High-performance Memory Research Articles

Outstanding Stability and Resistive Switching Performance through Octa-Amino-Polyhedral Oligomeric Silsesquioxane Modification in Flexible Perovskite Resistive Random-Access Memories.

Resistive random access memory (RRAM) has emerged as a promising candidate for next-generation storage technologies due to its simple structure, high running speed, excellent durability, high integration density, and low power consumption. This paper focuses on the application of organic-inorganic hybrid perovskite (OIHP) materials in RRAM by introducing an innovative three-dimensional POPA modification strategy, which is realized by binding octa-amine-polyhedral oligomeric silsesquioxanes (8NH2-POSS) onto the side chains of poly(acrylic acid) (PAA), thereby enhancing the material's resilience under elevated temperatures and humidity conditions. POPA cross-links with perovskite grains at crystalline boundaries through multiple -NH3+ and -C═O chemical anchoring sites on its branch chain, enhancing the grain adhesion, optimizing the film quality, and improving the cage structure distribution at the perovskite grain boundaries. The experimental results demonstrate that the POPA-modified OIHP RRAM exhibits an excellent resistance switching performance, with an optimal ON/OFF ratio of 5.0 × 105 and a data retention time of 104 s. After 150 days of environmental exposure, the ON/OFF ratio remains at 1.0 × 105, indicating good stability. Furthermore, the POPA modification endows the perovskite film with considerable flexibility, maintaining stable resistance switching performance under various bending radii. This study provides a vital reference for flexible, high-performance, and long-lifespan perovskite memory devices.

Enhancement of perpendicular magnetic anisotropy in W/Co/Pt films by nitrogen doping in the W layer

The modulation of perpendicular magnetic anisotropy (PMA) in films has been the subject of considerable research interest, as it is proposed to be a key component for the design and realization of efficient magnetic switching in spintronic devices. In this study, we report the appearance of PMA in the as-deposited WNx/Co/Pt films without annealing. The strength of the PMA is quantified by means of effective magnetic anisotropy constant Keff, which is correlated with the N2 gas/Ar gas flow rate ratio PN2. The highest Keff value, 1.347 × 106 erg/cm3, is obtained for the sample deposited with PN2 of 40%. This phenomenon can be explained in two ways. On the one hand, the results of the experiment demonstrate that appropriate nitrogen doping can facilitate the formation of an ideal nitrided state at the WNx/Co interface, while simultaneously reducing the roughness of the WNx/Co interface, which, in turn, enhances the PMA of the WNx/Co/Pt films. On the other hand, the first-principles calculations indicate that the enhancement of PMA can be attributed to the modification of orbital hybridization at the Co/Pt interface by WNx. This innovative approach has the potential to advance the development of high-performance magnetic random-access memory devices.

Demonstration of bipolar resistive memory fabricated using an ultra-thin BaTiOx resistive switching layer with a thickness of ∼5 nm

In this study, a high-performance non-volatile bipolar resistive random-access memory (RRAM), which was fabricated using an ultra-thin barium titanate (BaTiOx, BTO) film as a resistive switching (RS) layer, was demonstrated. The BTO RS layers, whose thicknesses were only about 5 nm, were prepared using radio-frequency sputtering under different oxygen flow rates. Introduction of oxygen was used to modify the chemical compositions of the BTO films. Copper was used as the top electrode material to realize a Cu/BTO/n+-Si electrochemical metallization memory, where the resistance switching was triggered by electrochemical reaction of Cu electrode. The RRAM could repeatedly and consistently switch between a high-resistance state and a low-resistance state over 300 cycles. A large memory window of 105 and a long data retention time of >1.5 × 104 s both at room temperature and 85 °C were observed. Moreover, the Cu/BTO/n+-Si RRAM showed high switching speeds of 60–70 ns.

Large resistive switching in ultrathin BiFeO3 thin films

Electrically induced resistive switching (RS) effects have been proposed as the basis for future non-volatile memories. In this work, 9 nm-thick BiFeO3 (BFO) epitaxial thin films were deposited on (001)-oriented SrTiO3 substrates by pulsed laser deposition and their resistive switching (RS) behaviors were investigated. A large resistive switching with ON/OFF ratio of ∼106 is observed, surpassing the performance of most resistive random access memories ever reported. The conducting filament is proposed to dominate the RS behavior in the positive voltage region, while the modulation of ferroelectric polarization is suggested to play a significant role in the negative voltage region. Our study significantly deepens the understanding of the physical origin of RS and could provide a reference for designing high-performance memories and memristors based on ultrathin ferroelectric films.

Asymmetrically Functionalized Phenanthro[9,10-d]imidazole-Based Donor–Acceptor Architectures for High-Performance Ternary Memory Devices

Asymmetrically Functionalized Phenanthro[9,10-d]imidazole-Based Donor–Acceptor Architectures for High-Performance Ternary Memory Devices

A Review Paper on Memory Fault Models and its Algorithms

The significance of testing semiconductor memories has grown significantly in the semiconductor industry due to the increased density of modern memory chips. This paper aims to investigate and analyze different types of functional faults present in today's memory technology. These faults include stuck-at faults, transition faults, coupling faults, address decoder faults, and neighborhood pattern-sensitive faults. The paper also delves into the techniques utilized to identify and detect these faults. In particular, the focus is placed on the importance of zero-one, checkerboard, and March pattern tests, which are widely employed to assess functional memory defects at different levels, such as the chip level, array level, and board level. Furthermore, the study provides an in-depth exploration of various test algorithms and thoroughly examines their fault coverage capabilities. Overall, this review paper provides valuable insights into the challenges posed by the dense nature of modern memory chips and offers a comprehensive analysis of functional faults in memory technology. By emphasizing the importance of testing and presenting a detailed exploration of fault detection methods and test algorithms, this study contributes to the advancement of reliable and high-performance memory devices in the electronic industry suggesting that MARCH algorithms outperform others when considering factors like fault coverage, power efficiency, area optimization, and time complexity parameters, making them the preferable choice for reliable and high-performance memory devices in the electronic industry.

Polar phonons features and intrinsic dielectric properties of RE2CoMnO6 (RE = Tb, Dy, Ho, Tm and Yb) double perovskites

RE-based double perovskite (DP) oxides of the RE₂TMTM'O₆ family exhibit intriguing magnetodielectric (MD) properties and magnetocaloric effects (MCEs), making them promising candidates for applications in high-performance supercapacitors, memories, spintronic devices, and cryogenic magnetic refrigeration. This paper addresses the vibrational properties of RE2CoMnO6 (RE = Tb, Dy, Ho, Tm, and Yb) ceramics for the first time using infrared reflectance spectroscopy. The dielectric merit factors in the microwave (MW) range, including the quality factor (Qu) and the static dielectric constant (ɛ0), were estimated. It was observed that the intrinsic dielectric constants decrease from approximately 26.2 to 14.4 as the rare earth ionic radius decreases. The mean values of these compounds suggest their suitability for applications in MW circuitry devices.

Supercapacitor electrodes based on Ru/RuO2 decorated on N,S-doped few-layer graphene

Innovatively designed flexible and high-performance shape memory polymer supercapacitors are promising next-generation energy storage devices for portable and wearable electronics. Therefore, thermally induced smart shape memory polymer polyurethane was synthesized using polycaprolactone/poly(ethylene glycol)/hexamethylene diisocyanate (PCL/PEG/HDI) in which the incorporation of PEG successfully assisted in lowering the transition temperature. The resulting polymer exhibits a remarkable transition temperature of 50 °C and a shape recovery ratio of 100 % at a strain of 300 %. The smart polymer was coated with a silver paste to ease the electron flow followed by coating with the electrode. Hybrid electrode materials are prepared in two stages. Initially, a few layers of holey-graphene were synthesized, where BET surface area reveals the presence of pores that are tuned by isothermal treatments in O2-atmosphere. Subsequently, active hybrid electrode materials nanostructured Ru/RuO2 decorated N, S-doped few layers of holey graphene were synthesized using hydrothermal methods. Here, nitrogen and sulfur doping in the basal or edge plane served as catalysts and metal coordination sites, facilitating the uniform distribution of ruthenium nanoparticles on the graphene. It is further confirmed by the density function theory (DFT) calculation using the adsorption energy. The optimized hybrid electrode material demonstrates a specific capacitance of 1297 mFcm−2 at a current density of 2 mAcm−2. A flexible-shape memory polymer supercapacitor exhibits a notable device-specific capacitance of 189 mFcm−2 at a current density of 1 mAcm−2, a high energy density of 45 µWhcm−2 at a power density of 0.25 mWcm−2, with 81 % of specific capacitance retention after eight shape programming and recovery cycle. Excellent shape recoverability and robust electrochemical performance make the as-fabricated symmetric supercapacitors a promising candidate for integration in flexible electronic applications.

Exploring the Adaptability of 4D Printed Shape Memory Polymer Featuring Dynamic Covalent Bonds.

4D printing (4DP) of high-performance shape memory polymers (SMPs), particularly using digital light processing (DLP), has garnered intense global attention due to its capability for rapid and high-precision fabrication of complex configurations, meeting diverse application requirements. However, the development of high-performance dynamic shape memory polymers (DSMPs) for DLP printing remains a significant challenge due to the inherent incompatibilities between the photopolymerization process and the curing/polymerization of high-strength polymers. Here, a mechanically robust DSMP compatible is developed with DLP printing, which incorporates dynamic covalent bonds of imine linking polyimide rigid segments, exhibiting remarkable mechanical performance (tensile strength ≈41.7MPa, modulus ≈1.63GPa) and thermal stability (Tg ∼ 113°C, Td ∼ 208°C). More importantly, benefiting from the solid-state plasticity conferred by dynamic covalent bonds, 4D printed structures demonstrate rapid network adaptiveness, enabling effortless realization of reconfiguration, self-healing, and recycling. Meanwhile, the extensive π-π conjugated structures bestow DSMP with an intrinsic photothermal effect, allowing controllable morphing of the 4D configuration through dual-mode triggering. This work not only greatly enriches the application scope of high-performance personalized configurations but also provides a reliable approach to addressing environmental pollution and energy crises.

Mgikit: demultiplexing toolkit for MGI fastq files.

MGI sequencing is reported to be an inexpensive solution to obtain genomics information. There is a growing need for software and tools to analyse MGI's outputs efficiently. mgikit is a tool collection to demultiplex MGI fastq data, reformat it effectively and produce visual quality reports. mgikit overcomes several limitations of the standard MGI demultiplexer. It is highly customizable to suit different kinds of datasets and is designed to achieve high performance and optimal memory utilization. The tool and its documentation are available at: https://sagc-bioinformatics.github.io/mgikit/.

Controllable fabrication of shape memory epoxy resin filament by polymerization-melting-mixing-drawing process and performance regulation

ABSTRACT This work controlled the polymerization reaction, melting, and mixing processing of a mixture including epoxy resin and polyethylene glycol (PEG) by a pulverizer, to effectively develop high-performance shape memory epoxy/PEG (EPP) filaments. The process parameters of polymerization-melting-mixing-drawing were determined through the extrusion force investigation. The results showed that EPP mixtures could be developed into filaments through polymerization-melting-mixing-drawing process, reducing the spinning temperature and filament diameter from 300°C and 31 mm to 235°C and 13 mm, respectively. The Fourier transform infrared spectra confirmed the complete polymerization of epoxy resin and the stable existence of PEG. EPP filaments developed through the polymerization-melting-mixing-drawing process have exhibited controllable dynamic thermal mechanical properties and Tg, excellent shape recovery effects, and adjustable stimulus temperature. This work developed shape memory EPP filaments through a polymerization-melting-mixing-drawing process, with controllable spinnability and adjustable stimulus temperature, providing a reference for high-performance filament fabrication and process optimization.

Cyclic tensile response and crack closure performance of novel superelastic Ni-Ti SMA cable grid-reinforced engineered cementitious composites

To further improve the tensile and self-healing properties of engineered cementitious composites (ECCs) for their special uses such as harsh environments, plastic hinge regions, and old members strengthening, an innovative high-performance superelastic Ni-Ti shape memory alloy (SMA) cable grid-reinforced ECC (SMACG-ECC) was first proposed and tested in this study. The effects of water-binder ratio, cable grid material, and reinforcement ratio on the cyclic tensile properties and crack closure performance of the proposed SMACG-ECC were systematically examined. Test results revealed that the tensile strength, initial crack strength, ultimate tensile strain, dense crack, crack closure, and self-healing properties of the traditional ECC were obviously improved by being reinforced with the SMACG. Compared with the ordinary steel cable grid-reinforced ECC (SCG-ECC), the proposed SMACG-ECC exhibited approximately 50 % lower first crack strength, similar tensile strength and deformation ductility, but a 53.7 %-75.1 % greater energy dissipation, 75.8 %-80.2 % lower residual strain, and 80.4 %-83.4 % smaller maximum residual crack width. The tensile properties, cracking performance, and self-healing ability improved with the incorporation of SMACG as well as the increase in reinforcement ratio. Notably, most specimens showed satisfactory composite operation with no cable ends slipping during the entire cyclic loading process, with a maximum tensile strain amplitude of 8 %.

Interactions among neighborhood conditions, sleep quality, and episodic memory across the adult lifespan

ABSTRACT Objectives On average, adults racialized as non-Hispanic Black and Hispanic sleep more poorly than adults racialized as non-Hispanic White (hereafter, Black, Hispanic, White), but associations between factors that may moderate sleep-memory associations in these groups, such as neighborhood conditions, are unclear. Poorer neighborhood conditions (e.g. lower neighborhood cohesion) may be negatively associated with sleep quality and multiplicatively influence sleep-memory associations. We hypothesized lower ratings of neighborhood conditions would be associated with poorer sleep quality and moderate the association between sleep quality and episodic memory, especially in Black and Hispanic adults, who are disproportionately situated in poor neighborhood conditions. Design Seven-hundred-thirty-six adults across the adult lifespan (27-89 years) were recruited from the northern Manhattan community as a part of the Offspring Study of Racial and Ethnic Disparities in Alzheimer’s disease. Sleep quality was assessed using a modified version of the Pittsburgh Sleep Quality Index, and episodic memory was evaluated with the Buschke Selective Reminding Test. With multiple regression models, we measured associations between perceived neighborhood conditions and sleep quality and the interaction between sleep quality and neighborhood conditions on episodic memory stratified by racial/ethnic and gender identity groups. Results Overall, poorer neighborhood conditions were associated with poorer sleep quality. In Black and Hispanic women, the sleep-memory association was moderated by neighborhood conditions. With more favorable neighborhood conditions, Black women showed an association between higher sleep quality and higher memory performance, and Hispanic women showed a protective effect of neighborhood (higher memory even when sleep quality was poor). Conclusion Poorer neighborhood experiences may contribute to poorer sleep quality across groups. In Black and Hispanic women, the association between sleep quality and episodic memory performance was dependent upon neighborhood conditions. These findings may inform tailored, structural level sleep interventions, aimed to improve neighborhood experiences and thereby sleep quality and episodic memory.

Ultra-low stress-hysteresis and huge superelasticity in NiMn-based shape memory microwire

Hysteresis related to first-order phase transformation in shape memory alloys, which is the macroscopic manifestation of energy dissipation, is detrimental to the precise control of actuation and causes structural and functional fatigue of components. It is of vital importance to explore high-performance shape memory alloys with low stress-hysteresis, large superelasticity, and wide temperature range operation in practical applications. Here, we have developed a Ni-Mn-Fe-In shape memory microwire, exhibiting an ultra-low stress-hysteresis and huge tensile superelasticity in a wide temperature range. The microwire shows a smooth surface and a single crystal structure (with ⟨001⟩A-oriented along the axial direction of microwire), and the microstructure of the microwire contains austenite matrix and sparsely distributed precipitates with an average size of 20–80 nm, all of which may be beneficial to obtain low hysteresis and large strains in the microwire. As a result, the microwire exhibits a minimum stress-hysteresis of as low as 8.5 MPa (with overall strain of 15.3%) and corresponding energy dissipation as low as 1.44 MJ/m3. The microwire always shows a low stress-hysteresis (less than 24 MPa) and low energy dissipation (less than 2.86 MJ/m3) above room temperature. The microwire shows a huge superelasticity with recoverable strains higher than 15% in the wide temperature range from 218 to 418 K. Together with the advantages of easy fabrication and no post-processing required, this microwire shows a tremendous potential for cyclic actuators and energy conversion devices under multi-field coupling.

Dimensional Scaling of Ferroelectric Properties of Hafnia-Zirconia Thin Films: Electrode Interface Effects.

Hafnia-based ferroelectric (FE) thin films are promising candidates for semiconductor memories. However, a fundamental challenge that persists is the lack of understanding regarding dimensional scaling, including thickness scaling and area scaling, of the functional properties and their heterogeneity in these films. In this work, excellent ferroelectricity and switching endurance are demonstrated in 4 nm-thick Hf0.5Zr0.5O2 (HZO) capacitors with molybdenum electrodes in capacitors as small as 65 nm × 45 nm in size. The HZO layer in these capacitors can be crystallized into the ferroelectric orthorhombic phase at the low temperature of 400 °C, making them compatible for back-end-of-line (BEOL) FE memories. With the benefits of thickness scaling, low operation voltage (1.2 V) is achieved with high endurance (>1010 cycles); however, a significant fatigue regime is noted. We observed that the bottom electrode, rather than the top electrode, plays a dominant role in the thickness scaling of HZO ferroelectric behavior. Furthermore, ultrahigh switched polarization (remanent polarization 2Pr ∼ 108 μC cm-2) is observed in some nanoscale devices. This study advances the understanding of dimensional scaling effects in HZO capacitors for high-performance FE memories.

Insight into Facile Ion Diffusion in Resistive Switching Medium toward Low Operating Voltage Memory.

The rapid increase in data storage worldwide demands a substantial amount of energy consumption annually. Studies looking at low power consumption accompanied by high-performance memory are essential for next-generation memory. Here, Graphdiyne oxide (GDYO), characterized by facile resistive switching behavior, is systematically reported toward a low switching voltage memristor. The intrinsic large, homogeneous pore-size structure in GDYO facilitates ion diffusion processes, effectively suppressing the operating voltage. The theoretical approach highlights the remarkably low diffusion energy of the Ag ion (0.11 eV) and oxygen functional group (0.6 eV) within three layers of GDYO. The Ag/GDYO/Au memristor exhibits an ultralow operating voltage of 0.25 V with a GDYO thickness of 5 nm; meanwhile, the thicker GDYO of 29 nm presents multilevel memory with an ON/OFF ratio of up to 104. The findings shed light on memory resistive switching behavior, facilitating future improvements in GDYO-based devices toward opto-memristors, artificial synapses, and neuromorphic applications.

Multifunction realization in MoS2/WS2/h-BN heterojunction: Integrated self-powered high-performance photodetection, visualization, nonvolatile memory, and synaptic simulation

Nowadays, efficient performance, functional diversification, device miniaturization and systematic integration are the trends for developing new electronic information technology. However, photodetectors with only optoelectronic detection functions cannot satisfy the growing demands of the multifunction required in single devices. This paper presents a MoS2/WS2/h-BN van der Waals heterojunction device realizing multifunctional applications which integrated self-powered high-performance photodetection, visualization, nonvolatile memory, and synaptic simulation. The heterojunction achieves a high responsivity of 9.28 A/W, an excellent specific detectivity of 6.1×1012 Jones, a large external quantum efficiency of 2358 %, a considerable on/off ratio of 7×105 and an ultrafast response time of 0.6 μs at 1 V bias under 488 nm laser irradiation. Besides, the photodetector shows excellent photovoltaic characteristics with a short-circuit current of 0.22 μA and an open-circuit voltage of 0.34 V. Moreover, the device also shows a favorable optical response to 532 and 633 nm lasers. The device also realizes visualization and can be used as an imaging sensor. In addition, the device integrates nonvolatile memory function due to the charge storage effect of h-BN and h-BN/SiO2 interface. Furthermore, biological synaptic functions, such as the memory process, short- and long-term plasticity, and double pulse facilitation, are also successfully simulated. This work realizes the high-performance and multifunctional applications of MoS2/WS2/h-BN device based on a new strategy of utilizing h-BN as a heterojunction substrate.

High-Performance LiNbO3 Domain Wall Memory Devices with Enhanced Selectivity via Optimized Metal-Semiconductor Contact.

Lithium niobate (LiNbO3) single-crystal nanodevices featuring elevated readout domain wall currents exhibit significant potential for integrated circuits in memory computing applications. Nevertheless, challenges stem from suboptimal electrode-LiNbO3 single crystal contact characteristics, which impact the stability of high currents within these devices. In this work, we concentrate on augmenting the domain wall current by refining the fabrication processes of domain wall random access memory (DWRAM). Each LiNbO3 domain wall nanodevice was fabricated using a self-aligned process. Device performance was significantly enhanced by introducing a 10 nm interlayer between the LiNbO3 and Cu electrodes. A comparative analysis of electrical properties was conducted on devices with interlayers made of chromium (Cr) and titanium (Ti), as well as devices without interlayers. After the introduction of the Ti interlayer, the device's coercive voltage demonstrated an 82% reduction, while the current density showed a remarkable 94-fold increase. A 100 nm sized device with the Ti interlayer underwent positive down-negative up pulse testing, demonstrating a writing time of 82 ns at 8 V and an erasing time of 12 μs at -9 V. These operating speeds are significantly faster than those of devices without interlayers. Moreover, the enhanced devices exhibited symmetrical domain switching hysteresis loops with retention times exceeding 106 s. Notably, the coercive voltage (Vc) dispersion remained narrow after more than 1000 switching cycles. At an elevated temperature of 400 K, the device's on/off ratio was maintained at 105. The device's embedded selector demonstrated an ultrahigh selectivity (>106) across various reading voltages. These results underscore the viability of high-density nanoscale integration of ferroelectric domain wall memory.

Accelerating Graph Analytics Using Attention-Based Data Prefetcher

Graph analytics shows promise for solving challenging problems on relational data. However, memory constraints arise from the large size of graphs and the high complexity of algorithms. Data prefetching is a crucial technique to hide memory access latency by predicting and fetching data into the memory cache beforehand. Traditional prefetchers struggle with fixed rules in adapting to complex memory access patterns in graph analytics. Machine learning (ML) algorithms, particularly long short-term memory (LSTM) models, excel in memory access prediction. However, they encounter challenges such as difficulty in learning interleaved access patterns and high storage costs when predicting in large memory address space. In addition, there remains a gap between designing a high-performance ML-based memory access predictor and developing an effective ML-based prefetcher for an existing memory system. In this work, we propose a novel Attention-based prefetching framework to accelerate graph analytics applications. To achieve high-performance memory access prediction, we propose A2P, a novel Attention-based memory Access Predictor for graph analytics. We use the multi-head self-attention mechanism to extract features from memory traces. We design a novel bitmap labeling method to collect future deltas within a spatial range, making interleaved patterns easier to learn. We introduce a novel super page concept, allowing the model to surpass physical page constraints. To integrate A2P into a memory system, we design a three-module prefetching framework composed of an existing memory hierarchy, a prefetch controller, and the predictor A2P. In addition, we propose a hybrid design to combine A2P and existing hardware prefetchers for higher prefetching performance. We evaluate A2P and the prefetching framework using the widely used GAP benchmark. Prediction experiments show that for the top three predictions, A2P outperforms the widely used state-of-the-art LSTM-based model by 23.1% w.r.t. Precision, 21.2% w.r.t. Recall, and 10.4% w.r.t. Coverage. Prefetching experiments show that A2P provides 18.4% IPC Improvement on average, outperforming state-of-the-art prefetchers BO by 17.2%, ISB by 15.0%, and Delta-LSTM by 10.9%. The hybrid prefetcher combining A2P and ISB achieves 21.7% IPC Improvement, outperforming the hybrid of BO and ISB by 16.3%.

High-performance photon number resolving detectors for 850–950 nm wavelength range

Since their first demonstration in 2001 [Gol’tsman et al., Appl. Phys. Lett. 79, 705–707 (2001)], superconducting-nanowire single-photon detectors (SNSPDs) have witnessed two decades of great developments. SNSPDs are the detector of choice in most modern quantum optics experiments and are slowly finding their way into other photon-starved fields of optics. Until now, however, in nearly all experiments, SNSPDs were used as “binary” detectors, meaning that they could only distinguish between 0 and >=1 photons, and photon number information was lost. Recent research has demonstrated proof-of-principle photon-number resolution (PNR) SNSPDs counting 2–5 photons. The photon-number-resolving capability is highly demanded in various quantum-optics experiments, including Hong–Ou–Mandel interference, photonic quantum computing, quantum communication, and non-Gaussian quantum state preparation. In particular, PNR detectors at the wavelength range of 850–950 nm are of great interest due to the availability of high-quality semiconductor quantum dots (QDs) [Heindel et al., Adv. Opt. Photonics 15, 613–738 (2023)] and high-performance cesium-based quantum memories [Ma et al., J. Opt. 19, 043001 (2017)]. In this paper, we demonstrate NbTiN-based SNSPDs with >94% system detection efficiency, sub-11 ps timing jitter for one photon, and sub-7 ps for 2 photons. More importantly, our detectors resolve up to 7 photons using conventional cryogenic electric readout circuitry. Through theoretical analysis, we show that the PNR performance of demonstrated detectors can be further improved by enhancing the signal-to-noise ratio and bandwidth of our readout circuitry. Our results are promising for the future of optical quantum computing and quantum communication.