the delay variation due to temporal proximity of the MIS is ~15%-45%. Whereas, considering all possible scenarios of MIS for characterization is computationally intensive with huge data volume. Various modeling techniques are developed for the characterization of MIS effect. Some techniques require coefficient extraction through multiple spice simulation, and do not discuss speed up approach or apply models with complicated algorithms to account for MIS effect. The STA flow accounts for process variation through uncertainty parameter to improve product yield. Some of the MIS delay variability models account for MIS variation through table look up approach, resulting in huge data volume or do not consider propagation of RAT in the design flow. Thus, there is a need for a methodology to model MIS effect with less computational resource, and integration of such effect into design flow without trading off the accuracy. A finite-point based analytical model for MIS effect is proposed for multiple input logic gates and similar approach is extended for setup/hold characterization of sequential elements. Integration of MIS variation into design flow is explored. The proposed methodology is validated using benchmark circuits at 45nm technology node under process variation. Experimental results show significant reduction in runtime and data volume with ~10% error compared to that of SPICE simulation.
ContributorsSubramaniam, Anupama R (Author) / Cao, Yu (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Roveda, Janet (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2012
Description
This creative project is an extension of the work being done as part of Senior Design in<br/>developing the See-Through Car Pillar, a system designed to render the forward car pillars in a car<br/>invisible to the driver so they can have an unobstructed view utilizing displays, sensors, and a<br/>computer. The first half of the paper provides the motivation, design and progress of the project, <br/>while the latter half provides a literature survey on current automobile trends, the viability of the<br/>See-Through Car Pillar as a product in the market through case studies, and alternative designs and <br/>technologies that also might address the problem statement.
ContributorsRoy, Delwyn J (Author) / Thornton, Trevor (Thesis director) / Kozicki, Michael (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05