Chalcogenide Ovonic Threshold Switching Selector

Today’s explosion of data urgently requires memory technologies capable of storing large volumes of data in shorter time frames,a feat unattain-able with Flash or DRAM.Intel Optane,commonly referred to as three-dimensional phase change memory,stands out as one of the most promising candidates.The Optane with cross-point architecture is constructed through layering a storage element and a selector known as the ovonic threshold switch(OTS).The OTS device,which employs chalcogenide film,has thereby gathered increased attention in recent years.In this paper,we begin by providing a brief introduction to the discovery process of the OTS phenomenon.Subsequently,we summarize the key elec-trical parameters of OTS devices and delve into recent explorations of OTS materials,which are categorized as Se-based,Te-based,and S-based material systems.Furthermore,we discuss various models for the OTS switching mechanism,including field-induced nucleation model,as well as several carrier injection models.Additionally,we review the progress and innovations in OTS mechanism research.Finally,we highlight the successful application of OTS devices in three-dimensional high-density memory and offer insights into their promising performance and extensive prospects in emerging applications,such as self-selecting memory and neuromorphic computing.

Volatile threshold switching memristor:An emerging enabler in the AIoT era

With rapid advancement and deep integration of artificial intelligence and the internet-of-things,artificial intelligence of things has emerged as a promising technology changing people’s daily life.Massive growth of data generated from the devices challenges the AIoT systems from information collection,storage,processing and communication.In the review,we introduce volatile threshold switching memristors,which can be roughly classified into three types:metallic conductive filament-based TS devices,amorphous chalcogenide-based ovonic threshold switching devices,and metal-insulator transition based TS devices.They play important roles in high-density storage,energy efficient computing and hardware security for AIoT systems.Firstly,a brief introduction is exhibited to describe the categories(materials and characteristics)of volatile TS devices.And then,switching mechanisms of the three types of TS devices are discussed and systematically summarized.After that,attention is focused on the applications in 3D cross-point memory technology with high storage-density,efficient neuromorphic computing,hardware security(true random number generators and physical unclonable functions),and others(steep subthreshold slope transistor,logic devices,etc.).Finally,the major challenges and future outlook of volatile threshold switching memristors are presented.

Impact of variations of threshold voltage and hold voltage of threshold switching selectors in 1S1R crossbar array

The impact of the variations of threshold voltage(V_(th))and hold voltage(V_(hold))of threshold switching(TS)selector in1 S1 R crossbar array is investigated.Based on ON/OFF state I–V curves measurements from a large number of Ag-filament TS selectors,V_(th)and V_(hold)are extracted and their variations distribution expressions are obtained,which are then employed to evaluate the impact on read process and write process in 32×321 S1 R crossbar array under different bias schemes.The results indicate that V_(th)and V_(hold)variations of TS selector can lead to degradation of 1 S1 R array performance parameters,such as minimum read/write voltage,bit error rate(BER),and power consumption.For the read process,a small V_(hold)variation not only results in the minimum read voltage increasing but it also leads to serious degradation of BER.As the standard deviation of V_(hold)and V_(th)increases,the BER and the power consumption of 1 S1 R crossbar array under 1/2 bias,1/3 bias,and floating scheme degrade,and the case under 1/2 bias tends to be more serious compared with other two schemes.For the write process,the minimum write voltage also increases with the variation of V_(hold)from small to large value.A slight increase of V_(th)standard deviation not only decreases write power efficiency markedly but also increases write power consumption.These results have reference significance to understand the voltage variation impacts and design of selector properly.

Oscillation neuron based on a low-variability threshold switching device for high-performance neuromorphic computing

Low-power and low-variability artificial neuronal devices are highly desired for high-performance neuromorphic computing.In this paper,an oscillation neuron based on a low-variability Ag nanodots(NDs)threshold switching(TS)device with low operation voltage,large on/off ratio and high uniformity is presented.Measurement results indicate that this neuron demonstrates self-oscillation behavior under applied voltages as low as 1 V.The oscillation frequency increases with the applied voltage pulse amplitude and decreases with the load resistance.It can then be used to evaluate the resistive random-access memory(RRAM)synaptic weights accurately when the oscillation neuron is connected to the output of the RRAM crossbar array for neuromorphic computing.Meanwhile,simulation results show that a large RRAM crossbar array(>128×128)can be supported by our oscillation neuron owing to the high on/off ratio(>10^(8))of Ag NDs TS device.Moreover,the high uniformity of the Ag NDs TS device helps improve the distribution of the output frequency and suppress the degradation of neural network recognition accuracy(<1%).Therefore,the developed oscillation neuron based on the Ag NDs TS device shows great potential for future neuromorphic computing applications.

Redox Memristors with Volatile Threshold Switching Behavior for Neuromorphic Computing

The spiking neural network(SNN),closely inspired by the human brain,is one of the most powerful platforms to enable highly efficient,low cost,and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system.In the hardware implementation,the building of artificial spiking neurons is fundamental for constructing the whole system.However,with the slowing down of Moore’s Law,the traditional complementary metal-oxide-semiconductor(CMOS)technology is gradually fading and is unable to meet the growing needs of neuromorphic computing.Besides,the existing artificial neuron circuits are complex owing to the limited bio-plausibility of CMOS devices.Memristors with volatile threshold switching(TS)behaviors and rich dynamics are promising candidates to emulate the biological spiking neurons beyond the CMOS technology and build high-efficient neuromorphic systems.Herein,the state-of-the-art about the fundamental knowledge of SNNs is reviewed.Moreover,we review the implementation of TS memristor-based neurons and their systems,and point out the challenges that should be further considered from devices to circuits in the system demonstrations.We hope that this review could provide clues and be helpful for the future development of neuromorphic computing with memristors.

Ag-GST/HfO_(x)-based unidirectional threshold switching selector with low leakage current and threshold voltage distribution for high-density cross-point arrays

The use of electrochemical-metallization-based volatile threshold switching selectors in cross-point arrays has been widely explored owing to their high on-off ratios and simple structure.However,these devices are unsuitable for cross-point architectures because of the difficulty in controlling the random filament formation that results in large fluctuations in the threshold voltage during operation.In this study,we investigated the unidirectional threshold transition characteristics associated with an Ag/GST/HfO_(x)/Pt-based bilayer selector and demonstrated the occurrence of a low leakage current(<1×10^(-11) A) and low distribution of the threshold voltage(Δ0.11 V).The bilayer structure could control the filament formation in the intermediate state through the insertion of an HfO_(x) tunneling barrier.By stacking a bilayer selector with NiO_(x)based resistive random-access memory,the leakage and programming currents of the device could be significantly decreased.For the crossbar array configuration,we performed equivalent circuit analysis of a one-selector oneresistor(1S1R) devices and estimated the optimal array size to demonstrate the applicability of the proposed structure.The maximum acceptable crossbar array size of the 1S1R device with the Ag/GST/HfO_(x)/Pt/Ti/NiO_(x)/Pt structure was 5.29×10^(14)(N^(2),N=2.3×10^(7)).

Differential pressure difference based altitude control of a stratospheric satellite

An autonomous altitude adjustment system for a stratospheric satellite(StratoSat)platform is proposed.This platform consists of a helium balloon,a ballonet,and a two-way blower.The helium balloon generates lift to balance the platform gravity.The two-way blower inflates and deflates the ballonet to regulate the buoyancy.Altitude adjustment is achieved by tracking the differential pressure difference(DPD),and a threshold switching strategy is used to achieve blower flow control.The vertical acceleration regulation ability is decided not only by the blower flow rate,but also by the designed margin of pressure difference(MPD).Pressure difference is a slow-varying variable compared with altitude,and it is adopted as the control variable.The response speed of the actuator to disturbance can be delayed,and the overshoot caused by the large inertia of the platform is inhibited.This method can maintain a high tracking accuracy and reduce the complexity of model calculation,thus improving the robustness of controller design.

Multilayer doped-GeSe OTS selector for improved endurance and threshold voltage stability

Selector devices are indispensable components of large-scale memristor array systems.The thereinto,ovonic threshold switching(OTS)selector is one of the most suitable candidates for selector devices,owing to its high selectivity and scalability.However,OTS selectors suffer from poor endurance and stability which are persistent tricky problems for applica-tion.Here,we report on a multilayer OTS selector based on simple GeSe and doped-GeSe.The experimental results show im-proving selector performed extraordinary endurance up to 1010 and the fluctuation of threshold voltage is 2.5%.The reason for the improvement may lie in more interface states which strengthen the interaction among individual layers.These develop-ments pave the way towards tuning a new class of OTS materials engineering,ensuring improvement of electrical perform-ance.

A high-speed true random number generator based on Ag/SiNx/n-Si memristor

The intrinsic variability of memristor switching behavior can be used as a natural source of randomness,this variability is valuable for safe applications in hardware,such as the true random number generator(TRNG).However,the speed of TRNG is still be further improved.Here,we propose a reliable Ag/SiNx/n-Si volatile memristor,which exhibits a typical threshold switching device with stable repeat ability and fast switching speed.This volatile-memristor-based TRNG is combined with nonlinear feedback shift register(NFSR)to form a new type of high-speed dual output TRNG.Interestingly,the bit generation rate reaches a high speed of 112 kb/s.In addition,this new TRNG passed all 15 National Institute of Standards and Technology(NIST)randomness tests without post-processing steps,proving its performance as a hardware security application.This work shows that the SiNx-based volatile memristor can realize TRNG and has great potential in hardware network security.

Controlled multilevel switching and artificial synapse characteristics in transparent HfAlO-alloy based memristor with embedded TaN nanoparticles

Atomic layer deposition technique has been used to prepare tantalum nitride nanoparticles(TaN-NPs)and sandwiched between Al-doped HfO;layers to achieve ITO/HfAlO/TaN-NP/HfAlO/ITO RRAM device.Transmission electron microscopy along with energy dispersive spectroscopy confirms the presence of TaN-NPs.X-ray photoelectron spectroscopy suggests that part of Ta N converted to tantalum oxynitride(TaO_(x)N_(y))which plays an important role in stable cycle-to-cycle resistive switching.Charge trapping and oxygen vacancy creation were found to be modified after the inclusion of Ta N-NPs inside RRAM structure.Also,HfAlO/TaO_(x)N_(y)interface due to the presence TaN-NPs improves the device-to-device switching reliability by reducing the probability of random rupture/formation of conductive filaments(CFs).DC endurance of more than 10^(3)cycles and memory data retention up to 10^(4)s was achieved with an insignificant variation of different resistance states.Multilevel conductance was attained by controlling RESET voltage with stable data retention in multiple states.The volatile threshold switching was monitored after controlling the CF forming at 200 nA current compliance with high selectivity of~10^(3).Synaptic learning behavior has been demonstrated by spike-rate-dependent plasticity(SRDP).Reliable potentiation and depression processes were observed after the application of suitable negative and positive pulses which shows the capability of the TaN–NPs based RRAM device for transparent synaptic devices.

Deep machine learning unravels the structural origin of mid-gap states in chalcogenide glass for high-density memory integration

The recent development of three-dimensional semiconductor integration technology demands a key component-the ovonic threshold switching(OTS)selector to suppress the current leakage in the high-density memory chips.Yet,the unsatisfactory performance of existing OTS materials becomes the bottleneck of the industrial advancement.The sluggish development of OTS materials,which are usually made from chalcogenide glass,should be largely attributed to the insufficient understanding of the electronic structure in these materials,despite of intensive research in the past decade.Due to the heavy first-principles computation on disordered systems,a universal theory to explain the origin of mid-gap states(MGS),which are the key feature leading to the OTS behavior,is still lacking.To avoid the formidable computational tasks,we adopt machine learning method to understand and predict MGS in typical OTS materials.We build hundreds of chalcogenide glass models and collect major structural features from both short-range order(SRO)and medium-range order(MRO)of the amorphous cells.After training the artificial neural network using these features,the accuracy has reached~95%when it recognizes MGS in new glass.By analyzing the synaptic weights of the input structural features,we discover that the bonding and coordination environments from SRO and particularly MRO are closely related to MGS.The trained model could be used in many other OTS chalcogenides after minor modification.The intelligent machine learning allows us to understand the OTS mechanism from vast amount of structural data without heavy computational tasks,providing a new strategy to design functional amorphous materials from first principles.

Conductive metallic filaments dominate in hybrid perovskite-based memory devices

Organic-inorganic hybrid perovskites (OHPs) are well-known as light-absorbing materials in solar cells and have recently attracted considerable attention for the applications in resistive switching memory. Previous studies have shown that ions can migrate to form a conductive channel in perovskites under an external voltage. However, the exact resistance mechanism for Ag or halogens which dominate the resistive behavior is still controversial. Here, we demonstrate a resistive switching memory device based on Ag/FA0.83MA0.17Pb(I0.82Br0.18)3/fluorine doped tin oxide (FTO). The migration of Ag cations and halide anions is demonstrated by energy dispersive X-ray spectroscopy (EDS) after the SET process (positive voltage on Ag). By comparing the I-V behavior of the Au-based devices, it is clear that the conductive channel formed by Ag is the main factor of the switching characteristics for Ag-based devices. Meanwhile, by controlling the appropriate SET voltage, two kinds of resistance characteristics of the analog switch and threshold switch can be realized in the Ag-based device. As a result, it may be possible to implement both data storage and neuromorphic computing in a single device.