Donor‐Induced Performance Tuning of Amorphous SrTiO<sub>3</sub> Memristive Nanodevices: Multistate Resistive Switching and Mechanical Tunability

Han

Adv Materials Technologies

et al. 2018

194

126

required. Several types of emerging mem ories have been researched in the past few decades such as magnetic memory, phase change memory, ferroelectric tunnel junc tions, and resistive switching memory. Among these emerging devices, resistive switching memory called memristors, introduced by Chua in 1971, [1] have strong points of small cell size, nonvolatile and random data access possibility, easy fabri cation process, and simple structure. [2,3] Because of these advantages, various mate rials are examined for achieving memris tive properties.In addition, different from the past sev eral decades, information is being made depending on experiences or repeated stimuli similar to that in the human brain. The human brain contains ≈10 11 neurons and 10 15 synapses, occupies a small space, and consumes less than 20 W, which is lower than the power required to run a household light bulb. [4][5][6] Moreover, the human brain is currently considered as the most intelligent and fastest operation system. Therefore, neuromorphic computing, which emu lates the human brain, has been regarded as a promising nextgeneration computing system. Studies on neuromorphic computing have been rapidly growing and highlighted for various applications such as artificial intelligence, sensors, robotic devices, and memory devices.Existing neural networks are implemented by the combination of machine learning as software and the von Neumann archi tecture as hardware based on the complementary metaloxide semiconductor (CMOS) technology. However, CMOSbased cir cuits require 6-12 transistors and the design is not flexible. [7] The present computing system with the von Neumann architecture is implemented by a serial operation through a central processing unit (CPU). Because of the von Neumann bottleneck, memory devices have limitations in data processing speed between memory and CPU and require high power and large space. [8][9][10] Therefore, a new neuromorphic computing system that is exe cuted by parallel operation with a high operation speed, low energy consumption, and small volume is critically required.To achieve such requirement, memristive materials have been actively examined as emulating several functions of human brain. A memristor could act as a single unit of synapse without software programming supports. Memristorbased neu romorphic architecture is implemented by parallel operation with efficient power, small volume, and high data processing Neuromorphic architectures are in the spotlight as promising candidates for substituting current computing systems owing to their high operation speed, scale-down ability, and, especially, low energy consumption. Among candidate materials, memristors have shown excellent synaptic behaviors such as spike time-dependent plasticity and spike rate-dependent plasticity by gradually changing their resistance state according to electrical input stimuli. Memristor can work as a single synapse without programming support, which remarkably satisfies the requirements of neuromorphic computing. Here, the mo...

Section: Requirements For Artificial Synapsesmentioning

confidence: 99%

Recent Advances in Memristive Materials for Artificial Synapses

Han

Adv Materials Technologies

et al. 2018

194

126

“…Our fabricated ReRAMs are attributed to localized accumulation of oxygen vacancies along the defect structure across the device [51,52]. Oxygen vacancy is known to facilitate the formation and rupture of nano-filaments, which is responsible for the bipolar switching between HRS and LRS [52]. Electrical characterization of ReRAM and measurement data was gathered with Keithley 4200 Semiconductor Characterization System (SCS).…”

Section: A-sto-based Reram Fabricationmentioning

confidence: 99%

“…Full details on the electrical, electroforming, and switching characteristics of the a-STO memristors can be found in Refs. [51][52][53].…”

Section: A-sto-based Reram Fabricationmentioning

confidence: 99%

Nano-Intrinsic True Random Number Generation: A Device to Data Study

IEEE Trans. Circuits Syst. I

Nili

Truong

et al. 2019

Self Cite

Recent advances in predictive data analytics and ever growing digitalization and connectivity with explosive expansions in industrial and consumer Internet-of-Things (IoT) has raised significant concerns about security of people's identities and data. It has created close to ideal environment for adversaries in terms of the amount of data that could be used for modeling and also greater accessibility for side-channel analysis of security primitives and random number generators. Random number generators (RNGs) are at the core of most security applications. Therefore, a secure and trustworthy source of randomness is required to be found. Here, we present a differential circuit for harvesting one of the most stochastic phenomenon in solidstate physics, random telegraphic noise (RTN), that is designed to demonstrate significantly lower sensitivities to other sources of noises, radiation and temperature fluctuations. We use RTN in amorphous SrTiO3-based resistive memories to evaluate the proposed true random number generator (TRNG). Successful evaluation on conventional true randomness tests (NIST tests) has been shown. Robustness against using predictive machine learning and side-channel attacks have also been demonstrated in comparison with non-differential readouts methods.

“…Recently, multilevel storage in a single memristor has attracted great interests to enhance the storage density without affecting scalability. [155][156][157] A thin fast ionic conductors 158 AgInSbTe (AIST) layer was introduced into an Ag/MAPbI 3 / FTO structure as an Ag reservoir and protecting layer ( Figure 24A). 97 The memristor showed tristate RS in the RESET process, providing multilevel memory capability ( Figure 24B).…”

Section: Flexible and Multilevel Applicationsmentioning

confidence: 99%

Memristors with organic‐inorganic halide perovskites

Zhao

Lin

et al. 2019

InfoMat

144

115

Organic‐inorganic halide perovskites (OHPs) have been intensively studied for application in solar cells with high conversion efficiency exceeding 22%. The unique electrical and optical properties of OHPs have led to their use in optoelectronic device applications beyond photovoltaics, such as light‐emitting diodes, photodetectors, transistors. New information storage technologies and computing architectures are being researched extensively with the aim of addressing the growing challenge of approaching end of Moore's law and von Neumann bottleneck. As the fourth basic circuit element, memristor is a leading candidate with powerful capabilities in information storage and neuromorphic computing applications. Recently, OHPs have received growing attention as promising materials for memristors. In particular, their mixed ionic‐electronic conduction ability paired with light sensitivity provide OHPs with the opportunity to display novel functions such as optical‐erase memory, optogenetics‐inspired synaptic functions, and light‐accelerated learning capability. This review covers recent advances in OHP‐based memristors development including memristive mechanism and analytical models, universal memristive characteristics for memory and neuromorphic computing applications, and novel multi‐functionalization. Challenges and future prospects of OHP‐based memristors are also discussed.