United States

In quest of ultra-nonlinear switching kinetics in ReRAM devices, field-accelerated ion transport in solids has attracted great interest as a potential source of non-linear response. We employed large-scale Molecular Dynamics simulations to explore the impact of external electric fields on the rates of ion transport in ceria. We found that the standard models cannot account for the non-linear high-field regime and instead propose a new analytical model which excellently predicts the behaviour at all field strengths. Focussing on the interplay between electric fields and grain boundaries, we explore the tunability of grain-boundary resistances through applying large electric fields.

Materials Challenges for Memory

Tuning grain-boundary resistances for ReRAM

molecular dynamics

ion transport

electric fields

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

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.

Rational design of redoxed-based memristive devices for novel computing paradigm

Ferroelectric control of Rashba states: towards non-volatile spintronics driven by ferroelectricity

Materials to memory: why and how

Analog memory: materials, devices, applications

An opportunity for magnetoelectrics in complex oxides: designing heterostructures at the atomic scale

Smart 2D materials: the new frontier

Memtransistors, an emerging type of device where the electrostatically coupled gate terminal governs the memristive charge transport, are potential candidates for applications revolving in-memory computing and brain-like computing [1-3]. However, the accurate determination of memory states and understanding of device physics across all operational regimes remains a challenge with existing models [1,4]. Here, a physics based model incorporating selenium vacancy dynamics, device self-heating with both electron and hole conduction was used to study defect-engineered ambipolar WSe2 memtransistors. This novel approach of modelling memtransistors has revealed that changes in channel electrostatics due selenium vacancy migrations lead to threshold voltage shifts which result in memristive behaviour. We show that a combination of 2D electrostatics and electron screening, induced changes in lateral electric field distribution, which results in gate voltage dependent memristive behaviour. These findings provide keen insights into control of changes in conductance states for all operational regimes, which would lead to more accurate predictions in development of memtransistors for various future memory technologies.

Investigation of memristive behaviour in defect engineered WSe2 memtransistors

Human brain-inspired neuromorphic computing (NC), which emulates neural activities, is considered for the low-power implementation of artificial intelligence. The realization of hardware elements, such as synaptic devices, is an essential path towards this goal. Electrolyte gating has been widely used for conductance modulation by massive carrier injections at low voltage and has been proven to be an effective way of emulating synaptic functions. Synaptic devices, in the form of synaptic transistors, have been studied using a wide variety of materials like oxides [1], organic materials [2], and 2D materials [3]. However, studies on metal-based ferromagnetic synaptic transistors remain unexplored. Additionally, a synaptic device based on magnetic metal has a spin degree of freedom that can be tuned along with. Here, we have demonstrated a three-terminal cobalt-based synaptic transistor by electrolyte gating at room temperature, which is the first kind of such studies. The working principle of the transistor is based on the formation of an electric double layer (EDL) at the interface of ionic liquid and channel and by intercalation/ extraction of oxygen ions. 
We have achieved gating controlled non-volatile, multilevel conducting states for analog computing. The device could successfully emulate essential synaptic functions related to short-term plasticity and long-term plasticity. Moreover, a transition from short-term memory to long-term memory could be achieved by tuning gate pulse amplitude, duration, and number. The crucial cognitive functions of learning, forgetting, re-learning, and the re-forgetting process were emulated. Along with memory and learning, the device showed dynamic filtering behavior as well. These results provide an insight into the potential application of ferromagnetic metal-based synaptic transistor for large-scale, low power consuming, silicon compatible neuromorphic computing network.

Cobalt Based Synaptic Transistor by Electric Double Layer Formation for Neuromorphic Computing.

Ferroelectric switching of spin-to-charge conversion in GeTe

Resistive random access memory (ReRAM) is touted to replace Silicon based flash memory due to its low operating voltage, fast access speeds and the potential to scale down to nm range for ultra-high density storage. In addition, its ability to retain multi level resistance states makes it suitable for neuromorphic computing application. Here, we develop a cationic ReRAM with a sputtered MgO as the insulating layer. The Ag/MgO/Au ReRAM stack reveals a strong dependence of its resistive switching properties on the sputtering conditions of MgO. Due to the highly stable sputtered MgO, repeatable resistive switching memory is achieved with a low ON voltage of ~0.7V and memory window of ~ 1x 105. Limiting Ag diffusion through a top electrode modified W/Ag/MgO/Au stack significantly reduces the abruptness of resistive switching, thereby demonstrating analog switching capability. This phenomenon is evident in the improved linearity and symmetry of potentiation and depression weight modulation pulses, demonstrating ideal Hebbian synaptic learning rules.

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