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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.

Materials Challenges for Memory

Investigation of memristive behaviour in defect engineered WSe2 memtransistors

memristive devices

defect-engineering

two-dimensional materials

memtransistors

After two years of virtual conferences, we're excited to host EMC 2022 in person at The Ohio State University in Columbus, Ohio. In addition to cutting-edge technical presentations given in multiple parallel sessions, opportunities to network with colleagues will be present throughout the conference. The size and format of the meeting makes networking easy, whether you have been coming to the conference for many years or are a first-time attendee.

EMC is coordinated with the Device Research Conference (DRC), which will be held the same week, from June 26-29. This recognizes the strong interaction between device and electronic materials research and provides fruitful exchange of information between attendees of both Conferences.

For information on the 80th Device Research Conference, please visit the DRC 2022 website.



EMC 2022

The Electronic Materials Conference (EMC) is the premier annual forum on the preparation, characterization and use of electronic materials. This year’s event will feature a plenary session, topical sessions, a poster session and more.

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

What does gender got to do with science

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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