Defect Induced Aging and Breakdown in High-k Dielectrics

abstract: High-k dielectrics have been employed in the metal-oxide semiconductor field effect transistors (MOSFETs) since 45 nm technology node. In this MOSFET industry, Moore’s law projects the feature size of MOSFET scales half within every 18 months. Such scaling down theory has not only led to the physical limit of manufacturing but also raised the reliability issues in MOSFETs. After the incorporation of HfO2 based high-k dielectrics, the stacked oxides based gate insulator is facing rather challenging reliability issues due to the vulnerable HfO2 layer, ultra-thin interfacial SiO2 layer, and even messy interface between SiO2 and HfO2. Bias temperature instabilities (BTI), hot channel electrons injections (HCI), stress-induced leakage current (SILC), and time dependent dielectric breakdown (TDDB) are the four most prominent reliability challenges impacting the lifetime of the chips under use. In order to fully understand the origins that could potentially challenge the reliability of the MOSFETs the defects induced aging and breakdown of the high-k dielectrics have been profoundly investigated here. BTI aging has been investigated to be related to charging effects from the bulk oxide traps and generations of Si-H bonds related interface traps. CVS and RVS induced dielectric breakdown studies have been performed and investigated. The breakdown process is regarded to be related to oxygen vacancies generations triggered by hot hole injections from anode. Post breakdown conduction study in the RRAM devices have shown irreversible characteristics of the dielectrics, although the resistance could be switched into high resistance state.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Resistive switching in ALD metal-oxides with engineered interfaces

L'abstract è presente nell'allegato / the abstract is in the attachmen

Variability in Resistive Memories

This research was supported by project B-TIC-624-UGR20 funded by the Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía (Spain) and the FEDER program. F.J.A. acknowledges grant PGC2018-098860-B-I00 and PID2021-128077NB-I00 financed by MCIN/ AEI/10.13039/501100011033/FEDER and A-FQM-66-UGR20 financed by the Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía (Spain) and the FEDER program. M.B.G. acknowledges the Ramón y Cajal Grant No. RYC2020-030150-I. M.L. and M.A.V. acknowl- edge generous support from the King Abdullah University of Science and Technology. A.N.M., N.V.A., A.A.D., M.N.K. and B.S. acknowledge the Government of the Russian Federation under Megagrant Program (agreement no. 074-02-2018-330 (2)) and the Ministry of Science and Higher Education of the Russian Federation under “Priority-2030” Academic Excellence Program of the Lobachevsky State University of Nizhny Novgorod (N-466-99_2021-2023). The authors thank D.O. Filatov, A.S. Novikov, and V.A. Shishmakova for their help in studying the dependence of MFPT on external voltage (Section 4). The devices in Section 4 were designed in the frame of the scientific program of the National Center for Physics and Mathematics (project “Artificial intel- ligence and big data in technical, industrial, natural and social systems”) and fabricated at the facilities of Laboratory of memristor nanoelectronics (state assignment for the creation of new laboratories for electronics industry). E.M. acknowledges the support provided by the European proj- ect MEMQuD, code 20FUN06, which has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.Resistive memories are outstanding electron devices that have displayed a large potential in a plethora of applications such as nonvolatile data storage, neuro- morphic computing, hardware cryptography, etc. Their fabrication control and performance have been notably improved in the last few years to cope with the requirements of massive industrial production. However, the most important hurdle to progress in their development is the so-called cycle-to-cycle variability, which is inherently rooted in the resistive switching mechanism behind the operational principle of these devices. In order to achieve the whole picture, variability must be assessed from different viewpoints going from the experi- mental characterization to the adequation of modeling and simulation techni- ques. Herein, special emphasis is put on the modeling part because the accurate representation of the phenomenon is critical for circuit designers. In this respect, a number of approaches are used to the date: stochastic, behavioral, meso- scopic..., each of them covering particular aspects of the electron and ion transport mechanisms occurring within the switching material. These subjects are dealt with in this review, with the aim of presenting the most recent advancements in the treatment of variability in resistive memories.Junta de Andalucía B-TIC-624-UGR20 PID2021-128077NB-I00European CommissionMCIN/AEI/FEDER A-FQM-66-UGR20 PGC2018-098860-B-I00Spanish Government RYC2020-030150-IKing Abdullah University of Science & TechnologyGovernment of the Russian Federation under Megagrant Program 074-02-2018-330 (2)Ministry of Science and Higher Education of the Russian Federation under "Priority-2030" Academic Excellence Program of the Lobachevsky State University of Nizhny Novgorod N-466-99_2021-2023European project MEMQuD 20FUN06EMPIR programmeEuropean Union's Horizon 2020 research and innovation programm

Hybrid Memristor-CMOS Computer for Artificial Intelligence: from Devices to Systems

Neuromorphic computing systems, which aim to mimic the function and structure of the human brain, is a promising approach to overcome the limitations of conventional computing systems such as the von-Neumann bottleneck. Recently, memristors and memristor crossbars have been extensively studied for neuromorphic system implementations due to the ability of memristor devices to emulate biological synapses, thus providing benefits such as co-located memory/logic operations and massive parallelism. A memristor is a two-terminal device whose resistance is modulated by the history of external stimulation. The principle of the resistance modulation, or resistance switching, for a typical oxide-based memristor, is based on oxygen vacancy migration in the oxide layer through ion drift and diffusion. When applied in computing systems, the memristor is often formed in a crossbar structure and used to perform vector-matrix multiplication operations. Since the values in the matrix can be stored as the device conductance values of the crossbar array, when an input vector is applied as voltage pulses with different pulse amplitudes or different pulse widths to the rows of the crossbar, the currents or charges collected at the columns of the crossbar correspond to the resulting VMM outputs, following Ohm’s law and Kirchhoff’s current law. This approach makes it possible to use physics to execute direct computing of this data-intensive task, both in-memory and in parallel in a single step. First of all, I will present a comprehensive physical model of the TaOx-based memristor device where the internal parameters including electric field, temperature, and VO concentration are self-consistently solved to accurately describe the device operation. Starting from the initial Forming process, the model quantitatively captures the dynamic RS behavior, and can reliably reproduce Set/Reset cycling in a self-consistent manner. Beyond clarifying the nature of the Forming and Set/Reset processes, a bulk-like doping effect was revealed by the model during Set and supported by experimental results. This phenomenon can lead to linear analog conductance modulation with a large dynamic range, which is very beneficial for low-power neuromorphic computing applications. Second, an integrated memristor/CMOS system consisting of a 54×108 passive memristor crossbar array directly fabricated on a CMOS chip is presented. The system includes all necessary analog/digital circuitry (including analog-digital converters and digital-analog converters), digital buses, and a programmable processor to control the digital and analog components to form a complete hardware system for neuromorphic computing applications. With the fully-integrated and reprogrammable chip, we experimentally demonstrated three popular models – a perceptron network, a sparse coding network, and a bilayer principal component analysis system with an unsupervised feature extraction layer and a supervised classification layer – all on the same chip. Beyond VMM operations, the internal dynamics of memristors allow the system to natively process temporal features in the input data. Specifically, a WOx-based memristor with short-term memory effect caused by spontaneous oxygen vacancy diffusion was utilized to implement a reservoir computing system to process temporal information. The spatial information of a digit image can be converted into streaming inputs fed into the memristor reservoir, leading to 100% accuracy for simple 4×5 digit recognition and 88.1% accuracy for the MNIST data set. The system was also employed for solving other nonlinear tasks such as emulating a second-order nonlinear system.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155040/1/seulee_1.pd

Characterization of the doped silicon dioxide and its implications on the resistive switching phenomena in the electrochemical metallization cells

In this Master's thesis, the switching behavior of the doped and undoped SiO2-based memory cells was compared. The aim of doping was to enhance the switching behavior of the ECM memory cells. About 270 samples were sputtered using the CT1000 cluster deposition tool in the IWE2 of RWTH Aachen University. For the deposition of the thin films, the platinum, titanium nitride and Al2O3 substrates were used. The deposition was performed by using three differently doped targets. The physical characterization of the thin films was done using SEM, XRR, XRD, and EDX. Electroforming and electric characterization of the fabricated memory cells were made in the probe station with the light microscope and the Keithley electrometer. The results of the physical and electrical characterization were analyzed using the principle of Exploratory Data Analysis (EDA). The analysis of the result shows that two undoped samples on the platinum substrate and some doped samples exhibit the unexpected volatile threshold switching of metallic and semiconductive origin, respectively. Linear fitting of the measurement data in a logarithmic scale suggests that Schottky- and Frenkel- Poole conduction mechanisms are not dominant

Über die Entwicklung von Memsensoren

Since the postulation of the experimental realization of memristive devices in 2008, a broad variety of concepts for the fabrication of memristive devices has been pursued and the underlying switching mechanisms have been studied in detail. The unique electronic properties of memristive devices inspire applications that go beyond conventional electronics, such as using memristive devices as programmable interconnects, to realize logics for in-array-computing or in neuromorphic engineering. A particularly interesting aspect of biological neural networks is the close connection between signal detection and processing at the neuron level, which is an essential contribution to their outstanding efficiency. This work evolves around the concept of memsensors, which unify the characteristic features of memristive devices and sensor devices and as such appear as promising candidates to realize a close connection between signal detection and processing on the device level. Memsensors are a highly interdisciplinary topic, bridging research in the fields of material science and electrical engineering and relating to insights from biology and medicine through neuromorphic engineering. The major objective of this thesis is to provide tools and building blocks and showcase pathways to incorporate memristive and sensitive properties into memsensor devices. For this purpose, motivated by an experimental point of view, a nanoparticle-based memristive device with diffusive memristive switching characteristics was developed and characterised in detail and sensors relying on semiconducting metal oxide thin films and nanostructures were thoroughly studied. In addition, in terms of modelling of memsensor circuits, emerging features such as amplitude adaptation are discussed, showcasing the particular eligibility of memsensors in the context of neuromorphic engineering

Probing the resistance switching mechanism in silicon suboxide memory devices

Redox-based resistive random access memory has the scope to greatly improve upon current electronic data storage, though the mechanism by which devices operate is not understood completely. In particular, the connection between oxygen migration, the formation of conductive filaments and device longevity is still disputed. Here, I used atomic force microscopy, scanning electron microscopy and x-ray photoelectron spectroscopy to characterise the growth of filaments and the movement of oxygen in silicon-rich silicon oxide memory devices. As such, I was able to establish some of the chemical and structural differences between states of different resistance, which would correspond to binary data storage states. The oxide active layer is reduced simultaneously to the appearance of surface distortion and volumes of high conductivity in an otherwise-insulating material. These results support the established model of a resistance switching mechanism that relies on the migration of oxygen ions under an electrical bias, forming conductive pathways in the switching material. Notably, I demonstrate a reduction in the active layer stoichiometry as a result of electrical stress and show for the first time the presence of multiple filamentary growths in three dimensions in an intrinsic switching material. In addition, I have proven the efficacy of an extension to the method of profiling conductivity variations in insulators in three dimensions using conductive atomic force microscopy. However, in this case my findings conflict with the status quo of this methodology. In particular, I demonstrate that the measurement process significantly affects the scanning probe, leading to the likelihood of data inaccuracy. This highlights the needed for further development of the technique and careful analysis of the data obtained