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A resistive switching ZnO homojunction diode fabricated with a stack of two dissimilar ZnO thin film shows promising characteristics necessary to address the crosstalk [1] problem in a cross-bar memory array. The device has a characteristic IV-hysteresis loop in forward bias, in conjunction with its pn junction characteristics. In the ac frequency range of 10 Hz to 15 kHz, the area inside the hysteresis loop initially increased, reaching a maximum at 1 kHz, and then decreases with increasing frequency. The maximum in the area of the hysteresis loop corresponds to the accumulation of space charge at the interface. The resistivity of the device was found to vary from 5 kΩ to 100 kΩ in the bias range 0V - 10V and in the range of 106 Ω when reverse biased the ratio of High Resistive State (HRS) to Low Resistive State (LRS) [2] has its maximum (~2.4) at 1 kHz ac signal at 3V.


Materials Challenges for Memory

Addressing crosstalk in cross-bar memory arrays with a resistive switching ZnO homojunction diode

hysteresis

homojunction

cross-bar memory arrays

crosstalk

resistive switch


![](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.


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For almost eight decades, the Device Research Conference (DRC) has brought together leading scientists, researchers and students to share their latest discoveries in device science, technology and modeling. Notably, many of the first public disclosures of key device technologies were made at the DRC. This year marks the 79th anniversary of the DRC—the longest running device research meeting in the world. As we commemorate this meeting, the high-caliber technical sessions will be highlighted by plenary talks and invited talks by international research pioneers and leaders behind modern electronic technology.

Given the ongoing global health concerns associated with COVID-19 (Coronavirus), the DRC leadership has decided to hold the 2021 Device Research Conference in a virtual (online) format. This follows an enormously successful 2020 DRC held virtually, in which we had record attendance and worldwide participation of more than 875 attendees from 33 countries.

The 2021 Conference will feature:

* Informative, timely short courses in rapidly developing fields
* Oral and poster presentations on electronic/photonic device experiments and simulations
* Plenary and invited presentations given by worldwide leaders
* Evening “rump” session
* Strong student participation and Student Paper Awards

DRC is held in coordination with the Electronic Materials Conference (EMC), which will occur the same week, from June 23-25. As has happened in person in past years, in 2021, the virtual DRC and EMC sessions on the shared day, Wednesday, June 23, will be open to all attendees from both conferences. This recognizes the strong interaction between device and electronic materials research and provides fruitful exchanges of information between attendees of both Conferences. For information on the 63rd Electronic Materials Conference, please visit the [EMC 2021 website.](https://www.mrs.org/63rd-emc/)

2021 DRC individual presentation videos will become available in Underline at the end of each technical session. A recording of each full live session with Q&A will be posted later during the conference. To facilitate re-viewing of recorded talks and sessions, the DRC Underline site will remain open through 11:59pm (US EDT) on July 7th. DRC Conference registration will remain open at www.mrs.org/drc-2021/registration through June 30th.

Thank you for joining us at the 2021 Device Research Conference!

**General Chair**
<br>Zhihong Chen, Purdue University

**Technical Program Chair**
<br>Becky (R.L.) Peterson, University of Michigan

**Technical Program Vice-Chair**
<br>Gregg Jessen, BAE Systems

You can register for the 79th Device Research Conference with the following link. https://www.mrs.org/drc-2021

DRC 2021

poster

Observing the domain structure of antiferromagnetic materials is notoriously difficult, and in the non-collinear antiferromagnet Mn3NiN eight arrangements of the local magnetic moments are possible in the unit cell. Yet for potential applications in antiferromagnetic spintronics it is essential to prepare known domain structures to control the properties of the devices. We grow Mn3NiN films 50nm thick on STO, pattern the films into Hall bars and cool below TN = 220K into the antiferromagnetic state. We then use a laser to generate a local in-plane heat gradient and detect the voltage generated due to the anomalous Nernst effect (ANE) - a manifestation of the Berry curvature of the material. From the sign of the voltage signal we can distinguish domains 2μm in diameter and can saturate the device to a single state using an applied magnetic field. We also show a form of heat assisted writing - by increasing the laser power and reversing the field we are able to write a domain with opposite sign of the ANE.

Using the anomalous Nernst effect to image domains in non collinear antiferromagnetic Mn3NiN thin films

Wake-up effect is a major issue for ferroelectric HfO2-based memory devices. Here, two TiN/Hf0.5Zr0.5O2/TiN structures deposited by magnetron sputtering on silicon are compared. The maximum remanant polarization is higher than 21µC/cm² for both samples, a strong difference is observed in the electrical behavior. For the mesa sample, the difference between maximal and initial Pr is only 3µC/cm² whereas it is around 14µC/cm² in the non-mesa case. We discuss the root causes of these behaviors in light of structural results. Various applications have been suggested for fluorite-structure ferroelectrics due to their advantages over the conventional perovskite-structure ferroelectrics. In this presentation we will focus on (Hf,Zr)O2 (HZO) thin films deposition for the capacitor of Ferroelectric Random Access Memories (FRAM) in the 1Transitor-1Capacitor (1T-1C) model. (Hf,Zr)O2 thin films are studied to either fully understand the stabilization of the ferroelectric phase (f-phase) or to fit with industrial requirements. Samples are stacks of Si/TiN/Hf0.5Zr0.5O/TiN/Pt. All materials are grown by sputtering at room temperature following by a rapid thermal annealing at 450°C during 30 seconds under N2 atmosphere. The samples are called NM, and M. NM and M refers to two different architectures, respectively non-mesa and mesa structures. The description and size of NM and M samples is given in figure 1 for each sample. We report the fabrication of two samples deposited by magnetron sputtering with excellent performances, quite similar to samples deposited by Atomic Layer Deposition. Pr values are among the highest for samples deposited by sputtering. Although the N-sample and NM-samples show very close Pr values, the two samples show completely different electrical behaviors. During cycling, the increase of Pr value for the NM-sample is more than an order of magnitude higher than the M-sample. It is accompanied by a decrease of the endurance which is two order of magnitude higher for the NM-sample than for the M-sample. As electrical behaviors are not the same, for low stress conditions M-sample has a higher Pr value during cycling whereas for high stress conditions NM-sample has a higher Pr value during cycling. As a matter of fact, it has been proven that maximum Pr values are more sensitive to stress conditions than the structures themselves. The origins of the different electrical behaviors come from the micro-crystalline structures of the two samples, according to GIXRD results. The crystallization takes place during the annealing step. During annealing, M-sample is built with a TiN TE fully covering the HZO layer whereas the TiN covers only partially the HZO layer in case of the NM-sample. It induces different stress states which lead to two different micro-crystalline patterning. The M-sample shows no monoclinic peak, whereas the NM-sample shows many monoclinic orientations. It can explain the huge reduction of the wake-up effect.

Reduction of HfZrO2 capacitor wake-up effect

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'.


Li-Based Composite Memristor for Artificial Neural Networks

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.

Addressing crosstalk in cross-bar memory arrays with a resistive switching ZnO homojunction diode

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Using the anomalous Nernst effect to image domains in non collinear antiferromagnetic Mn3NiN thin films

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