Intrinsically Stretchable Floating Gate Memory Transistors for Data Storage of Electronic Skin Devices.

To propel electronic skin (e-skin) to the next level by integrating artificial intelligence features with advanced sensory capabilities, it is imperative to develop stretchable memory device technology. A stretchable memory device for e-skin must offer, in particular, long-term data storage while ensuring the security of personal information under any type of deformation. However, despite the significance of these needs, technology related to stretchable memory devices remains in its infancy. Here, we report an intrinsically stretchable floating gate (FG) polymer memory transistor. The device features a dual-stimuli (optical and electrical) writing system to prevent easy erasure of recorded data. An FG comprising an intermixture of Ag nanoparticles and elastomer and with proper energy-band alignment between the semiconductor and dielectric facilitated sustainable memory performance, while achieving a high memory on/off ratio (>105) and a long retention time (106 s) with the ability to withstand 50% uniaxial or 30% biaxial strain. In addition, our memory transistor exhibited high mechanical durability over multiple stretching cycles (1000 times), along with excellent environmental stability with respect to factors such as temperature, moisture, air, and delamination. Finally, we fabricated a 7 × 7 active-matrix memory transistor array for personalized storage of e-skin data and successfully demonstrated its functionality.

Enhanced Resistive Switching and Synaptic Characteristics of ALD Deposited AlN-Based RRAM by Positive Soft Breakdown Process.

Nitride film played an essential role as an excellent diffusion barrier in the semiconductor field for several decades. In addition, interest in next-generation memories induced researchers' attention to nitride film as a new storage medium. A Pt/AlN/TaN device was investigated for resistive random-access memory (RRAM) application in this work. Resistive switching properties were examined in the AlN thin film formed by atomic layer deposition (ALD). The unique switching feature conducted under the positive voltage was investigated, while the typical bipolar switching was conducted under the application of negative voltage. Good retention and DC, and pulse endurances were achieved in both conditions and compared to the memory performances. Finally, the electronic behaviors based on the unique switching feature were analyzed through X-ray photoelectron spectroscopy (XPS) and the current-voltage (I-V) linear fitting model.

Controlling Native Oxidation of HfS2 for 2D Materials Based Flash Memory and Artificial Synapse.

Two-dimensional (2D) materials based artificial synapses are important building blocks for the brain-inspired computing systems that are promising in handling large amounts of informational data with high energy-efficiency in the future. However, 2D devices usually rely on deposited or transferred insulators as the dielectric layer, resulting in various challenges in device compatibility and fabrication complexity. Here, we demonstrate a controllable and reliable oxidation process to turn 2D semiconductor HfS2 into native oxide, HfOx, which shows good insulating property and clean interface with HfS2. We then incorporate the HfOx/HfS2 heterostructure into a flash memory device, achieving a high on/off current ratio of ∼105, a large memory window over 60 V, good endurance, and a long retention time over 103 seconds. In particular, the memory device can work as an artificial synapse to emulate basic synaptic functions and feature good linearity and symmetry in conductance change during long-term potentiation/depression processes. A simulated artificial neural network based on our synaptic device achieves a high accuracy of ∼88% in MNIST pattern recognition. Our work provides a simple and effective approach for integrating high-k dielectrics into 2D material-based memory and synaptic devices.

Self-Assembled Peptide Nanofibers with Voltage-Regulated Inverse Photoconductance.

Inverse photoconductance is an uncommon phenomenon observed in selective low-dimensional materials, in which the electrical conductivity of the materials decreases under light illumination. The unique material property holds great promise for biomedical applications in photodetectors, photoelectric logic gates, and low-power nonvolatile memory, which remains a daunting challenge. Especially, tunable photoconductivity for biocompatible materials is highly desired for interfacing with biological systems but is less explored in organic materials. Here, we report nanofibers self-assembled with cyclo-tyrosine-tyrosine (cyclo-YY) having voltage-regulated inverse photoconductance and photoconductance. The peptide nanofibers can be switched back and forth by a bias voltage for imitating biological sensing in artificial vision and memory devices. A peptide optoelectronic resistive random access memory (PORRAM) device has also been fabricated using the nanofibers that can be electrically switched between long-term and short-term memory. The underlying mechanism of the reversible photoconductance is discussed in this paper. Due to the inherent biocompatibility of peptide materials, the reversible photoconductive nanofibers may have broad applications in sensing and storage for biotic and abiotic interfaces.

Performance of parallel FDTD method for shared- and distributed-memory architectures: Application tobioelectromagnetics.

This work provides an in-depth computational performance study of the parallel finite-difference time-domain (FDTD) method. The parallelization is done at various levels including: shared- (OpenMP) and distributed- (MPI) memory paradigms and vectorization on three different architectures: Intel's Knights Landing, Skylake and ARM's Cavium ThunderX2. This study contributes to prove, in a systematic manner, the well-established claim within the Computational Electromagnetic community, that the main factor limiting FDTD performance, in realistic problems, is the memory bandwidth. Consequently a memory bandwidth threshold can be assessed depending on the problem size in order to attain optimal performance. Finally, the results of this study have been used to optimize the workload balancing of simulation of a bioelectromagnetic problem consisting in the exposure of a human model to a reverberation chamber-like environment.

DNA Micro-Disks for the Management of DNA-Based Data Storage with Index and Write-Once-Read-Many (WORM) Memory Features.

DNA-based data storage has attracted attention because of its higher physical density of the data and longer retention time than those of conventional digital data storage. However, previous DNA-based data storage lacked index features and the data quality of storage after a single access was not preserved, obstructing its industrial use. Here, DNA micro-disks, QR-coded micro-sized disks that harbor data-encoded DNA molecules for the efficient management of DNA-based data storage, are proposed. The two major features that previous DNA-based data-storage studies could not achieve are demonstrated. One feature is accessing data items efficiently by indexing the data-encoded DNA library. Another is achieving write-once-read-many (WORM) memory through the immobilization of DNA molecules on the disk and their enrichment through in situ DNA production. Through these features, the reliability of DNA-based data storage is increased by allowing selective and multiple accession of data-encoded DNA with lower data loss than previous DNA-based data storage methods.

Programming Escherichia coli to function as a digital display.

Synthetic genetic circuits offer the potential to wield computational control over biology, but their complexity is limited by the accuracy of mathematical models. Here, we present advances that enable the complete encoding of an electronic chip in the DNA carried by Escherichia coli (E. coli). The chip is a binary-coded digit (BCD) to 7-segment decoder, associated with clocks and calculators, to turn on segments to visualize 0-9. Design automation is used to build seven strains, each of which contains a circuit with up to 12 repressors and two activators (totaling 63 regulators and 76,000 bp DNA). The inputs to each circuit represent the digit to be displayed (encoded in binary by four molecules), and output is the segment state, reported as fluorescence. Implementation requires an advanced gate model that captures dynamics, promoter interference, and a measure of total power usage (RNAP flux). This project is an exemplar of design automation pushing engineering beyond that achievable "by hand", essential for realizing the potential of biology.

HMB in DRAM-less NVMe SSDs: Their usage and effects on performance.

Solid-state drives (SSDs) that do not have internal dynamic random-access memory (DRAM) are being widely spread for client SSD and embedded SSD markets in recent years because they are cheap and consume less power. Obviously, their performance is lower than conventional SSDs because they cannot exploit advantages of DRAM in the controller. However, this problem can be alleviated by using host memory buffer (HMB) feature of Non-Volatile Memory Express (NVMe), which allows SSDs to utilize the DRAM of host. In this paper, we show that commercial DRAM-less SSDs clearly exhibit worse I/O performance than SSDs with internal DRAM, but this can be improved by using the HMB feature. We also present methods that reveal how the host memory buffer is used in commercial DRAM-less SSDs to improve I/O performance. Through extensive experiments, we conclude that DRAM-less SSDs evaluated in this study mainly exploit the host memory buffer as an address mapping table cache rather than a read cache or write buffer to improve I/O performance.

Non-Volatile Transistor Memory with a Polypeptide Dielectric.

Organic nonvolatile transistor memory with synthetic polypeptide derivatives as dielectric was fabricated by a solution process. When only poly (γ-benzyl-l-glutamate) (PBLG) was used as dielectric, the device did not show obvious hysteresis in transfer curves. However, PBLG blended with PMMA led to a remarkable increase in memory window up to 20 V. The device performance was observed to remarkably depend on the blend ratio. This study suggests the crystal structure and the molecular alignment significantly affect the electrical performance in transistor-type memory devices, thereby provides an alternative to prepare nonvolatile memory with polymer dielectrics.

Organic-Inorganic Nanocomposite Film for High-Performance Stretchable Resistive Memory Device.

A compatible organic/inorganic nanocomposite film for a stretchable resistive memory device with high performance is demonstrated using poly(4-vinylpyridine)-block-poly(propyl methacrylate) (P4VP-b-PPMA) with zinc oxide (ZnO) nanoparticle. The PPMA soft segment is designed for reducing the rigidity of the active layer, while the P4VP block serves as a charge-trapping component to induce conductive filament and also a compatible moiety for inorganic nanoparticles through hydrogen bonding. The experimental results show that the P4VP-b-PPMA-based electrical memory device exhibits write-once-read-many-times memory behavior and an excellent ON/OFF current ratio of over 105 with a stable turn-on voltage (Vset ) around -2.0 V and stable memory behavior upon stretching up to 60% strain. On the other hand, P4VP-b-PPMA/ZnO nanocomposite film switches the memory characteristic to the dynamic random access memory behavior. The stretchable memory device prepared from the nanocomposite film can have a stretching durability over 40% strain and up to 1000 times cycling stretch-relaxation test. This work demonstrates a new strategy using nanocomposite films with tunable electrical characteristics and enhanced mechanical properties for stretchable electrical devices.

Detection of bovine serum albumin using hybrid TiO2 + graphene oxide based Bio - resistive random access memory device.

Bio - molecules detection and their quantification with a high precision is essential in modern era of medical diagnostics. In this context, the memristor device which can change its resistance state is a promising technique to sense the bio - molecules. In this work, detection of the Bovine Serum Albumin (BSA) protein using resistive switching memristors based on TiO2 and TiO2 + graphene oxide (GO) is explored. The sensitivity of BSA detection is found to be 4 mg/mL. Both the devices show an excellent bipolar resistive switching with an on/off ratio of 73 and 100 respectively, which essentially demonstrates that the device with GO, distinguishes the resistance states with a high precision. The enhanced performance in the GO inserted device (~ 650 cycles) is attributed to the prevention of multi-dimensional and random growth of conductive paths.

Logic Gate Functions Built with Nonvolatile Resistive Switching and Thermoresponsive Memory Based on Biologic Proteins.

Logic gate functions built with nonvolatile resistive switching and thermoresponsive memory based on biologic proteins were investigated. The "NAND" and "NOR" functions of logic gates in soya protein devices have been built at room temperature by their nonvolatile ternary WORM resistive switching behaviors. Furthermore, heating the devices from room temperature to 358 K results in a switch from tristable state to bistable state WORM resistive switching behavior, indicating that the thermoresponsiveness can be efficiently memorized. The biologic transient nonvolatile memory device consisting of soya protein is illustrated. This device exhibits a long data retention time (104 s) and significant HRS/LRS ratio (∼105); the transient response of the current to voltage of an as-fabricated device is also explored. The soya protein based memory device on a gelatin film substrate is also assessed to validate the feasibility of degradation and biological compatibility for the implantable biological electronic device, that is, innoxious and avirulent to the human body. This can offer alternative avenues for exploring prospective bioelectronic devices.

SwiftOrtho: A fast, memory-efficient, multiple genome orthology classifier.

BACKGROUND: Gene homology type classification is required for many types of genome analyses, including comparative genomics, phylogenetics, and protein function annotation. Consequently, a large variety of tools have been developed to perform homology classification across genomes of different species. However, when applied to large genomic data sets, these tools require high memory and CPU usage, typically available only in computational clusters. FINDINGS: Here we present a new graph-based orthology analysis tool, SwiftOrtho, which is optimized for speed and memory usage when applied to large-scale data. SwiftOrtho uses long k-mers to speed up homology search, while using a reduced amino acid alphabet and spaced seeds to compensate for the loss of sensitivity due to long k-mers. In addition, it uses an affinity propagation algorithm to reduce the memory usage when clustering large-scale orthology relationships into orthologous groups. In our tests, SwiftOrtho was the only tool that completed orthology analysis of proteins from 1,760 bacterial genomes on a computer with only 4 GB RAM. Using various standard orthology data sets, we also show that SwiftOrtho has a high accuracy. CONCLUSIONS: SwiftOrtho enables the accurate comparative genomic analyses of thousands of genomes using low-memory computers. SwiftOrtho is available at https://github.com/Rinoahu/SwiftOrtho.

Taming Performance Variability of Healthcare Data Service Frameworks with Proactive and Coarse-Grained Memory Cleaning.

This article explores the performance optimizations of an embedded database memory management system to ensure high responsiveness of real-time healthcare data frameworks. SQLite is a popular embedded database engine extensively used in medical and healthcare data storage systems. However, SQLite is essentially built around lightweight applications in mobile devices, and it significantly deteriorates when a large transaction is issued such as high resolution medical images or massive health dataset, which is unlikely to occur in embedded systems but is quite common in other systems. Such transactions do not fit in the in-memory buffer of SQLite, and SQLite enforces memory reclamation as they are processed. The problem is that the current SQLite buffer management scheme does not effectively manage these cases, and the naïve reclamation scheme used significantly increases the user-perceived latency. Motivated by this limitation, this paper identifies the causes of high latency during processing of a large transaction, and overcomes the limitation via proactive and coarse-grained memory cleaning in SQLite.The proposed memory reclamation scheme was implemented in SQLite 3.29, and measurement studies with a prototype implementation demonstrated that the SQLite operation latency decreases by 13% on an average and up to 17.3% with our memory reclamation scheme as compared to that of the original version.

Multi-Path Dilated Residual Network for Nuclei Segmentation and Detection.

As a typical biomedical detection task, nuclei detection has been widely used in human health management, disease diagnosis and other fields. However, the task of cell detection in microscopic images is still challenging because the nuclei are commonly small and dense with many overlapping nuclei in the images. In order to detect nuclei, the most important key step is to segment the cell targets accurately. Based on Mask RCNN model, we designed a multi-path dilated residual network, and realized a network structure to segment and detect dense small objects, and effectively solved the problem of information loss of small objects in deep neural network. The experimental results on two typical nuclear segmentation data sets show that our model has better recognition and segmentation capability for dense small targets.

Fabrication of carboxymethyl cellulose and graphene oxide bio-nanocomposites for flexible nonvolatile resistive switching memory devices.

Nowadays the development of natural biomaterials as promising building polymers for flexible, biodegradable, biocompatible and environmentally friendly electronic devices is of great interest. As the most common natural polymers, cellulose and its derivatives have the potential to be applied in the devices owing to the easy processing, nontoxicity and biodegradability. Here, write-once-read-many-times resistive switching devices based on biodegradable carboxymethyl cellulose-graphene oxide (CMC-GO) nanocomposite are demonstrated for the first time. The hybridization sites formed by the gelation of CMC and GO molecules contribute to the excellent memory behaviors. When compared with devices base on pure GO and CMC, the device with the Al/CMC-GO/Al/SiO2 structure exhibits brilliant write-once-read-many-times (WORM) switching characteristics such as high ON/OFF current ratio of ˜105, low switching voltage of 2.22 V, excellent stability and durability. What's more, the device shows high flexibility and good resistive switching behaviors even with soft PET substrate (Al/CMC-GO/Al/PET structure). This newly designed cellulose-graphene oxide-based polymer nanocomposites are quite cheap and easy processed for large scale manufacturing of memory devices and can further contribute to future biodegradable data storage applications such as portable stretchable displays, wearable electronics and electronic skins in the coming age of artificial intelligence.

Featherweight long read alignment using partitioned reference indexes.

The advent of Nanopore sequencing has realised portable genomic research and applications. However, state of the art long read aligners and large reference genomes are not compatible with most mobile computing devices due to their high memory requirements. We show how memory requirements can be reduced through parameter optimisation and reference genome partitioning, but highlight the associated limitations and caveats of these approaches. We then demonstrate how these issues can be overcome through an appropriate merging technique. We incorporated multi-index merging into the Minimap2 aligner and demonstrate that long read alignment to the human genome can be performed on a system with 2 GB RAM with negligible impact on accuracy.

Estimating the Memory Order of Electrocorticography Recordings.

OBJECTIVE: This paper presents a data-driven method for estimating the memory order (the average length of the statistical dependence of a given sample on previous samples) of a recorded electrocorticography (ECoG) sequence. METHODS: The proposed inference method is based on the relationship between the loss in predicting the next sample in a time-series and the dependence of this sample on the previous samples. Specifically, the memory order is estimated to be the number of past samples that minimize the least squares error (LSE) in predicting the next sample. To deal with the lack of an analytical model for ECoG recordings, the proposed method combines a collection of different predictors, thereby achieving LSE at least as low as the LSE achieved by each of the different predictors. RESULTS: ECoG recordings from six patients with epilepsy were analyzed, and the empirical cumulative density functions (ECDFs) of the memory orders estimated from these recordings were generated, for rest as well as pre-ictal time intervals. For pre-ictal time intervals, the electrodes corresponding to the seizure-onset-zone were separately analyzed. The estimated ECDFs were different between patients and between different types of blocks. For all the analyzed patients, the estimated memory orders were on the order of tens of milliseconds (up to 100 ms). SIGNIFICANCE: The proposed method facilitates the estimation of the causal associations between ECoG recordings, as these associations strongly depend on the recordings' memory. An improved estimation of causal associations can improve the performance of algorithms that use ECoG recordings to localize the epileptogenic zone. Such algorithms can aid doctors in their pre-surgical planning for the surgery of patients with epilepsy.

Impact of the precursor chemistry and process conditions on the cell-to-cell variability in 1T-1R based HfO2 RRAM devices.

The Resistive RAM (RRAM) technology is currently in a level of maturity that calls for its integration into CMOS compatible memory arrays. This CMOS integration requires a perfect understanding of the cells performance and reliability in relation to the deposition processes used for their manufacturing. In this paper, the impact of the precursor chemistries and process conditions on the performance of HfO2 based memristive cells is studied. An extensive characterization of HfO2 based 1T1R cells, a comparison of the cell-to-cell variability, and reliability study is performed. The cells' behaviors during forming, set, and reset operations are monitored in order to relate their features to conductive filament properties and process-induced variability of the switching parameters. The modeling of the high resistance state (HRS) is performed by applying the Quantum-Point Contact model to assess the link between the deposition condition and the precursor chemistry with the resulting physical cells characteristics.

Reliability of Tidal Volume in Average Volume Assured Pressure Support Mode.

BACKGROUND: Remote monitoring is increasingly used in patients who receive home mechanical ventilation. The average volume assured pressure support mode is a target volume pressure preset mode that delivers a given tidal volume (VT) within a range of controlled inspiratory pressures. In a mode such as this, it is important to verify that the VT value retrieved from the ventilator SD card is accurate. METHODS: A lung model was set with C (Compliance) 0.075 L/cm H2O and RI (Inspiratory resistance)-RE (Expiratory resistance) 15-25 cm H2O/L/s (model 1) or with C 0.050 L/cm H2O and RI 6 cm H2O/L/s (model 2) and 6 cm H2O effort. Three home-care ventilators (A40, PrismaST30, and Vivo40) were set to average volume assured pressure support mode with 0.3 and 0.6 L VT each at PEEP 5 and 10 cm H2O, and were connected to the lung model with and without nonintentional leak. The reference airway pressure and flow were measured by a data logger. VT was expressed in body temperature and pressure saturated. We assessed the difference in VT between the ventilator SD card and a data logger relative to set VT and factors associated with its magnitude. RESULTS: For A40, PrismaST30, and Vivo40, the adjusted mean VT differences between the ventilator SD card and the data logger were -0.053 L (95% CI -0.067 to -0.039 L) (P < .001), -0.002 L (95%CI -0.022 to 0.019 L) (P = .86), and -0.067 L (95% CI -0.007 to 0.127 L) (P = .03), respectively. The partial Spearman correlation coefficients between the ventilator SD card and a data logger were 0.89 (P < .001), 0.59 (P < .001), and 0.78 (P < .001), respectively to the ventilators. The relative variations in measured VT from the set VT were 16.0, -12.0, and 6.7% for the ventilator SD card, and were -2.5, -7.5, and -27.2% for the data logger, respectively. The discrepancy in ventilator between SD card and data logger were influenced by PEEP for the PrismaST30 ventilator, nonintentional leak for the Vivo40 ventilator and PEEP, nonintentional leak, and underlying disease, the effect of each depending on the levels of the other factors, for the A40 ventilator. CONCLUSIONS: In the 3 home-care ventilators, the ventilator SD card underestimated VT. Factors involved in this difference differed among the ventilators.