Volatile Memory Research Articles

The Memory in Return Volatility: An Analysis of Mutual Fund Returns

ABSTRACTThis paper examines long memory in the return volatility in the cross‐section of U.S. mutual funds. Our results provide evidence of this phenomenon. Through univariate analysis, we find that the long memory in mutual fund return volatility is more pronounced than in stock return volatility. Additionally, the long memory estimate is negatively related to expected fund returns. Holding a long position in shorter‐term memory funds and a short position in longer‐term memory funds generates significant excess returns of 0.26% per month for value‐weighted portfolios.

Ultra-high endurance silicon photonic memory using vanadium dioxide

Silicon photonics arises as a viable solution to address the stringent resource demands of emergent technologies, such as neural networks. Within this framework, photonic memories are fundamental building blocks of photonic integrated circuits that have not yet found a standardized solution due to several trade-offs among different metrics such as energy consumption, speed, footprint, or fabrication complexity, to name a few. In particular, a photonic memory exhibiting ultra-high endurance performance (>106 cycles) has been elusive to date. Here, we report an ultra-high endurance silicon photonic volatile memory using vanadium dioxide (VO2) exhibiting a record cyclability of up to 107 cycles without degradation. Moreover, our memory features an ultra-compact footprint below 5 µm with the potential for nanosecond and picojoule programming performance. Our silicon photonic memory could find application in emerging photonic applications demanding a high number of memory updates, such as photonic neural networks with in situ training.

Diffusive Memristor with CuS Nanoparticles Embedded in Polymeric Film as Artificial Nociceptor.

The threshold behavior and the ion diffusion dynamics in diffusive volatile memristors have a very uncanny resemblance to the transduction process of biological nociceptors. Hence, the diffusive memristors are considered the most suited for making artificial nociceptive systems. To facilitate their widespread adoption, it is imperative to develop polymeric or organic-inorganic hybrid material-based diffusive memristors that are economical, biocompatible, and easily processable. In this study, we present a cluster-type polymeric diffusive memristor where copper is used as the active top electrode. The switching medium comprises copper(II) sulfide (CuS) nanoparticles embedded in poly(ethylene oxide) (PEO). The devices show electrochemical metalization (ECM)-type and bidirectional diffusive volatile memory with high nonlinearity (104) and turn-on slope (5.6 mV/dec). They reliably remain diffusive volatile with up to 10 wt % CuS in PEO and for a wide range of compliance (10-6 to 10-2 A) without transitioning to the bipolar nonvolatile type. The low reduction potential of CuS and optimal segmental dynamics of PEO work synergistically to ensure stable and reproducible diffusive memory. The CuS nanoparticles act as bipolar electrodes, undergoing local oxidation and reduction under the influence of the bias. The switching of resistance states in the CuS-PEO memristors is attributed to the formation of cluster-type filaments between CuS nanoparticles within the PEO matrix supported by the participation of copper ions from the top Cu electrode. The observation of low filament temperature and the independence of on-state resistance with respect to the device area and temperature further corroborate the cluster-type filament in CuS-PEO memristors. Using a 5 wt % CuS-based device, an artificial nociceptor is realized, which successfully mimics most of the nociceptive plasticities such as threshold, relaxation, no adaptation, and sensitization.

Synthesis and Anisotropic Memristive Behavior of Borophene Nanosheets

Neuromorphic computing, marked by its parallel computational abilities and low power usage, has become pivotal in advancing artificial intelligence. However, the advancement of neuromorphic computing has faced significant obstacles due to the performance limitations of traditional memory devices struggling with high power consumption and limited reliability. Two‐dimensional (2D) materials have been extensively investigated as high‐performance memristive materials, but they are often restricted by fixed memristive properties, which complicate circuit design and limit flexibility. Here, we report that multilayer borophene nanosheets represent a breakthrough material, displaying anisotropic variable memristive properties. The nanosheets, comprising semiconductor α’‐4H‐borophene sheets and metal β12‐borophene sheets, have been synthesized on aluminum foil surface through chemical vapor deposition (CVD) method. The multilayer borophene nanosheets exhibit volatile memory behavior in the vertical direction and non‐volatile memory behavior in the planar direction. This innovative class of 2D nanosheets not only overcomes the limitations of conventional memory devices but also expands the potential applications of borophene‐based memories in information storage and in‐memory computing.

Synthesis and Anisotropic Memristive Behavior of Borophene Nanosheets.

Neuromorphic computing, marked by its parallel computational abilities and low power usage, has become pivotal in advancing artificial intelligence. However, the advancement of neuromorphic computing has faced significant obstacles due to the performance limitations of traditional memory devices struggling with high power consumption and limited reliability. Two-dimensional (2D) materials have been extensively investigated as high-performance memristive materials, but they are often restricted by fixed memristive properties, which complicate circuit design and limit flexibility. Here, we report that multilayer borophene nanosheets represent a breakthrough material, displaying anisotropic variable memristive properties. The nanosheets, comprising semiconductor α'-4H-borophene sheets and metal β12-borophene sheets, have been synthesized on aluminum foil surface through chemical vapor deposition (CVD) method. The multilayer borophene nanosheets exhibit volatile memory behavior in the vertical direction and non-volatile memory behavior in the planar direction. This innovative class of 2D nanosheets not only overcomes the limitations of conventional memory devices but also expands the potential applications of borophene-based memories in information storage and in-memory computing.

Tunable sub-threshold current firing via insulator-to-metal transition enabled by lithographic nanochannels for neuromorphic applications

The electric field-driven insulator-to-metal transition (IMT) offers a promising platform for developing controllable, futuristic neuromorphic nanoelectronics. However, the volatile nature of IMT, typically stimulated by a specific threshold voltage, limits its potential use primarily to switch-like applications. To broaden its applications, including in-material data processing, achieving on-demand IMT activation with dynamic memory capability is essential. This study demonstrates on-demand modulation of IMT behavior using spatially confined VO2 nanochannels, designed by local probe lithography. This approach enables the integration of ultrafast (∼180 ns) volatile switches (on/off ratio >103) and memory storage, from short- to long-term, in a single device. Notably, the threshold voltage was effectively reduced from 5.6 V to 2.8 V by precisely modulating the width of spatially embedded VO2 nanochannels. The observed memory behavior is attributed to persistent metallic domains and preferential IMT along these channels, as confirmed by optical and Kelvin probe force microscopy. Furthermore, the ability to classify input patterns, even in the presence of noise, was demonstrated using interconnected coplanar nanochannels by leveraging the short-term memory characteristics of the IMT. This report marks a significant step towards on-demand nanoscale manipulation of the IMT dynamics, laying the groundwork for ultrasmall, high-speed, and energy-efficient conventional and neuromorphic nanoelectronics.

Exploring Calendar Anomalies and Volatility Dynamics in Cryptocurrencies: A Comparative Analysis of Day-of-the-Week Effects before and during the COVID-19 Pandemic

This study investigates calendar anomalies and their impact on returns and volatility patterns in the cryptocurrency market, focusing on day-of-the-week effects before and during the COVID-19 pandemic. Using advanced statistical models from the GARCH family, we analyze the returns of Binance USD, Bitcoin, Binance Coin, Cardano, Dogecoin, Ethereum, Solana, Tether, USD Coin, and Ripple. Our findings reveal significant shifts in volatility dynamics and day-of-the-week effects on returns, challenging the notion of market efficiency. Notably, Bitcoin and Solana began exhibiting day-of-the-week effects during the pandemic, whereas Cardano and Dogecoin did not. During the pandemic, Binance USD, Ethereum, Tether, USD Coin, and Ripple showed multiple days with significant day-of-the-week effects. Notably, positive returns were generally observed on Sundays, whereas a shift to negative returns on Mondays was evident during the COVID-19 period. These patterns suggest that exploitable anomalies persist despite the market’s continuous operation and increasing maturity. The presence of a long-term memory in volatility highlights the need for robust trading strategies. Our research provides valuable insights for investors, traders, regulators, and policymakers, aiding in the development of effective trading strategies, risk management practices, and regulatory policies in the evolving cryptocurrency market.

(Invited) Towards High-Performance Domain-Specific in-Sensor and in-Memory Accelerators

Internet of Things (IoT) devices are projected to attain an $1100B market by 2025, with a web of interconnection projected to comprise approximately 75+ billion IoT devices. The large number of IoTs consist of sensory systems that enable massive data collection from the environment and people. However, considerable portions of the captured sensory data are redundant and unstructured. Data conversion of such large raw data, storing in volatile memories, transmission, and computation in on-/off-chip processors, impose high energy consumption, latency, and a memory bottleneck at the edge. Therefore, high-speed, low-power, and normally-off computing domain-specific architectures should be explored and developed to overcome these issues. Motivated by the aforementioned concerns, we will be focusing on cross-layer (device/circuit/architecture/application) co-design of energy-efficient and high-performance processing-in-sensor and processing-in-memory platforms for implementing complex AI and machine learning tasks, bioinformatics tasks, graph processing, etc. We explain how to leverage innovations from circuits and architecture to integrate sensors, memory, and logic to break the existing memory and power walls.

Volatile and Nonvolatile Programmable Iontronic Memristor with Lithium Imbued TiOx for Neuromorphic Computing Applications.

We demonstrate a lithium (Li) imbued TiOx iontronic device that exhibits synapse-like short-term plasticity behavior without requiring a forming process beforehand or a compliance current during switching. A solid-state electrolyte lithium phosphorus oxynitride (LiPON) behaves as the ion source, and the embedding and releasing of Li ions inside the cathodic like TiOx renders volatile conductance responses from the device and offers a natural platform for hardware simulating neuron functionalities. Besides, these devices possess high uniformity and great endurance as no conductive filaments are present. Different short-term pulse-based phenomena, including paired pulse facilitation, post-tetanic potentiation, and spike rate-dependent plasticity, were observed with self-relaxation characteristics. Based on the voltage excitation period, the time scale of the volatile memory can be tuned. Temperature measurement reveals the ion displacement-induced conductance channels become frozen below 220 K. In addition, the volatile analog devices can be configured into nonvolatile memory units with multibit storage capabilities after an electroforming process. Therefore, on the same platform, we can configure volatile units as nonlinear dynamic reservoirs for performing neuromorphic training and the nonvolatile units as the weight storage layer. We proceed to use voice recognition as an example with the tunable time constant relationship and obtain 94.4% accuracy with a minimal training data set. Thus, this iontronic platform can effectively process and update temporal information for reservoir and neuromorphic computing paradigms.

In-Sensor Organic Electrochemical Transistor for the Multimode Neuromorphic Olfactory System.

The olfactory system is one of the six basic sensory nervous systems. Developing artificial olfactory systems is challenging due to the complexity of chemical information decoding and memory. Conventional chemical sensors can convert chemical signals into electric signals to decode gas information but they lack memory functions. Additional storage and processing units would significantly increase the complexity and power consumption of the devices, especially for portable and wearable devices. Here, an olfactory-inspired in-sensor organic electrochemical transistor (OI-OECT) is proposed, with the integrated functions of chemical information decoding, tunable memory level, and selectivity of vapor sensing. The ion-gel electrolyte endows the OI-OECT with the function of tunable memory levels and a low operating voltage. Typical synaptic behaviors, including inhibitory postsynaptic current and paired-pulse facilitations, are successfully achieved. Importantly, the gas memory level can be effectively modulated by the gate voltages (0 and -1 V), which realized the transformation of volatile and nonvolatile memory. Furthermore, benefiting from the recognition of multiple gases and ability to detect cumulative damage caused by gases, the OI-OECT is demonstrated for early warning system targeting leakage detection of two gases (NH3 and H2S). This work achieves the integrated functions of chemical gas information decode, tunable gas memory level, and selectivity of gas in a single device, which provides a promising pathway for the development of future artificial olfactory systems.

A Comprehensive Literature Review on Volatile Memory Forensics

Through a systematic literature review, which is considered the most comprehensive way to analyze the field of memory forensics, this paper investigates its development through past and current methodologies, as well as future trends. This paper systematically starts with an introduction to the key issues and a notable agenda of the research questions. Appropriate inclusion and exclusion criteria were then developed, and a deliberate search strategy was adopted to identify primary research studies aligned with the research question. The paper goes into specific details of six different memory categories, notably volatile memory, interpreting their advantages and the tactics used to retrieve the data. A detailed comparison with existing reviews and other relevant papers is made, forming a broader and wider picture of the research. The discussion summarizes the main findings, particularly the rise of more complex and advanced cyber threats and the necessity of more effective forensic methods for their investigation. This review pinpoints the possibilities for future study with the purpose of staying ahead in the evolving technological landscape. This overview is undoubtedly an essential resource for professionals and researchers working in digital forensics. It allows them to stay competent and provides enough insight into the current trends while marking the future direction in digital forensics methodology.

Implementation of 8-bit reservoir computing through volatile ZrOx-based memristor as a physical reservoir

In this study, we employed a sputtering process to construct a memristive device within the ITO/ZrOx/TaN structure for implementing neuromorphic computing. Initially, we scanned the basic electrical properties of the ITO/ZrOx/TaN device using a DC voltage sweep on the top ITO electrode. A highly uniform gradual resistive switching phenomenon was observed over 100 cycles. The current decay in the low-resistance state was effectively controlled by the volatile memory properties. Gradual conductance changes for potentiation and depression were achieved by applying electrical pulses, enabling the establishment of multi-level conductance states. In addition, the emulation of various synaptic functions was achieved by following the learning rules of SRDP, EPSC, STDP, ADSP, Pavlovian associative learning, and PPF. Finally, 8-bit reservoir computing was demonstrated in cost-effective pattern generation and recognition, highlighting the ITO/ZrOx/TaN device’s advantageous memory storage properties for synaptic characteristics.

Halide perovskite photovoltaics for in-sensor reservoir computing

As intelligent electronics get laden with multimodal sensors, the data transfer and computation increase their energy expenditure. Consequently, researchers aim to develop efficient computing paradigms or integrate energy harvesting from ambient sources. Halide perovskites possess unique photophysics and coupled ionic-electronic dynamics that actualise memory devices for brain-inspired computing. Synergising the computing capability with their conventional light harvesting efficacy could aptly address the aforementioned problem. In a novel approach, the transient open circuit voltage (VOC) of a methylammonium lead bromide-based solar cell was exploited to serve as a self-powered volatile short-term memory for optoelectronic in-sensor reservoir computing. The origin of the memory is attributed to the influence of mobile ions on the carrier generation and recombination in the device. The system's versatility and task specificity were shown by engineering the volatility of the memory. The benchmarking task of MNIST handwritten digit recognition was performed with the highly reproducible and robust transformation of optical inputs into unique reservoir states. To demonstrate the high nonlinearity, second-order time-series prediction (NARMA2) was performed. Finally, an exemplary cardiac health-monitoring application was showcased by monolithic reading and processing of a physiological time series known as photoplethysmography (PPG) to identify atrial fibrillation with increased computational efficiency.

Advanced Volatile Memory Forensics through Autopsy Integration

The main goal of this study is to design a novel plugin for the Autopsy forensic framework to enable forensic analysts to identify and extract volatile memory from small-scale digital devices. This includes network peripherals and Internet of Things devices, smartphones, and industrial-control systems. Given the importance of volatile memory to digital crime and cybersecurity investigations, an accurate and reliable tool is needed to non-destructively acquire forensic copies of the evidence. In the context of small-scale devices, this study is of acute importance to bridge the gap that exists in current forensic research and forensic practice, using separate tools can be challenging due to compatibility issues and the complexity of managing multiple system. In conclusion, the developed Autopsy plugin, which has been termed the MemoryIntegrator, seamlessly harmonizes with Autopsy forensic framework and is designed to work together with Volatility tool, specializing in detailed memory analysis. Consequently, the following main outcomes result from the experimentation and application of the plugin: Promotes the default forensic activity of Autopsy by providing the analysts with a way to swiftly and directly harvest and evaluate volatile data from diverse small scale digital devices. The implementation of the plugin ensures that the integrity of the memory data is maintained throughout the extraction and analysis process. This is facilitated by cryptographic hash validations that confirm that there are no changes in the data from the extraction to the point of analysis. The plugin maintains the integrity of the memory data from the time of extraction to the time of analysis using cryptographic hash validations which verifies that these data has not been manipulated at this point. MemoryIntegrator outmatched all the forensic tools herewith because conducting forensic test back at home verified its superiority in terms of the extraction of data from memory speed and the authenticity and formula which it uses in analysis. In the modern world, this is critical to investigate digital crimes and incidences that affect cybersecurity.

The effects of Hf-doping and thermal treatment on the resistive switching properties of rf magnetron sputtered Sm2(1-x)HfxCe2O7 thin films

The Sm2(1-x)HfxCe2O7 thin films were prepared via the RF sputtering, and the effects of different doping concentrations and thermal treatments on the resistive switching properties of Al/Sm2(1-x)HfxCe2O7/ITO devices were studied. The research indicates that at a HfO2 content of 0.05 mol%, the switching cycles can be increased from 1031 for undoped film to 1221, along with a Ron/Roff ratio > 101, and a retention time 104 s. By employing an annealing process at 300 °C, the device demonstrates extended endurance, enduring 2185 switching cycles, achieving a Ron/Roff ratio > 101, and boasting a retention time of 104 s, thereby showcasing excellent durability and data retention properties. The I–V characteristics are primarily influenced by the addition of Hf4+ ions and atomic rearrangement after thermal treatment. The conductive filaments are formed by oxygen vacancies and metal ion-assisted filaments in the Al/Sm2(1-x)HfxCe2O7/ITO device with x = 0.05 annealed at 300 °C. Moreover, the switching window can be enhanced to a Ron/Roff ratio of approximately 103 through post-metal annealing. This study suggests that Al/Sm2(1-x)HfxCe2O7/ITO devices hold promise as volatile memory materials.

VolMemDroid—Investigating android malware insights with volatile memory artifacts

Android based smartphones have become a top target for malware writers due to their widespread use. A number of malicious applications are present on play stores and downloaded on daily basis, posing a significant threat to users’ personal and business data. As a result, the design of malware analysis frameworks is crucial in protecting the growing number of users who rely on their smart phones for routine and business tasks. The traditional signature based schemes for malware detection are unable to handle new and sophisticated malware. Furthermore, generic solutions based on static analysis schemes become less effective in the presence of obfuscated malware. In this study, a dynamic analysis based framework, VolMemDroid, for detecting malicious applications for Android is proposed. The framework extracts the volatile memory artifacts for profiling malicious Android applications. For this purpose, the memory forensic framework of volatility is utilized. A number of volatility plugins are analyzed for their compatibility w.r.t the Android platform and their ability in modeling the application’s behavior. After testing a number of plugins, chosen plugins are further processed for extraction of features. A comprehensive feature set for Android malware detection and categorization is proposed. It has been found that the suggested framework is effective for detecting Android malicious applications with an F1-score of 0.972, which is better than existing volatile memory based approaches for Android malware detection. The framework is also found to be effective in categorizing malicious Android applications into four distinct classes.

Volatile working memory representations crystallize with practice

Working memory, the process through which information is transiently maintained and manipulated over a brief period, is essential for most cognitive functions1–4. However, the mechanisms underlying the generation and evolution of working-memory neuronal representations at the population level over long timescales remain unclear. Here, to identify these mechanisms, we trained head-fixed mice to perform an olfactory delayed-association task in which the mice made decisions depending on the sequential identity of two odours separated by a 5 s delay. Optogenetic inhibition of secondary motor neurons during the late-delay and choice epochs strongly impaired the task performance of the mice. Mesoscopic calcium imaging of large neuronal populations of the secondary motor cortex (M2), retrosplenial cortex (RSA) and primary motor cortex (M1) showed that many late-delay-epoch-selective neurons emerged in M2 as the mice learned the task. Working-memory late-delay decoding accuracy substantially improved in the M2, but not in the M1 or RSA, as the mice became experts. During the early expert phase, working-memory representations during the late-delay epoch drifted across days, while the stimulus and choice representations stabilized. In contrast to single-plane layer 2/3 (L2/3) imaging, simultaneous volumetric calcium imaging of up to 73,307 M2 neurons, which included superficial L5 neurons, also revealed stabilization of late-delay working-memory representations with continued practice. Thus, delay- and choice-related activities that are essential for working-memory performance drift during learning and stabilize only after several days of expert performance.

Graphene–fullerene heterostructures as robust and flexible nanomechanical bits

Electrical computers have revolutionized society over the past several decades, but questions have remained about their ability to perform in extreme environments, such as their stability at high-temperature conditions. This has motivated the recent surge of interest in developing mechanical computing platforms at all length scales, including the nanoscale, in which traditional electrical computers are augmented with mechanical ones. However, the most proposed nanomechanical bits are volatile memory bits based on the dynamic response of nanomechanical resonators, and as such, there is a need to develop robust and reprogrammable entirely non-volatile nanomechanical bits. Here, we exploit the multiple quasi-stable configurations of the graphene/fullerene/graphene (GFG) van der Waals heterostructure to work as a novel nonvolatile nanomechanical bit. The GFG heterostructure is unique and robust in that it can return to its initial state without further mechanical input, it can be used for logic functions at relevant operating temperatures through simple application of uniaxial strain, it can exhibit reprogrammability between five basic logic gates (NOT, AND, NAND, OR, NOR) by varying the magnitude or direction of applied strain, and it can be used to represent combinatorial logic through full and half adders. These findings provide a new opportunity to develop mechanical computers based on the large class of nanomaterials.

Enhancing Lifetime and Performance of MLC NVM Caches Using Embedded Trace Buffers

Large volumes of on-chip and off-chip memory are required by contemporary applications. Emerging non-volatile memory technologies including STT-RAM, PCM, and ReRAM are becoming popular for on-chip and off-chip memories as a result of their desirable properties. Compared to traditional memory technologies such as SRAM and DRAM, they have minimal leakage current and high packing density. Non Volatile Memories (NVM), however, have a low write endurance, a high write latency, and high write energy. Non-volatile Single Level Cell (SLC) memories can store a single bit of data in each memory cell, whereas Multi Level Cells (MLC) can store two or more bits in each memory cell. Although MLC NVMs have substantially higher packing density than SLCs, their lifetime and access speed are key concerns. For a given cache size, MLC caches consume 1.84× less space and 2.62× less leakage power than SLC caches. We propose Trace buffer Assisted Non-volatile Memory Cache (TANC), an approach that increases the lifespan and performance of MLC-based last-level caches using the underutilized Embedded Trace Buffers (ETB). TANC improves the lifetime of MLC LLCs up to 4.36× and decreases average memory access time by 4% compared to SLC NVM LLCs and by 6.41× and 11%, respectively, compared to baseline MLC LLCs.

Developing Exp-FIGARCH Hybrid Models for Time Series Modelling

In this paper, we introduced a new hybrid model namely Exponential Autoregressive-Fractional Integrated Generalized Autoregressive Conditional Heteroscedasticity (ExpAR-FIGARCH) model and study financial data. The Daily Nigeria All Share Stock Index that exhibit nonlinear, volatility and long memory effect were analyzed in the study. The existing ExpAR-Generalized Autoregressive Conditional Heteroscedasticity (ExpAR-GARCH) model were estimated and compared with the proposed ExpAR-FIGARCHmodel. Results showed that the new hybrid model is better based on efficient parameters, serial correlation analysis and forecast measures of accuracy. Therefore, as a conclusion, the current study indicates that the ExpAR-FIGARCHmodel performed better compared to the ExpAR-GARCHhybrid model. Therefore, the ExpAR-FIGARCHmodel is a better option for modeling nonlinear, volatility and long memory characteristics of time series. Future study should focus on the application of the developed hybrid ExpAR-FIGARCHmodel using health, meteorological and economic data.