Memtransistors Based on Nanopatterned Graphene Ferroelectric Field-Effect Transistors (original) (raw)
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Molecular ferroelectric/semiconductor interfacial memristors for artificial synapses
npj Flexible Electronics, 2022
With the burgeoning developments in artificial intelligence, hardware implementation of artificial neural network is also gaining pace. In this pursuit, ferroelectric devices (i.e., tunneling junctions and transistors) with voltage thresholds were recently proposed as suitable candidates. However, their development is hindered by the inherent integration issues of inorganic ferroelectrics, as well as poor properties of conventional organic ferroelectrics. In contrast to the conventional ferroelectric synapses, here we demonstrated a two-terminal ferroelectric synaptic device using a molecular ferroelectric (MF)/semiconductor interface. The interfacial resistance can be tuned via the polarization-controlled blocking effect of the semiconductor, owing to the high ferroelectricity and field amplification effect of the MF. Typical synaptic features including spike timing-dependent plasticity are substantiated. The introduction of the semiconductor also enables the attributes of optoelec...
Graphene/Ferroelectric (Ge-Doped HfO2) Adaptable Transistors Acting as Reconfigurable Logic Gates
Nanomaterials
We present an array of 225 field-effect transistors (FETs), where each of them has a graphene monolayer channel grown on a 3-layer deposited stack of 22 nm control HfO2/5 nm Ge-HfO2 intermediate layer/8 nm tunnel HfO2/p-Si substrate. The intermediate layer is ferroelectric and acts as a floating gate. All transistors have two top gates, while the p-Si substrate is acting as a back gate. We show that these FETs are acting memtransistors, working as two-input reconfigurable logic gates with memory, the type of the logic gate depending only on the values of the applied gate voltages and the choice of a threshold current.
Energy-Efficient Organic Ferroelectric Tunnel Junction Memristors for Neuromorphic Computing
Advanced Electronic Materials, 2019
adaptive junctions between neurons, alter their weight depending on the magnitude, duration, number, and time delay between signal pulses. [6] An increase of synaptic weight is termed potentiation while its decrease is known as depression. Memory and cognitive activity in the brain ensue via excitatory or inhibitory postsynaptic currents (EPSC/IPSC) leading to shortterm and long-term potentiation (STP/ LTP), paired-pulsed facilitation (PPF) and depression (PPD), and spike-timingdependent plasticity (STDP). Complementary metal-oxide-semiconductor (CMOS)-based neuromorphic devices consist of a network of transistors and capacitors. [7] While their performance has surged dramatically over the years, constant off-chip communication between separate memory and processor units, commonly known as the von Neumann bottleneck, consumes a lot of power and limits the computation speed. Memristors with integrated on-chip memory and computing capabilities provide an attractive energy-efficient alternative. [4] Various types of memristors, including insulating oxides with formed filaments, [1,8-12] phase-change memories, [13,14] electrochemical devices, [15-17] conducting polymers, [18,19] and oxide-based ferroelectric tunnel junctions (FTJs), [20-24] have been investigated and exploited as artificial synapse. In an FTJ, gradual reversal of ferroelectric polarization inside a thin tunnel barrier and ensuing changes of the potential barrier caused by electrostatic charge screening produce a memristor response. [20,24] Since the tunneling current is typically small, the emulation of synaptic functions by an FTJ requires only limited energy. Despite its promise
Combining graphene and organic ferroelectric for possible memory devices
Both ferroelectric materials and graphene attract plenty of scientific attention. Ferroelectrics are well known for their ability to maintain a polarization, which can be switched/reversed by an external electric field. Organic ferroelectrics (e.g. PVDF/TrFE) are of special interest because of their flexibility and durability. Graphene has already demonstrated its promise for future electronics. The two materials brought together give a new functionality of non-volatile memory. Proof-of-concept works have been already done, but only with exfoliated graphene. The main goal of this research is to study the possibility of making such devices using CVD graphene, in large amounts. In other words, we address the feasibility of this kind of graphene-based memory devices. This can be important for graphene-based electronics in the near future.
Graphene memristors based on humidity-mediated reduction of graphene oxide
Journal of Materials Chemistry C, 2023
Memristors have emerged as promising devices for neuromorphic applications, particularly as synaptic weight. Graphene oxide, a partially oxidised and electrically insulating form of graphene, has been employed in metal/insulator/metal devices, where resistance switching based on the filamentary growth of the contacting metals has been demonstrated. Here we demonstrate an alternative highly reproducible resistance switching mechanism based on solid-state reduction of GO thin-films mediated by adsorbed water. It is shown that distinguishable and highly stable resistance states can be controllably realised in graphene oxide metal/insulator/metal devices. We have unravelled the growth mechanism and determined the growth kinetic of reduced graphene oxide, which enables a deterministic way to tune the resistance in GO devices. The demonstration of highly reproducible memristors based on graphene oxide crossbar devices is very promising for the realisation of low-cost and environmentally benign solution-processable neuromorphic synaptic weight.
ACS applied materials & interfaces, 2018
Brain-inspired computing is an emerging field, which intends to extend the capabilities of information technology beyond digital logic. The progress of the field relies on artificial synaptic devices as the building block for brainlike computing systems. Here, we report an electronic synapse based on a ferroelectric tunnel memristor, where its synaptic plasticity learning property can be controlled by nanoscale interface engineering. The effect of the interface engineering on the device performance was studied. Different memristor interfaces lead to an opposite virgin resistance state of the devices. More importantly, nanoscale interface engineering could tune the intrinsic band alignment of the ferroelectric/metal-semiconductor heterostructure over a large range of 1.28 eV, which eventually results in different memristive and spike-timing-dependent plasticity (STDP) properties of the devices. Bidirectional and unidirectional gradual resistance modulation of the devices could theref...
Advances in Memristor Neural Networks - Modeling and Applications
2018
The unique electronic and optical properties of newly discovered 2D crystals such as graphene, graphene oxide, molybdenum disulfide, and so on demonstrate the tremendous potential in creating ultrahigh-density nano-and bioelectronics for innovative image recognition systems, storage and processing of big data. A new type of memristors with a floating photogate based on biocompatible graphene and other 2D crystals with extremely low power consumption and footprint is considered. The photocatalytic oxidation of graphene is proposed as an effective method of creating synapse-like 2D memristive devices with photoresistive switching for nonvolatile electronic memory of ultrahigh density. Particular attention is paid to the new concept of the formation of self-assembled nanoscale memristive elements interfacing artificial electronic neural networks. 2D photomemristors with a floating photogate exhibit multiple states controlled in a wide range of electromagnetic radiation and can be used for neuromorphic computations, pattern recognition and image processing needed to create artificial intelligence.
Electronic synapses with near-linear weight update using MoS2/graphene memristors
Applied Physics Letters
Emulating the human brain's circuitry composed of neurons and synapses is an emerging area of research on mitigating the "von Neumann bottleneck" in present computer architectures. The building block of these neuromorphic systemsthe synapse is commonly realized with oxide-based or phase change material-based devices, whose operation is limited by high programming currents and high reset currents. In this work, we have realized non-volatile resistive switching MoS2/graphene devices that exhibit multiple conductance states at low operating currents. The MoS2/graphene devices