Memory Density Research Articles

Dynamics of Ferroelectric Flux Closure Array in Oxide Superlattices.

Topological polar soliton such as skyrmions, merons, vortices, flux closures represent topologically nontrivial structures with their stability governed by specific boundary conditions. These polar solitons can be utilized in enhancing memory density and reducing energy consumption in nanoelectronic devices. Flux closure domains exhibit high density and thermal stability, with a strain gradient as large as ≈106m-1 at the core, which is tunable by adjusting the materials thickness, periodicity. The practical utilization of topological structures like flux closure in advanced applications requires the ability to manipulate them using external stimuli and ensuring their stability under thermal excitation. In this study, piezo-force microscopy is employed to investigate the manipulation of flux closure nano-domains through external electric field and temperature to observe their evolution. The findings demonstrate that the application of electric field can create or annihilate these nano-domains and modify their density. Temperature variations significantly affect the density of flux closure domains, domain walls, correlating with enhanced capacitance of the system. This is crucial for improving the memory density of storage devices. Thus, by adjusting the density of these domains, it is possible to tailor the functional properties of nanoelectronic devices, such as capacitance and electromechanical response, enabling advanced application.

Differential predictive value of resident memory CD8+T cell subpopulations in patients with non-small-cell lung cancer treated by immunotherapy

BackgroundA high density of resident memory T cells (TRM) in tumors correlates with improved clinical outcomes in immunotherapy-treated patients. In most clinical studies, TRM are defined by the CD103 marker....

Binary associative memory networks: A review of mathematical framework and capacity analysis

In recent years, heightened interest has been ignited in associative memory networks, largely attributed to their perceived equivalence with the attention mechanism, a fundamental component of the Transformer architecture. The opaque nature of deep neural networks, often characterized as “black boxes”, has intensified the pursuit of explainability, positioning associative memory networks as promising candidates for illuminating the inherent complexities of deep learning models. Despite their increasing prominence, the mathematical analysis of their capacity remains a significant research gap, which constitutes the central focus of this paper. To address this gap, we commence with a review of the mathematical framework underpinning associative memory networks, with particular emphasis on their binary configurations, drawing insights from the derivation of the dense associative memory model. Additionally, we review a systematic methodology for analyzing the capacity of binary associative memory networks, building upon established studies of dense associative memory networks. Utilizing this analytical framework, we derive the capacity of several prominent associative memory networks, including binary modern Hopfield networks and binary spherical Hopfield networks. Through comprehensive discussions and rigorous deductions, we aim to elucidate the characteristics of binary associative memory networks, thereby providing valuable insights and practical guidance for their effective application in real-world scenarios.

Longevity, enhanced memory, and altered density of dendritic spines in hippocampal CA3 and dentate gyrus after hemizygous deletion of Pde2a in mice.

Studies using acute or subchronic pharmacological inhibition of phosphodiesterase 2 A (PDE2A) have led to its proposal as a target for treatment of cognitive deficits associated with neuropsychiatric and neurodegenerative disease. However, the impact of continuous inhibition of PDE2A on memory is unknown. Moreover, the neuroanatomical regions mediating memory enhancement have not been categorically identified. To address these open questions, we studied knockout mice and hippocampus restricted manipulations. Pde2a heterozygous knockout mice are viable with no gross histological abnormalities. The mice exhibit enhanced spatial and object recognition memory that is independent of anxiolytic effects and is paralleled by increased density of dendritic mushroom and thin spines in hippocampal CA3 and dentate gyrus in adult mice. In CA1, subtle alterations in spine density were seen, while theta-burst LTP and paired-pulse facilitation were normal. Spatial memory enhancement persists in aged Pde2a heterozygous knockout mice, and to our surprise these mice live significantly longer than wild-type littermate controls. In summary, we provide evidence that life-long reduction of PDE2A expression promotes spine formation and maturation, exerts beneficial effects on memory, and increases lifespan.

Long sequence Hopfield memory*

Abstract Sequence memory is an essential attribute of natural and artificial intelligence that enables agents to encode, store, and retrieve complex sequences of stimuli and actions. Computational models of sequence memory have been proposed where recurrent Hopfield-like neural networks are trained with temporally asymmetric Hebbian rules. However, these networks suffer from limited sequence capacity (maximal length of the stored sequence) due to interference between the memories. Inspired by recent work on Dense Associative Memories, we expand the sequence capacity of these models by introducing a nonlinear interaction term, enhancing separation between the patterns. We derive novel scaling laws for sequence capacity with respect to network size, significantly outperforming existing scaling laws for models based on traditional Hopfield networks, and verify these theoretical results with numerical simulation. Moreover, we introduce a generalized pseudoinverse rule to recall sequences of highly correlated patterns. Finally, we extend this model to store sequences with variable timing between states’ transitions and describe a biologically-plausible implementation, with connections to motor neuroscience.

Numerical Modeling of Vortex-Based Superconducting Memory Cells: Dynamics and Geometrical Optimization.

The lack of dense random-access memory is one of the main obstacles to the development of digital superconducting computers. It has been suggested that AVRAM cells, based on the storage of a single Abrikosov vortex-the smallest quantized object in superconductors-can enable drastic miniaturization to the nanometer scale. In this work, we present the numerical modeling of such cells using time-dependent Ginzburg-Landau equations. The cell represents a fluxonic quantum dot containing a small superconducting island, an asymmetric notch for the vortex entrance, a guiding track, and a vortex trap. We determine the optimal geometrical parameters for operation at zero magnetic field and the conditions for controllable vortex manipulation by short current pulses. We report ultrafast vortex motion with velocities more than an order of magnitude faster than those expected for macroscopic superconductors. This phenomenon is attributed to strong interactions with the edges of a mesoscopic island, combined with the nonlinear reduction of flux-flow viscosity due to the nonequilibrium effects in the track. Our results show that such cells can be scaled down to sizes comparable to the London penetration depth, ∼100 nm, and can enable ultrafast switching on the picosecond scale with ultralow energy per operation, ∼10-19 J.

ADVANCING MEMORY DENSITY: A NOVEL DESIGN FOR MULTIPLE-BIT-PER-CELL PHASE CHANGE MEMORY

Multiple-bit-per-cell phase-change memory (MPCM) has emerged as a promising solution to address the escalating demands for high-density, low-power, and fast-access memory in modern computing and data storage systems. This paper presents a novel device design aimed at enabling multiple bits per cell in phase-change memory, thereby significantly enhancing memory density while maintaining performance and reliability. Leveraging innovative material compositions and advanced fabrication techniques, the proposed design demonstrates the potential to push the boundaries of memory capacity, efficiency, and scalability. Through comprehensive simulation analysis and performance evaluations, we showcase the feasibility and advantages of the new device design, highlighting its potential to revolutionize memory architectures and meet the evolving needs of next-generation computing systems.

Impact of Program-Erase Operation Intervals at Different Temperatures on 3D Charge-Trapping Triple-Level-Cell NAND Flash Memory Reliability.

Three-dimensional charge-trapping (CT) NAND flash memory has attracted extensive attention owing to its unique merits, including huge storage capacities, large memory densities, and low bit cost. The reliability property is becoming an important factor for NAND flash memory with multi-level-cell (MLC) modes like triple-level-cell (TLC) or quad-level-cell (QLC), which is seriously affected by the intervals between program (P) and erase (E) operations during P/E cycles. In this work, the impacts of the intervals between P&E cycling under different temperatures and P/E cycles were systematically characterized. The results are further analyzed in terms of program disturb (PD), read disturb (RD), and data retention (DR). It was found that fail bit counts (FBCs) during the high temperature (HT) PD process are much smaller than those of the room temperature (RT) PD process. Moreover, upshift error and downshift error dominate the HT PD and RT PD processes, respectively. To improve the memory reliability of 3D CT TLC NAND, different intervals between P&E operations should be adopted considering the operating temperatures. These results could provide potential insights to optimize the lifetime of NAND flash-based memory systems.

High-density 3D-NAND flash with dual-string select line (D-SSL)

In this study, high-density 3D-NAND flash memory is proposed. Using D-SSL, lateral shrink of cell area can be achieved by distinguishing two strings in a word-line (WL). We verify erase/program and read characteristics using technology computer-aided design (TCAD) simulations with rigorous calibrated condition. The proposed high density NAND flash has normally-on state SSLs through additional process and electrical treatment. In the conventional reference NAND flash, the cell string connected to the bit-line (BL) is distinguished by WL cut (WLC). On the other hand, in the proposed high density NAND flash, the cell string is selected by utilizing the normally-on state SSLs using trapped hole and doped arsenic at the intersection of SSL1 and SSL2. Compared with the conventional scheme, the proposed D-SSL exhibits almost same erase/program and read characteristics. Consequently, the proposed D-SSL scheme can increase memory density with reduced number of WLCs by distinguishing strings using D-SSL.

First-principles investigation of near-field energy transfer between localized quantum emitters in solids

We present a predictive and general approach to investigate near-field energy transfer processes between localized defects in semiconductors, which couples first-principles electronic structure calculations and a nonrelativistic quantum electrodynamics description of photons in the weak-coupling regime. The approach is general and can be readily applied to investigate broad classes of defects in solids. We apply our approach to investigate an exemplar point defect in an oxide, the F center in MgO, and we show that the energy transfer from a magnetic source, e.g., a rare-earth impurity, to the vacancy can lead to spin nonconserving long-lived excitations that are dominant processes in the near field, at distances relevant to the design of photonic devices and ultrahigh dense memories. We also define a descriptor for coherent energy transfer to predict geometrical configurations of emitters to enable long-lived excitations, that are useful to design optical memories in semiconductor and insulators. Published by the American Physical Society 2024

Recent Progress in Memrsitor Array Structures and Solutions for Sneak Path Current Reduction

AbstractMemristors have diverse potential for improving data storage through linear memory control and synaptic operation in AI and neuromorphic computing. Prior research on optimizing memristors in next‐generation devices has generally indicated that emerging arrays and vertical structures can improve memory density, although special fabrication steps are required to realize improved operation. Until now, many obstructions, such as the sneak path current and forming processes from the initial device in array structure operation at the device level, have limited the development of array‐based memristor devices for further progressing circuits and integrated design. In this paper, memristor array studies are examined that have suggested solutions for sneak path current and forming operation problems at the device level. Ultimately, representative solutions are proposed to progress memristors into array structures by introducing the latest research on one diode‐one RRAM (1D1R), one selector‐one RRAM (1S1R), overshoot suppressed RRAM (OSRRAM), self‐rectifying cell (SRC), charge trap memory (CTM) and their applications. Additionally, essential details demonstrating the practical implementation of these devices in crossbar array memory are investigated. Finally, the advantages and perspectives of these array‐based memristor solutions are summarized.

Enhancing end-to-end control in autonomous driving through kinematic-infused and visual memory imitation learning

This paper presents an exploration, study, and comparison of various alternatives to enhance the capabilities of an end-to-end control system for autonomous driving based on imitation learning by adding visual memory and kinematic input data to the deep learning architectures that govern the vehicle. The experimental comparison relies on fundamental error metrics (MAE, MSE) during the offline assessment, supplemented by several external complementary fine-grain metrics based on the behavior of the ego vehicle at several urban test scenarios in the CARLA reference simulator in the online evaluation. Our study focuses on a lane-following application using different urban scenario layouts and visual bird-eye-view input. The memory addition involves architectural modifications and different sensory input types. The kinematic data integration is managed with a modified input. The experiments encompass both typical driving scenarios and extreme never-seen conditions. Additionally, we conduct an ablation study examining various memory lengths and densities. We prove experimentally that incorporating visual memory capabilities and kinematic input data makes the driving system more robust and able to handle a wider range of challenging situations, including those not encountered during training, in terms of reduction of collisions and speed self-regulation, resulting in a 75% enhancement. All the work we present here, including model architectures, trained model weights, comparison tool, and the dataset, is open-source, facilitating replication and extension of our findings.

Simultaneous resistance switching and rectifying effects in a single hybrid perovskite

AbstractHalide perovskites with naturally coupled electron‐ion dynamics hold great potential for nonvolatile memory applications. Self‐rectifying memristors are promising as they can avoid sneak currents and simplify device configuration. Here we report a self‐rectifying memristor firstly achieved in a single perovskite (NHCINH3)3PbI5 (abbreviated as (IFA)3PbI5), which is sandwiched by Ag and ITO electrodes as the simplest cell in a crossbar array device configuration. The iodide ions of (IFA)3PbI5 can be easily activated, of which the migration in the bulk contributes to the resistance hysteresis and the reaction with Ag at the interface contributes to the spontaneous formation of AgI. The perfect combination of n‐type AgI and p‐type (IFA)3PbI5 gives rise to the rectification function like a p–n diode. Such a self‐rectifying memristor exhibits the record‐low set power consumption and voltage. This work emphasizes that the multifunction of ions in perovskites can simplify the fabrication procedure, decrease the programming power, and increase the integration density of future memory devices.image

The immunological milieu of oropharyngeal squamous cell carcinoma (OPC) according to HPV and smoking status and its influence on prognosis.

3147 Background: OPC exhibits distinct epidemiological, genetic, and clinical characteristics, significantly influenced by the presence of HPV. Despite multidisciplinary therapy, approximately 40% of OPC patients (pts) experience recurrence, and those who achieve cure often endure late and morbid sequelae of treatment. In this study, we comprehensively explored the composition and functional status of the tumor immune microenvironment (TIME) in a cohort of OPC pts treated with curative intent and investigated TIME differences based on HPV infection, smoking status, and clinical outcomes. Methods: Two tissue microarrays with baseline OPC tissues were stained with a previously validated panel containing 33 metal-tagged markers. Imaging mass cytometry was performed with the Fluidigm Helios CyTOF instrument, and analysis performed with Visiopharm software. HPV status was determined by p16 IHC and/or DNA testing. Comparison of cell types and markers densities among subgroups (HPV positive vs negative) were analyzed with Wilcoxon signed-rank test and its association with clinical outcomes (recurrence vs no-recurrence) with log-rank and Cox Proportional Hazards. Multiple comparisons were corrected by Benjamini Hochberg procedure. Results: A total of 99 samples from 61 pts from a single institution were included in this study. The majority of pts were male (N=47, 77%) and heavy smokers (N=55, 90%); 72% were HPV-negative. The median age was 61 years (range, 39 – 87 years), and 52% of cases were diagnosed with stages III-IVB (AJCC 8th edition). Most pts (57%) was treated with surgery (±RT). Thirty-three pts (54%) experienced recurrent disease. Smoking, stage III-IVB, and HPV-negative tumors were associated with higher recurrence rates (p <0.01). Regarding the OPC TIME, the most prevalent cell populations in the stroma were fibroblasts (22%), followed by T-cells (17%) and endothelial cells (10%). The stroma of HPV+ tumors exhibited higher densities of memory T cells (FDR < 0.01), B cells (FDR = 0.02), and T cells (FDR = 0.02) compared to HPV negative counterparts, which displayed an increased density of neutrophils (FDR = 0.04). The density of cancer stem cells was directly associated with smoking history (FDR<0.001), and an increased density of intra-tumoral B-cells was found in non-smokers (FDR=0.01). Higher intra-tumoral neutrophil density was linked to recurrent disease (FDR < 0.001). Conversely, higher densities of memory CD8+ T cells and non-M2 macrophages in the TIME were associated with a lower chance of recurrence (FDR < 0.1). Conclusions: The intricate analysis of the OPC TIME revealed significant differences according to HPV and smoking status and highlighted the prognostic role of neutrophils, memory CD8+ T cells, and non-M2 macrophages. These findings may guide the development of personalized therapeutic strategies for OPC.

Multi-Level Switching of Spin-Torque Ferromagnetic Resonance in 2D Magnetite.

2D magnetic materials hold substantial promise in information storage and neuromorphic device applications. However, achieving a 2D material with high Curie temperature (TC), environmental stability, and multi-level magnetic states remains a challenge. This is particularly relevant for spintronic devices, which require multi-level resistance states to enhance memory density and fulfil low power consumption and multi-functionality. Here, the synthesis of 2D non-layered triangular and hexagonal magnetite (Fe3O4) nanosheets are proposed with high TC and environmental stability, and demonstrate that the ultrathin triangular nanosheets show broad antiphase boundaries (bAPBs) and sharp antiphase boundaries (sAPBs), which induce multiple spin precession modes and multi-level resistance. Conversely, the hexagonal nanosheets display slip bands with sAPBs associated with pinning effects, resulting in magnetic-field-driven spin texture reversal reminiscent of "0" and "1" switching signals. In support of the micromagnetic simulation, direct explanation is offer to the variation in multi-level resistance under a microwave field, which is ascribed to the multi-spin texture magnetization structure and the randomly distributed APBs within the material. These novel 2D magnetite nanosheets with unique spin textures and spin dynamics provide an exciting platform for constructing real multi-level storage devices catering to emerging information storage and neuromorphic computing requirements.

Advanced Modeling and Simulation of Multilayer Spin-Transfer Torque Magnetoresistive Random Access Memory with Interface Exchange Coupling.

In advancing the study of magnetization dynamics in STT-MRAM devices, we employ the spin drift-diffusion model to address the back-hopping effect. This issue manifests as unwanted switching either in the composite free layer or in the reference layer in synthetic antiferromagnets-a challenge that becomes more pronounced with device miniaturization. Although this miniaturization aims to enhance memory density, it inadvertently compromises data integrity. Parallel to this examination, our investigation of the interface exchange coupling within multilayer structures unveils critical insights into the efficacy and dependability of spintronic devices. We particularly scrutinize how exchange coupling, mediated by non-magnetic layers, influences the magnetic interplay between adjacent ferromagnetic layers, thereby affecting their magnetic stability and domain wall movements. This investigation is crucial for understanding the switching behavior in multi-layered structures. Our integrated methodology, which uses both charge and spin currents, demonstrates a comprehensive understanding of MRAM dynamics. It emphasizes the strategic optimization of exchange coupling to improve the performance of multi-layered spintronic devices. Such enhancements are anticipated to encourage improvements in data retention and the write/read speeds of memory devices. This research, thus, marks a significant leap forward in the refinement of high-capacity, high-performance memory technologies.

High-Density, Nonvolatile, Flexible Multilevel Organic Memristor Using Multilayered Polymer Semiconductors.

Nonvolatile organic memristors have emerged as promising candidates for next-generation electronics, emphasizing the need for vertical device fabrication to attain a high density. Herein, we present a comprehensive investigation of high-performance organic memristors, fabricated in crossbar architecture with PTB7/Al-AlOx-nanocluster/PTB7 embedded between Al electrodes. PTB7 films were fabricated using the Unidirectional Floating Film Transfer Method, enabling independent uniform film fabrication in the Layer-by-Layer (LbL) configuration without disturbing underlying films. We examined the charge transport mechanism of our memristors using the Hubbard model highlighting the role of Al-AlOx-nanoclusters in switching-on the devices, due to the accumulation of bipolarons in the semiconducting layer. By varying the number of LbL films in the device architecture, the resistance of resistive states was systematically altered, enabling the fabrication of novel multilevel memristors. These multilevel devices exhibited excellent performance metrics, including enhanced memory density, high on-off ratio (>108), remarkable memory retention (>105 s), high endurance (87 on-off cycles), and rapid switching (∼100 ns). Furthermore, flexible memristors were fabricated, demonstrating consistent performance even under bending conditions, with a radius of 2.78 mm for >104 bending cycles. This study not only demonstrates the fundamental understanding of charge transport in organic memristors but also introduces novel device architectures with significant implications for high-density flexible applications.

Multi-level phase-change behaviors of Ge2Sb2Te5/Sb7Se3 bilayer films and a design rule of multi-level phase-change films

Multi-level phase-change (MLPC) memory can not only further raise the storage density of high-density phase-change memory, but also have significant applications in neuromorphic computing. However, acquisitions of MLPC materials are very challenging. MLPC is only observed in a small number of phase-change materials and can’t be designed determinately because the mechanism of MLPC emergence has been understood incompletely. Here, we investigate phase-change behaviors of six Ge2Sb2Te5(x nm)/Sb7Se3 (60-x nm) bilayer films with x = 5, 10, 15, 20, 35, and 50. Three-level phase-change memory is observed for five bilayer films with x < 50, but not for one with x = 50, revealing the dependence of MLPC memory on the thickness ratio of two constituent layers composing bilayer films. A parallel resistance model is proposed to describe the measured sheet resistance of bilayer films and can be used to explain the thickness-ratio dependence of three-level phase-change memory very well. Subsequently, a design rule for MLPC memory films is given out unambiguously. Based on the parallel resistance model, MLPC memories are simulated for bilayer and trilayer films. Three-level memory indeed appears in bilayer films only when the design rule is met, confirming the validity of our design rule. Four-level memory is predicted in trilayer films as the design rule is obeyed.

Abstract NG07: Association between somatic microsatellite instability, hypermutation status, and specific T cell subsets in colorectal cancer tumors

Abstract NG07: Association between somatic microsatellite instability, hypermutation status, and specific T cell subsets in colorectal cancer tumors

Replica symmetry breaking in supervised and unsupervised Hebbian networks

Abstract Hebbian neural networks with multi-node interactions, often called Dense Associative Memories, have recently attracted considerable interest in the statistical mechanics community, as they have been shown to outperform their pairwise counterparts in a number of features, including resilience against adversarial attacks, pattern retrieval with extremely weak signals and supra-linear storage capacities. However, their analysis has so far been carried out within a replica-symmetric theory. In this manuscript, we relax the assumption of replica symmetry and analyse these systems at one step of replica-symmetry breaking, focusing on two different prescriptions for the interactions that we will refer to as supervised and unsupervised learning. We derive the phase diagram of the model using two different approaches, namely Parisi's hierarchical ansatz for the relationship between different replicas within the replica approach, and the so-called telescope ansatz within Guerra's interpolation method: our results show that replica-symmetry breaking does not alter the threshold for learning and slightly increases the maximal storage capacity. Further, we also derive analytically the instability line of the replica-symmetric theory, using a generalization of the De Almeida and Thouless approach.