Highly reproducible and CMOS-compatible VO2-based oscillators for brain-inspired computing.

With remarkable electrical and optical switching properties induced at low power and near room temperature (68 °C), vanadium dioxide (VO2) has sparked rising interest in unconventional computing among the phase-change materials research community. The scalability and the potential to compute beyond the von Neumann model make VO2 especially appealing for implementation in oscillating neural networks for artificial intelligence applications, to solve constraint satisfaction problems, and for pattern recognition. Its integration into large networks of oscillators on a Silicon platform still poses challenges associated with the stabilization in the correct oxidation state and the ability to fabricate a structure with predictable electrical behavior showing very low variability. In this work, the role played by the different annealing parameters applied by three methods (slow thermal annealing, flash annealing, and rapid thermal annealing), following the vanadium oxide atomic layer deposition, on the formation of VO2 grains is studied and an optimal substrate stack configuration that minimizes variability between devices is proposed. Material and electrical characterizations are performed on the different films and a step-by-step recipe to build reproducible VO2-based oscillators is presented, which is argued to be made possible thanks to the introduction of a hafnium oxide (HfO2) layer between the silicon substrate and the vanadium oxide layer. Up to seven nearly identical VO2-based devices are contacted simultaneously to create a network of oscillators, paving the way for large-scale implementation of VO2 oscillating neural networks.

Analytical modelling of the transport in analog filamentary conductive-metal-oxide/HfOx ReRAM devices.

The recent co-optimization of memristive technologies and programming algorithms enabled neural networks training with in-memory computing systems. In this context, novel analog filamentary conductive-metal-oxide (CMO)/HfOx redox-based resistive switching memory (ReRAM) represents a key technology. Despite device performance enhancements reported in literature, the underlying mechanism behind resistive switching is not fully understood. This work presents the first physics-based analytical model of the current transport and of the resistive switching in these devices. As a case study, analog TaOx/HfOx ReRAM devices are considered. The current transport is explained by a trap-to-trap tunneling process, and the resistive switching by a modulation of the defect density within the sub-band of the TaOx that behaves as electric field and temperature confinement layer. The local temperature and electric field distributions are derived from the solution of the electric and heat transport equations in a 3D finite element ReRAM model. The intermediate resistive states are described as a gradual modulation of the TaOx defect density, which results in a variation of its electrical conductivity. The drift-dynamics of ions during the resistive switching is analytically described, allowing the estimation of defect migration energies in the TaOx layer. Moreover, the role of the electro-thermal properties of the CMO layer is unveiled. The proposed analytical model accurately describes the experimental switching characteristic of analog TaOx/HfOx ReRAM devices, increasing the physical understanding and providing the equations necessary for circuit simulations incorporating this technology.

Analog Resistive Switching Devices for Training Deep Neural Networks with the Novel Tiki-Taka Algorithm.

A critical bottleneck for the training of large neural networks (NNs) is communication with off-chip memory. A promising mitigation effort consists of integrating crossbar arrays of analogue memories in the Back-End-Of-Line, to store the NN parameters and efficiently perform the required synaptic operations. The "Tiki-Taka" algorithm was developed to facilitate NN training in the presence of device nonidealities. However, so far, a resistive switching device exhibiting all the fundamental Tiki-Taka requirements, which are many programmable states, a centered symmetry point, and low programming noise, was not yet demonstrated. Here, a complementary metal-oxide semiconductor (CMOS)-compatible resistive random access memory (RRAM), showing more than 30 programmable states with low noise and a symmetry point with only 5% skew from the center, is presented for the first time. These results enable generalization of Tiki-Taka training from small fully connected networks to larger long-/short-term-memory types of NN.

A multi-timescale synaptic weight based on ferroelectric hafnium zirconium oxide.

Brain-inspired computing emerged as a forefront technology to harness the growing amount of data generated in an increasingly connected society. The complex dynamics involving short- and long-term memory are key to the undisputed performance of biological neural networks. Here, we report on sub-µm-sized artificial synaptic weights exploiting a combination of a ferroelectric space charge effect and oxidation state modulation in the oxide channel of a ferroelectric field effect transistor. They lead to a quasi-continuous resistance tuning of the synapse by a factor of 60 and a fine-grained weight update of more than 200 resistance values. We leverage a fast, saturating ferroelectric effect and a slow, ionic drift and diffusion process to engineer a multi-timescale artificial synapse. Our device demonstrates an endurance of more than 10 10 cycles, a ferroelectric retention of more than 10 years, and various types of volatility behavior on distinct timescales, making it well suited for neuromorphic and cognitive computing.

Structural Assessment of Interfaces in Projected Phase-Change Memory.

Non-volatile memories based on phase-change materials have gained ground for applications in analog in-memory computing. Nonetheless, non-idealities inherent to the material result in device resistance variations that impair the achievable numerical precision. Projected-type phase-change memory devices reduce these non-idealities. In a projected phase-change memory, the phase-change storage mechanism is decoupled from the information retrieval process by using projection of the phase-change material's phase configuration onto a projection liner. It has been suggested that the interface resistance between the phase-change material and the projection liner is an important parameter that dictates the efficacy of the projection. In this work, we establish a metrology framework to assess and understand the relevant structural properties of the interfaces in thin films contained in projected memory devices. Using X-ray reflectivity, X-ray diffraction and transmission electron microscopy, we investigate the quality of the interfaces and the layers' properties. Using demonstrator examples of Sb and Sb2Te3 phase-change materials, new deposition routes as well as stack designs are proposed to enhance the phase-change material to a projection-liner interface and the robustness of material stacks in the devices.

State dependence and temporal evolution of resistance in projected phase change memory.

Phase change memory (PCM) is being actively explored for in-memory computing and neuromorphic systems. The ability of a PCM device to store a continuum of resistance values can be exploited to realize arithmetic operations such as matrix-vector multiplications or to realize the synaptic efficacy in neural networks. However, the resistance variations arising from structural relaxation, 1/f noise, and changes in ambient temperature pose a key challenge. The recently proposed projected PCM concept helps to mitigate these resistance variations by decoupling the physical mechanism of resistance storage from the information-retrieval process. Even though the device concept has been proven successfully, a comprehensive understanding of the device behavior is still lacking. Here, we develop a device model that captures two key attributes, namely, resistance drift and the state dependence of resistance. The former refers to the temporal evolution of resistance, while the latter refers to the dependence of the device resistance on the phase configuration of the phase change material. The study provides significant insights into the role of interfacial resistance in these devices. The model is experimentally validated on projected PCM devices based on antimony and a metal nitride fabricated in a lateral device geometry and is also used to provide guidelines for material selection and device engineering.

Back-End, CMOS-Compatible Ferroelectric Field-Effect Transistor for Synaptic Weights.

Neuromorphic computing architectures enable the dense colocation of memory and processing elements within a single circuit. This colocation removes the communication bottleneck of transferring data between separate memory and computing units as in standard von Neuman architectures for data-critical applications including machine learning. The essential building blocks of neuromorphic systems are nonvolatile synaptic elements such as memristors. Key memristor properties include a suitable nonvolatile resistance range, continuous linear resistance modulation, and symmetric switching. In this work, we demonstrate voltage-controlled, symmetric and analog potentiation and depression of a ferroelectric Hf0.57Zr0.43O2 (HZO) field-effect transistor (FeFET) with good linearity. Our FeFET operates with low writing energy (fJ) and fast programming time (40 ns). Retention measurements have been performed over 4 bit depth with low noise (1%) in the tungsten oxide (WOx) readout channel. By adjusting the channel thickness from 15 to 8 nm, the on/off ratio of the FeFET can be engineered from 1 to 200% with an on-resistance ideally >100 kΩ, depending on the channel geometry. The device concept is using earth-abundant materials and is compatible with a back end of line (BEOL) integration into complementary metal-oxide-semiconductor (CMOS) processes. It has therefore a great potential for the fabrication of high-density, large-scale integrated arrays of artificial analog synapses.

Electrical and optical properties of epitaxial binary and ternary GeTe-Sb2Te3 alloys.

Phase change materials such as pseudobinary GeTe-Sb2Te3 (GST) alloys are an essential part of existing and emerging technologies. Here, we investigate the electrical and optical properties of epitaxial phase change materials: α-GeTe, Ge2Sb2Te5 (GST225), and Sb2Te3. Temperature-dependent Hall measurements reveal a reduction of the hole concentration with increasing temperature in Sb2Te3 that is attributed to lattice expansion, resulting in a non-linear increase of the resistivity that is also observed in GST225. Fourier transform infrared spectroscopy at room temperature demonstrates the presence of electronic states within the energy gap for α-GeTe and GST225. We conclude that these electronic states are due to vacancy clusters inside these two materials. The obtained results shed new light on the fundamental properties of phase change materials such as the high dielectric constant and persistent photoconductivity and have the potential to be included in device simulations.

Chemical and structural arrangement of the trigonal phase in GeSbTe thin films.

The thermal and electrical properties of phase change materials, mainly GeSbTe alloys, in the crystalline state strongly depend on their phase and on the associated degree of order. The switching of Ge atoms in superlattice structures with trigonal phase has been recently proposed to develop memories with reduced switching energy, in which two differently ordered crystalline phases are the logic states. A detailed knowledge of the stacking plane sequence, of the local composition and of the vacancy distribution is therefore crucial in order to understand the underlying mechanism of phase transformations in the crystalline state and to evaluate the retention properties. This information is provided, as reported in this paper, by scanning transmission electron microscopy analysis of polycrystalline and epitaxial Ge2Sb2Te5 thin samples, using the Z-contrast high-angle annular dark field method. Electron diffraction clearly confirms the presence of compositional mixing with stacking blocks of 11, 9 or 7 planes corresponding to Ge3Sb2Te6, Ge2Sb2Te5, and GeSb2Te4, alloys respectively in the same trigonal phase. By increasing the degree of order (according to the annealing temperature, the growth condition, etc) the spread in the statistical distribution of the blocks reduces and the distribution of the atoms in the cation planes also changes from a homogenous Ge/Sb mixing towards a Sb-enrichment in the planes closest to the van der Waals gaps. Therefore we show that the trigonal phase of Ge2Sb2Te5, the most studied chalcogenide for phase-change memories, is actually obtained in different configurations depending on the distribution of the stacking blocks (7-9-11 planes) and on the atomic occupation (Ge/Sb) at the cation planes. These results give an insight in the factors determining the stability of the trigonal phase and suggest a dynamic path evolution that could have a key role in the switching mechanism of interfacial phase change memories and in their data retention.

Metal-Insulator Transition Driven by Vacancy Ordering in GeSbTe Phase Change Materials.

Phase Change Materials (PCMs) are unique compounds employed in non-volatile random access memory thanks to the rapid and reversible transformation between the amorphous and crystalline state that display large differences in electrical and optical properties. In addition to the amorphous-to-crystalline transition, experimental results on polycrystalline GeSbTe alloys (GST) films evidenced a Metal-Insulator Transition (MIT) attributed to disorder in the crystalline phase. Here we report on a fundamental advance in the fabrication of GST with out-of-plane stacking of ordered vacancy layers by means of three distinct methods: Molecular Beam Epitaxy, thermal annealing and application of femtosecond laser pulses. We assess the degree of vacancy ordering and explicitly correlate it with the MIT. We further tune the ordering in a controlled fashion attaining a large range of resistivity. Employing ordered GST might allow the realization of cells with larger programming windows.

Revisiting the Local Structure in Ge-Sb-Te based Chalcogenide Superlattices.

The technological success of phase-change materials in the field of data storage and functional systems stems from their distinctive electronic and structural peculiarities on the nanoscale. Recently, superlattice structures have been demonstrated to dramatically improve the optical and electrical performances of these chalcogenide based phase-change materials. In this perspective, unravelling the atomistic structure that originates the improvements in switching time and switching energy is paramount in order to design nanoscale structures with even enhanced functional properties. This study reveals a high- resolution atomistic insight of the [GeTe/Sb2Te3] interfacial structure by means of Extended X-Ray Absorption Fine Structure spectroscopy and Transmission Electron Microscopy. Based on our results we propose a consistent novel structure for this kind of chalcogenide superlattices.

Sub-nanometre resolution of atomic motion during electronic excitation in phase-change materials.

Phase-change materials based on Ge-Sb-Te alloys are widely used in industrial applications such as nonvolatile memories, but reaction pathways for crystalline-to-amorphous phase-change on picosecond timescales remain unknown. Femtosecond laser excitation and an ultrashort x-ray probe is used to show the temporal separation of electronic and thermal effects in a long-lived (>100 ps) transient metastable state of Ge2Sb2Te5 with muted interatomic interaction induced by a weakening of resonant bonding. Due to a specific electronic state, the lattice undergoes a reversible nondestructive modification over a nanoscale region, remaining cold for 4 ps. An independent time-resolved x-ray absorption fine structure experiment confirms the existence of an intermediate state with disordered bonds. This newly unveiled effect allows the utilization of non-thermal ultra-fast pathways enabling artificial manipulation of the switching process, ultimately leading to a redefined speed limit, and improved energy efficiency and reliability of phase-change memory technologies.

Surface reconstruction-induced coincidence lattice formation between two-dimensionally bonded materials and a three-dimensionally bonded substrate.

Sb2Te3 films are used for studying the epitaxial registry between two-dimensionally bonded (2D) materials and three-dimensional bonded (3D) substrates. In contrast to the growth of 3D materials, it is found that the formation of coincidence lattices between Sb2Te3 and Si(111) depends on the geometry and dangling bonds of the reconstructed substrate surface. Furthermore, we show that the epitaxial registry can be influenced by controlling the Si(111) surface reconstruction and confirm the results for ultrathin films.