United States

Artificial neural networks employing the biomimetic functions of memristors and crossbar array architecture have been actively explored and designed for use in energy‐efficient neuromorphic computing. In particular, a memristor with a crossbar array architecture is more efficiently able to perform vector-matrix multiplication compared to conventional CMOS hardware. In this process, a memristor with certain characteristics is required, such as ohmic behavior in all resistance states and symmetry/linearity in its long-term potentiation/depression behavior. In this study, to improve these characteristics, we proposed and demonstrated a Li-based composite memristor (LCM) that consists of three phases: Li-doped TiO2 as a Li reservoir, Li4Ti5O12 as the insulating phase, and Li7Ti5O12, as the metallic phase. In the LCM, resistance switching property was induced by adjusting the ratio of the metallic to the insulating phase. The phase formation and coexistence of TiO2, Li4Ti5O12, and Li7Ti5O12 were confirmed using X-ray diffraction patterns and Raman spectral analysis. The proposed LCM exhibited symmetric and gradual switching behavior for both set and reset switching in a full I-V sweep because the driving force leading to the increase/decrease in the ratio of the metallic phase inside the thin film during set and reset switching was the same. The proposed LCM was thus demonstrated to be promising for improving both the ohmic behavior of synapse devices and the linearity and symmetry of long-term potentiation/depression behavior required for artificial neural networks. 
Acknowledgments: This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (2019R1F1A1059637) and the Ministry of Trade, Industry &amp; Energy (MOTIE, Korea) under the Industrial Strategic Technology Development Program. No. 10068075, &#39;Development of Mott-transition-based forming-less non-volatile resistive switching memory &amp; array&#39;.


Materials Challenges for Memory

Li-Based Composite Memristor for Artificial Neural Networks

lithium titanate

phase transition

composite memristor

lithium-based memristor

artificial neural networks

**BRACIS** originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – **SBIA** (22 editions), and the Brazilian Symposium on Neural Networks – **SBRN** (13 editions). **ENIAC** is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. **STIL** is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: **CE-IA**, **CE-IC** and **CE-PLN**.

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BRACIS 2021

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – SBIA (22 editions), and the Brazilian Symposium on Neural Networks – SBRN (13 editions). 
ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. 
STIL is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: CE-IA, CE-IC and CE-PLN

Artificial neural networks employing the biomimetic functions of memristors and crossbar array architecture have been actively explored and designed for use in energy‐efficient neuromorphic computing. In particular, a memristor with a crossbar array architecture is more efficiently able to perform vector-matrix multiplication compared to conventional CMOS hardware. In this process, a memristor with certain characteristics is required, such as ohmic behavior in all resistance states and symmetry/linearity in its long-term potentiation/depression behavior. In this study, to improve these characteristics, we proposed and demonstrated a Li-based composite memristor (LCM) that consists of three phases: Li-doped TiO2 as a Li reservoir, Li4Ti5O12 as the insulating phase, and Li7Ti5O12, as the metallic phase. In the LCM, resistance switching property was induced by adjusting the ratio of the metallic to the insulating phase. The phase formation and coexistence of TiO2, Li4Ti5O12, and Li7Ti5O12 were confirmed using X-ray diffraction patterns and Raman spectral analysis. The proposed LCM exhibited symmetric and gradual switching behavior for both set and reset switching in a full I-V sweep because the driving force leading to the increase/decrease in the ratio of the metallic phase inside the thin film during set and reset switching was the same. The proposed LCM was thus demonstrated to be promising for improving both the ohmic behavior of synapse devices and the linearity and symmetry of long-term potentiation/depression behavior required for artificial neural networks. 
Acknowledgments: This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (2019R1F1A1059637) and the Ministry of Trade, Industry & Energy (MOTIE, Korea) under the Industrial Strategic Technology Development Program. No. 10068075, 'Development of Mott-transition-based forming-less non-volatile resistive switching memory & array'.


poster


![](https://assets.underline.io/uploads/markdown_image/1/image/2e10c515564e9ec3287ee4c664011c20.png)

The energy cost of storing and moving data is enormous, and is growing at such a rapid pace that within a few years the power demand may overwhelm some developed nations. Non-volatile memory (NVM) and neuromorphic computing are enabling critical advances in this area, but there are many scientific and technological challenges. This conference will cover the materials challenges of different memory technologies, from the fundamental processes and mechanisms, to industry perspectives on materials specifications and manufacturing.

Electrochemical devices allowing to modulate their resistive state through ionic motion upon electric bias are known as memristors. Currently, memristors are positioned as a major candidate to overcome the bottlenecks in current electronic-based transistors in terms of downscaling capabilities and energy consumption. The vast majority of memristor are based on two types of ions: either oxygen vacancy migration, in the so-called Valence Change Memories (VCM), or a metal cation, usually Ag+ and Cu2+, in the so-called Electrochemical Metallization Cells (ECM). Despite the excellent performance of both systems, a widespread implementation of oxygen-based memristors in today's integrated circuits is delayed due to the need to address cycle-to-cycle and device-to-device variabilities while circumventing electroforming, which are inherent issues associated to the filamentary nature of the switching mechanism. Recently, Li-ionic memories are emerging as an alternative, given the higher diffusivity of Li+ when compared to oxygen, and the ability of Li-oxides to accumulate and deplete lithium at the interfaces and bulk. The scarce literature regarding Li-based memristors focuses mainly on well-known high-voltage cathode battery materials like LixCoO2, rather sluggish conductor as LiLaTiO3or magnetic spinels like LiFe5O8.1,2 Performance-wise, Li-based oxides have shown promising applications in neuromorphic computing thanks to a close-to-analog switching response. Despite their promise, it remains unclear how to design the defect chemistry and control the switching behavior of Li-based materials towards their performances. For instance, the material class of Lithium titanate compounds present intrinsic properties that could be beneficial to implement for a Li-operated memristor: a) the existence of a metal-insulator transition upon lithium intercalation, concomitant with a phase transition and valence change in the Ti cation, b) the spinel to rock-salt phase transition occurs with a neglectable volume change and c) there is a large difference in electronic conductivity and diffusivity between the two phases. In this work,3 we first discuss the iono-electronic structure requirements to define Li-oxides and select their chemistries for resistive switching applications and neuromorphic computing. Second, we exemplify the resistive switching capabilities of these systems for different lithium stoichiometries and discuss current challenges and tricks in processing to make these. Third, we conceptually discuss how the Li-stroichiometry can be used as a knob to alter switching performance to be either linear or non-linear for neural computation, and from a material perspective how models for that can be thought of. To exemplify this, we will report most recent results on non-volatile, and non-filamentary bipolar resistive switching characteristics of lithium titanates compounds, Li4+3xTi5O12, that can be operated either for deep or synaptic neural networks applications. Here, we employed a recently proposed strategy to overcome lithium loss during thin film deposition and finely control the final lithiation degree of the films4 to create a stoichiometrically lithiated Li4Ti5O12 spinel phase and a highly lithiated Li7Ti5O12 rock- salt phase memristive devices. Changing the Li-content from a stoichiometrically lithiated spinel phase to a highly lithiated rock-salt phase results in the capability to tune the performance in a wide range in terms of accessible resistance window (from ratios of 102 to 106 at low voltage operation, respectively), switching symmetry (from highly asymmetric to symmetric behavior, respectively) and retention (from few minutes up to 105 s at room temperature, respectively), among others. In other words, controlling the lithiation degree might offer a suitable path to reduce stochasticity from which current filamentary memristive devices inherently suffer, mainly due to the difficulties to control the amount of vacancies generated, and paves a way to further control of ionic migration for novel iono-electronic devices in neuromorphic computation.

Neuromorphic Computing with Li-based Battery Materials

The generation, detection, and manipulation of pure spin currents have attracted considerable attention in the field of spintronics owing to their rich physical phenomena and potential applications [1-2]. The most convenient method for generating and detecting the pure spin currents is to utilize the spin Hall magnetoresistance (SMR) [3-4]. In this letter, a novel structure consisting of two heavy metals that delivers competing spin currents of opposite spin indices is presented. We studied spin Hall effect in Ta-Pt alloys with various Ta composition for Ta/Pt/Ni80Fe20/ MgO (001) heterostructures by means of SMR. To determine the relative contributions of Pt and Ta, we measured a series of samples of Ta（tTa=0-3nm）/Pt（3nm）/Ni80Fe20/MgO (001) with a constant Pt (3) layer and various thicknesses of the Ta layer. Low resistivity, flat, and highly (111)-textured Pt-Ta alloy films are synthesized. It is found the SMR ratio is enhanced with a Ta capping layer at an optimum composition of x between 0.5nm to 1nm. The spin accumulation enhancement at the Pt/Ni80Fe20 interface after capping with a Ta layer, which functions as a spin sink at small thickness, is responsible for the SMR ratio enhancement, even though Ta and Pt have opposite spin Hall angels. Our results not only demonstrate a viable scheme for multiple spin currents, but also expand a new avenue for switching a perpendicular ferromagnetic layer by competing spin currents.

Enhancement of the spin Hall magnetoresistance in Ta/Pt/Ni80Fe20/MgO(100) heterostructures by competing spin currents

Neuromorphic computing architectures have superior properties over conventional computers in solving a number of problems such as image recognition, combinatorial optimization or prediction. As a concept, neural networks have been proven to be fast and flexible. However, their implementation in conventional digital architecture requires large amount of resources, as subsequent multiplication and addition operations need to be performed for each input of a single neuron. We present a complete architecture for neuromorphic computing including: a magnetic tunel junction (MTJ) based multi-bit cell forming a programmable synaptic weight, an electronic neuron and the artificial neural network itself. We demonstrate a successful operation of such network by presenting simulation results of a representative problem solution.


Neuromorphic computing architecture based on serially connected magnetic tunnel junctions

Doping with La is a promising route to improve endurance of ferroelectric Hf0.5Zr0.5O2 film for non-volatile random access memories [1-2]. However, the beneficial effect of La on the endurance of polycrystalline films may be accompanied by degradation of the retention [3]. Here, we have investigated whether this endurance - retention dilemma also occurs in La-doped epitaxial Hf0.5Zr0.5O2 films.
Epitaxial 1% La-doped Hf0.5Zr0.5O2 films of thickness in the 4.5 - 13 nm range were deposited by pulsed laser deposition on SrTiO3(001) (STO) and STO/Si(001) substrates, buffered with a La2/3Sr1/3MnO3 electrode. The films structure was studied by X-ray diffraction (XRD) and surface topography was studied using atomic force microscopy. Top Pt electrodes were deposited on top of the films to characterize their ferroelectric properties (polarization, leakage, endurance and retention).
Compared to undoped epitaxial Hf0.5Zr0.5O2 films [4-5], large values of polarization are obtained in a wider thickness range, whereas the coercive fields are similar, and the leakage current is substantially reduced. Compared to polycrystalline La-doped films [2-3], wake-up effect of epitaxial La-doped films is limited to a few cycles. Despite fatigue, endurance in epitaxial La-doped films is more than 1010 cycles, and this good property is accompanied by excellent retention of more than 10 years at room temperature. These results demonstrate that the wake-up effect and endurance–retention dilemma are not intrinsic in La-doped Hf0.5Zr0.5O2.

Epitaxial La-Doped Hf0.5Zr0.5O2 Thin Films with Enhanced Ferroelectric Properties

As the promising candidate of artificial synapses, memristor has attracted more and more interest in the future neuromorphic networks. Compared to the traditional Si-based devices, the flexible memristors exhibit light-weight, wearable, and cheap advantages. Inorganic compounds have excellent electrical performance but suffer from weak flex characteristics, and it can’t bear large bent degrees. The organic-inorganic hybrid materials combine flexible properties with outstanding electrical performance, so it is a favorable choice for the flexible memristor materials. Here, some essential synaptic functions are demonstrated in the Al-based hydroquinone (HQ) organic-inorganic hybrid film memristor on ITO-coated flexible PET substrate. Setting different set compliance currents and reset voltages, multi-level resistances are achieved at a steady state with little fluctuation. Through the pulse stimulation, paired-pulse facilitation, short-term to long-term plasticity transition and spike-timing-dependent plasticity (STDP) are successfully emulated. After 10000 times bent test (curvature radius ~4.5 mm), the electrical performance also exhibits stable RHRS/RLRS ratio ~1000, and essential synaptic functions emulation without serious failure resistive switching. The flexible memristor is potential to apply in the wearable electronics and smart artificial skins.


High-performance Flexible Memristor Based on Organic-Inorganic Hybrid Thin Film Derived from Molecular Layer Deposition for Artificial Synapse Device

The discovery of memristor, theorized in 1971 by L. Chua, has led to the development of novel artificial neuromorphic concepts and devices, including ferroelectric-based ones. Ferroelectric Tunnel Junction (FTJ) type memristors based on zirconium-doped hafnium oxide, Hf_0.5 Zr_0.5 O_2 (HZO) have recently displayed synaptic learning capabilities. In addition, HZO processes are already fully compatible with silicon CMOS industry with oxide layers thinner than 10 nm. In the present work, the HZO layer is realized by room temperature magnetron sputtering of a Hf_0.5 Zr_0.5 O_2 ceramic target and subsequently crystallized by rapid thermal annealing. Using different bottom electrode layers (germanium, titanium nitride, platinum) grown on silicon and different substrates (n-doped silicon, n-doped germanium), we studied the effect on the stabilized crystalline phase and microstructure (Fig), band structure alignment and electrical properties of thin HZO films. Furthermore, we explored the effect of ultra-thin buffer layers between the electrodes and the HZO layer, trading on the material, the position and the thickness. We exploited X-ray photoemission spectroscopy to analyze the chemistry and the electronic state of the electrodes/HZO interface. X-ray reflectometry and grazing incidence X-ray diffraction (GIXRD) were used to probe the thickness and structural characteristics of the HZO layer, whose ferroelectricity is associated to the polar orthorhombic phase. We will discuss our results in the framework of structural, chemical and physical properties of the different electrode/ferroelectric interfaces and their effect on the electrical properties of thin HZO-based junctions.

Influence of bottom electrodes on HZO thin film features

Resistive switching (RS) memories have been a crucial component for the requirements of the Internet of Things (IoT) which demands ultra-low power and high-density devices with new computing principles, exploiting low cost green products and technologies. Most of the reported resistive switching devices use conventional methods (PVD and CVD) which are quite expensive due to their up-scale production. Solution-processing methods have been improved, being now a reliable technology that offers many advantages for RS devices such as, high versatility, large area uniformity, transparency, low-cost and a simple fabrication of two-terminal structures. Solution-based metal oxide RS devices are emergent and promising non-volatile memories for future electronics. 
In this work, we review the present status of solution-based metal oxide RS devices is highlighted as well as discuss the solution synthesis parameters which induce a massive influence on the overall performance of these devices.[1] Next, a precise analysis of the most reported transition metal oxide thin films and electrode interface is presented. Then the recent advances on RS devices that will allow the massive production and compatibility with flexible substrates targeting low cost for neuromorphic and memory applications are discussed. Finally, the main challenges in solution-based metal oxide RS devices are discussed and future trends are proposed.

Recent Challenges and the Future of Metal Oxide Resistive Switching Devices

Memristors have been at the forefront of nanoelectronics research for the last decade, offering a valuable component to reconfigurable computing. 
Over this time period a number of publications have studied most of their aspects, generally centering around their static state-dependent programmability. 
In the present work, the behavior of metal-oxide memristors under a noninvasive
AC perturbation in a range of frequencies is studied. Metal-oxide memristors present a variable cut-off frequency when their state is switched. 
Also, they are found to behave as RC low-pass filters and thus can be used as reconfigurable filters. 
The usable reconfigurability window of the devices can be tailored to encompass specific frequency ranges by amending the devices’ capacitance, through engineering of its geometrical characteristics, such as area and thickness. Furthermore, by changing the active material of the device the behavior remains consistent, pointing to a unified behavior across metal-oxide memristors, irrespective of material used. This study provides useful insights for the eventual usage of devices in AC circuits and extends current knowledge by characterising their frequency dependent characteristics.

Frequency Response of Metal-Oxide Memristors

Electronic devices that can simulate the dynamics of neurotransmission in the human body are of great interest for the development of artificial intelligence in modern information technology. In this paper, an artificial nociceptor realized by a single metal-oxide nanobelt device with a unique capacitive-coupled threshold switching behavior is demonstrated. Via thermal admittance spectroscopy and temperature dependent sweeping study, the properties of the nanobelt devices are determined by Schottky emission at low bias and by defect assisted quantum tunneling at high bias subject to a threshold voltage. The low activation energy associated with dynamic electron trapping gives rise to a voltage-dependent volatile threshold switching behavior. This threshold switching behavior allows the emulation of several characteristic features of a nociceptor, a critical type of sensory neuron in the human body, including “threshold”, “relaxation”, “no adaptation”, “allodynia” and “hyperalgesia” behaviors, essential for the realization of electronic sensory receptors that detect noxious stimuli and signal rapid warning to the central nervous system. One-dimensional metal oxide nanobelt devices of this type yield multifunctional nociceptor performance that is fundamental for applications in artificial intelligence systems, representing a key step in the realization of neural integrated devices via a bottom-up approach.

Threshold Switching in Single Metal‐Oxide Nanobelt Devices Emulating an Artificial Nociceptor

One of the most desirable attributes of non-volatile memories and memristors is a fast and non-destructive readout of their resistive state. Prototypical ferroelectric memories use the bulk photovoltaic response associated to the polarization of ferroelectric films to address this requirement by optically sensing binary memory cells. A more advanced type of non-volatile memories are ferroelectric tunnel junctions. They feature resistive state ratios RHigh/RLow of 10^6, with an accessible continuum of resistive states, making them promising candidates for neuromorphic computing applications. However, tunnel junctions have ultrathin ferroelectric barriers in the range 3-6 nm that prevented a measurable photovoltaic response originated in the bulk of the barrier. We present the optical sensing of the resistive state of a ferroelectric tunnel junction using the Schottky barrier formed in the bottom interface to dramatically enhance its optical response. Our FTJ based on the La0.7Sr0.3MnO3/BaTiO3 (LSMO/BTO) interface, one of the best performing and studied material combinations, builds up an optically active Schottky barrier whose properties can be tuned by performing a resistive switch. By integrating a transparent conducting top electrode of ITO, we make the junctions optically accessible to light. UV illumination with 3.3 eV energy, exceeding the BTO bandgap, generates a solid photovoltaic response in the high resistance state originated in the Schottky barrier, with an open circuit voltage Voc of 0.4 V at 20 K. The photovoltaic effect is absent using lower energy red light (1.9 eV). In the RLow state, the Schottky barrier is removed and the photo response disappears, enabling the optical sensing of the resistive state.


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Li-Based Composite Memristor for Artificial Neural Networks

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