Etude de l'intégration du collage direct cuivre/oxyde pour l'élaboration d'une architecture 3D-SIC

The context of this work is the three-dimensional integration of electronic devices. Among the various techniques allowing to assemble both mechanically and electrically stacked chips, the direct bonding of Cu-SiO2 mixed surfaces is the most promising option to date. Thanks to this method, the interconnection density of 106/cm² aimed by the industry is achievable, while providing a low contact resistivity and excellent reliability. The objective of this study is to demonstrate the compatibility of the direct hybrid bonding Cu-SiO2 process with integrations and architectures that mimic real circuits. For this purpose, test vehicles incorporating two-layer and four-layer copper test structures have been specifically designed. Furthermore, finite element simulations of the direct bonding process have been developed within the Abaqus software. First, the 200 and 300 mm chip-to-wafer direct bonding process is validated. Morphological and electrical characterizations show that this stacking method does not deteriorate the integrity and performances of two-layer test structures with respect to a wafer-to-wafer integration. Furthermore, thermal cycling tests confirm the excellent mechanical strength of the bonded dies. The second part of this work focuses on morphological, electrical and reliability characterizations of four-layer test structures. In this case, the 200 mm wafer-to-wafer architecture of the test vehicles is close to an industrial integration. The various observations conducted with scanning and transmission electron microscopy indicate an excellent bonding quality of Cu/Cu and SiO2/SiO2 interfaces. Furthermore, the formation mechanisms of cavities at the Cu/Cu interface and the copper diffusion phenomenon in the silica are investigated. Electrical characterizations show functional yields above 95 % and standard deviations below 3 % after annealing at 200 or 400 °C. Finally, reliability studies including unbiased HAST, thermal cycling, temperature storage and électromigration test prove the resistance to corrosion and the mechanical robustness of this integration. Finally, the finite element simulations indicate that the cohesive interactions at the bonding interface, combined with the thermal expansion of the copper during the annealing, significantly assist the bonding process of copper surfaces with a dishing effect. In addition, the macroscopic plastic deformation of the copper appears to have a detrimental effect on the sealing of the interface by slowing the propagation of the bonding wave.Cette thèse s'inscrit dans le contexte de l'intégration tridimensionnelle des dispositifs électroniques. Parmi les différentes techniques permettant d'assembler à la fois mécaniquement et électriquement les puces empilées, le collage direct de surfaces mixtes Cu-SiO2 représente l'option la plus prometteuse à ce jour. En effet, cette méthode permet d'atteindre la densité d'interconnexions de 106/cm² visée par l'industrie, tout en offrant une faible résistivité de contact et une excellente fiabilité. L'objectif de ce travail est de démontrer la compatibilité du procédé de collage direct hybride Cu-SiO2 avec des intégrations et des architectures proches de circuits réels. Dans ce but, des véhicules de tests intégrant des structures de cuivre à deux et quatre niveaux d'interconnexions ont été conçus spécifiquement. De plus, des simulations par éléments finis du procédé collage direct ont été développées au sein du logiciel Abaqus. Dans un premier temps, le procédé de collage direct puce-à-plaque en 200 et 300 mm est validé. Des caractérisations morphologiques et électriques montrent que cette méthode d'assemblage ne dégrade pas l'intégrité et les performances de structures de tests à deux niveaux par rapport à une intégration plaque-à-plaque. Par ailleurs, des tests de cyclage thermique confirment l'excellente robustesse mécanique des empilements. La deuxième partie de cette thèse s'intéresse à la caractérisation de la morphologie, des performances électriques et de la fiabilité de structures de tests à quatre niveaux d'interconnexions. Dans ce cas, l'architecture plaque-à-plaque en 200 mm des véhicules de tests se veut proche d'une intégration industrielle. Les diverses observations par microscopie électronique à balayage et en transmission indiquent une excellente qualité de collage des interfaces Cu/Cu et SiO2/SiO2. Par ailleurs, les mécanismes de formation des cavités nanométriques à l'interface Cu/Cu et le phénomène de diffusion du cuivre dans la silice sont investigués. Les caractérisations électriques révèlent des rendements de fonctionnement supérieurs à 95 % ainsi que des écarts types inférieurs à 3 % après recuit à 200 ou 400 °C. Enfin, les études de fiabilité incluant des tests de stockage en chaleur humide, de cyclage thermique, de stockage en température et d'électromigration attestent de la résistance à la corrosion et de la robustesse mécanique de cette intégration. Pour finir, les simulations par éléments finis indiquent que les interactions cohésives à l'interface de collage, combinées à la dilatation thermique du cuivre pendant le recuit, assistent significativement le processus de collage de surfaces de cuivre incurvées par sur-polissage. En outre, la déformation plastique macroscopique du cuivre semble avoir un effet néfaste sur le processus de scellement en freinant la propagation de l'onde de collage

Conductive filament evolution dynamics revealed by cryogenic (1.5 K) multilevel switching of CMOS-compatible Al2O3/TiO2 resistive memories

This study demonstrates multilevel switching at 1.5 K of Al2O3/TiO2-x resistive memory devices fabricated with CMOS-compatible processes and materials. The I-V characteristics exhibit a negative differential resistance (NDR) effect due to a Joule-heating-induced metal-insulator transition of the Ti4O7 conductive filament. Carrier transport analysis of all multilevel switching I-V curves show that while the insulating regime follows the space charge limited current (SCLC) model for all resistance states, the conduction in the metallic regime is dominated by SCLC and trap-assisted tunneling (TAT) for low- and high-resistance states respectively. A non-monotonic conductance evolution is observed in the insulating regime, as opposed to the continuous and gradual conductance increase and decrease obtained in the metallic regime during the multilevel SET and RESET operations. Cryogenic transport analysis coupled to an analytical model accounting for the metal-insulator-transition-induced NDR effects and the resistance states of the device provide new insights on the conductive filament evolution dynamics and resistive switching mechanisms. Our findings suggest that the non-monotonic conductance evolution in the insulating regime is due to the combined effects of longitudinal and radial variations of the Ti4O7 conductive filament during the switching. This behavior results from the interplay between temperature- and field-dependent geometrical and physical characteristics of the filament.Comment: 8 pages, 4 figure

Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition

Surface electromyogram (sEMG) signals result from muscle movement and hence they are an ideal candidate for benchmarking event-driven sensing and computing. We propose a simple yet novel approach for optimizing the spike encoding algorithm's hyper-parameters inspired by the readout layer concept in reservoir computing. Using a simple machine learning algorithm after spike encoding, we report performance higher than the state-of-the-art spiking neural networks on two open-source datasets for hand gesture recognition. The spike encoded data is processed through a spiking reservoir with a biologically inspired topology and neuron model. When trained with the unsupervised activity regulation CRITICAL algorithm to operate at the edge of chaos, the reservoir yields better performance than state-of-the-art convolutional neural networks. The reservoir performance with regulated activity was found to be 89.72% for the Roshambo EMG dataset and 70.6% for the EMG subset of sensor fusion dataset. Therefore, the biologically-inspired computing paradigm, which is known for being power efficient, also proves to have a great potential when compared with conventional AI algorithms.Comment: Accepted to International Conference on Neuromorphic Systems (ICONS 2021

Observation of Highly Nonlinear Resistive Switching of Al2O3/TiO2-x Memristors at Cryogenic Temperature (1.5 K)

In this work, we investigate the behavior of Al2O3/TiO2-x cross-point memristors in cryogenic environment. We report successful resistive switching of memristor devices from 300 K down to 1.5 K. The I-V curves exhibit negative differential resistance effects between 130 and 1.5 K, attributed to a metal-insulator transition (MIT) of the Ti4O7 conductive filament. The resulting highly nonlinear behavior is associated to a maximum ION/IOFF ratio of 84 at 1.5 K, paving the way to selector-free cryogenic passive crossbars. Finally, temperature-dependant thermal activation energies related to the conductance at low bias (20 mV) are extracted for memristors in low resistance state, suggesting hopping-type conduction mechanisms.Comment: 4 pages, 4 figures, IEEE 14th Nanotechnology Materials & Devices Conference (NMDC 2019

Analog programming of CMOS-compatible Al2_2O3_3/TiO2-x_\textrm{2-x} memristor at 4.2 K after metal-insulator transition suppression by cryogenic reforming

The exploration of memristors' behavior at cryogenic temperatures has become crucial due to the growing interest in quantum computing and cryogenic electronics. In this context, our study focuses on the characterization at cryogenic temperatures (4.2 K) of TiO$_\textrm{2-x}$-based memristors fabricated with a CMOS-compatible etch-back process. We demonstrate a so-called cryogenic reforming (CR) technique performed at 4.2 K to overcome the well-known metal-insulator transition (MIT) which limits the analog behavior of memristors at low temperatures. This cryogenic reforming process was found to be reproducible and led to a durable suppression of the MIT. This process allowed to reduce by approximately 20% the voltages required to perform DC resistive switching at 4.2 K. Additionally, conduction mechanism studies of memristors before and after cryogenic reforming from 4.2 K to 300 K revealed different behaviors above 100 K, indicating a potential change in the conductive filament stoichiometry. The reformed devices exhibit a conductance level that is 50 times higher than ambient-formed memristor, and the conduction drop between 300 K and 4.2 K is 100 times smaller, indicating the effectiveness of the reforming process. More importantly, CR enables analog programming at 4.2 K with typical read voltages. Suppressing the MIT improved the analog switching dynamics of the memristor leading to approximately 250% larger on/off ratios during long-term depression (LTD)/long-term potentiation (LTP) resistance tuning. This enhancement opens up the possibility of using TiO$_{\textrm{2-x}}$-based memristors to be used as synapses in neuromorphic computing at cryogenic temperatures

AIDX: Adaptive Inference Scheme to Mitigate State-Drift in Memristive VMM Accelerators

An adaptive inference method for crossbar (AIDX) is presented based on an optimization scheme for adjusting the duration and amplitude of input voltage pulses. AIDX minimizes the long-term effects of memristance drift on artificial neural network accuracy. The sub-threshold behavior of memristor has been modeled and verified by comparing with fabricated device data. The proposed method has been evaluated by testing on different network structures and applications, e.g., image reconstruction and classification tasks. The results showed an average of 60% improvement in convolutional neural network (CNN) performance on CIFAR10 dataset after 10000 inference operations as well as 78.6% error reduction in image reconstruction.Comment: This paper is submitted to IEEE Transactions Circuits and Systems II: Express Brief

Oxygen vacancy engineering of TaOx-based resistive memories by Zr doping for improved variability and synaptic behavior

Resistive switching devices are promising emerging non-volatile memories. However, one of the biggest challenges for resistive switching (RS) memory applications is the device-to-device (D2D) variability which is related to the intrinsic stochastic formation and configuration of oxygen vacancy (VO) conductive filaments. In order to reduce D2D variability, the control of oxygen vacancy formation and configuration is paramount. We report in this study Zr doping of TaOx-based RS devices prepared by pulsed laser deposition (PLD) as an efficient mean to reduce VO formation energy and increase conductive filament (CF) confinement, thus reducing D2D variability. Such findings were supported by X-ray photoelectron spectroscopy (XPS), spectroscopic ellipsometry (SE) and electronic transport analysis. Zr doped films presented increased VO concentration, and more localized VO thanks to the interaction with Zr. According to DC and pulse mode electrical characterization, D2D variability was decreased by a factor of 7, resistance window was doubled and a more gradual and monotonic long-term potentiation/depression (LTP/LTD) in pulse switching was achieved in forming-free Zr:TaOx devices thus displaying promising performance for artificial synapse applications.Comment: 19 pages, 10 figure

Hardware-aware Training Techniques for Improving Robustness of Ex-Situ Neural Network Transfer onto Passive TiO2 ReRAM Crossbars

Passive resistive random access memory (ReRAM) crossbar arrays, a promising emerging technology used for analog matrix-vector multiplications, are far superior to their active (1T1R) counterparts in terms of the integration density. However, current transfers of neural network weights into the conductance state of the memory devices in the crossbar architecture are accompanied by significant losses in precision due to hardware variabilities such as sneak path currents, biasing scheme effects and conductance tuning imprecision. In this work, training approaches that adapt techniques such as dropout, the reparametrization trick and regularization to TiO2 crossbar variabilities are proposed in order to generate models that are better adapted to their hardware transfers. The viability of this approach is demonstrated by comparing the outputs and precision of the proposed hardware-aware network with those of a regular fully connected network over a few thousand weight transfers using the half moons dataset in a simulation based on experimental data. For the neural network trained using the proposed hardware-aware method, 79.5% of the test set's data points can be classified with an accuracy of 95% or higher, while only 18.5% of the test set's data points can be classified with this accuracy by the regularly trained neural network.Comment: 15 pages, 11 figure