Memory Applications Research Articles

Investigation on the mechanism and a universal structural design method for resistive switching devices

Resistive random-access memories have attracted significant attention in memory applications, while the physical mechanism behind their resistive switching behavior is still unclear. As a key issue, the migration of oxygen vacancies (VO) directly influences the performance of devices in the formation and rupture of conductive filaments (CFs). In this work, the distance of VO migration is performed as electric field dependent and affects the performance of the device. Sufficient distance of VO migration is essential for formation and rupture of CF completely, leading to, ideally, SET/RESET transition of the device. Based on this mechanism, an enhanced electric field is designed that optimizes the stability of the device significantly, which is demonstrated to be a promising approach to optimize the properties of the device. The investigation is helpful for guiding the design of device structures.

Combining Machine Learning and Lifetime-Based Resource Management for Memory Allocation and Beyond

Memory management is fundamental to the performance of all applications. On modern server architectures, an application's memory allocator needs to balance memory utilization against the ability to use 2MB huge pages, which are crucial for achieving high performance. This paper shows that prior C++ memory allocators are fundamentally limited because optimizing this trade-off depends on the knowledge of object lifetimes, which is information allocators lack. We introduce a two-step approach to attain high memory utilization in huge pages. We first introduce a novel machine-learning approach that predicts the lifetime of freshly allocated objects using the stack trace at the time of allocation and treats stack traces as natural language. We then present a fundamentally new type of memory allocator that exploits (potentially incorrect) object lifetime predictions to achieve high memory utilization at full huge page usage. In contrast to prior memory allocators that organize their heap around size classes and free lists, our allocator organizes the heap based on predicted lifetime classes and adjusts to mispredictions on the fly. We demonstrate experimentally that this learned lifetime-aware memory allocator (LLAMA) reduces fragmentation with huge pages by up to 78%. Our approach gives rise to a new methodology for applying ML in computer systems. In addition, similar space-time bin packing problems abound in computer science and we discuss how this approach has applications beyond memory allocation to a wide range of problems.

Thermally generated magnonic spin currents in a polycrystalline gadolinium iron garnet thin film with perpendicular magnetic anisotropy

Rare-earth iron garnets (REIGs) are the benchmark systems for magnonics, including the longitudinal spin Seebeck effect (LSSE). While most research has focused on single-crystalline REIGs on complimentary garnet substrates, moving to more, cost-effective complementary metal-oxide semiconductor (CMOS)-compatible substrates is important to integrate REIG thin films with existing technology. In this regard, we grow a 130 nm-thick polycrystalline gadolinium iron garnet (GdIG) film on the Si/SiO2 substrate and investigate the temperature-dependent LSSE. Interestingly, the polycrystalline GdIG film exhibits perpendicular magnetic anisotropy (PMA) at room temperature which is induced by tensile in-plane (IP)-strain originating from the thermal-expansion mismatch between the GdIG film and the substrate during rapid thermal annealing. Further, a spin-reorientation transition from the out-of-plane IP direction below TS = 180 K is observed. Additionally, the film reveals a magnetic compensation temperature, TComp, of ≈240 K. The LSSE voltage not only demonstrates a sign-inversion around TComp, but also shows noticeable changes around TS. As compared to a single-crystalline GdIG film, the lower LSSE voltage for the polycrystalline GdIG is attributed to the higher effective magnetic anisotropy and enhanced magnon scattering at the grain boundaries. Our study not only paves the way for the cost-effective growth of CMOS-compatible REIG-based systems with PMA for magnonic memory and information processing applications, but also highlights the fact that the spincaloritronic and spin-insulatronic properties of the polycrystalline REIGs follow those of their single-crystalline counterparts with reduced spin-to-charge conversion efficiency through LSSE which can be tuned further by controlling the average gran size and interface engineering.

Enhancing ferroelectric performance in hafnia-based MFIS capacitor through interface passivation and bulk doping

In recent times, there has been a notable surge of interests in hafnia (HfO2)-based ferroelectrics, primarily due to their remarkable ferroelectric properties employed in ultra-thin configurations, alongside their compatibility with the conventional CMOS manufacturing process. In order to harness the full potential of HfO2-based films for high-performance non-volatile memory applications, it is imperative to enhance their ferroelectric characteristics and durability. This study introduces a straightforward approach aimed at augmenting the ferroelectric performance of Hf x Zr1−x O2 (HZO) films deposited on silicon (Si) substrates through the engineering of oxygen vacancies (V O). The results of this endeavor demonstrate a significant enhancement in ferroelectric performance, characterized by a 2Pr value of 47 μC cm−2 and impressive endurance, enduring up to 108 cycles under an 8 MV cm−1 electric field without the need of a wake-up process. This marked improvement can be attributed to a dual-pronged approach, involving the incorporation of an Al2O3 interlayer and the introduction of Al atoms into the HZO film. The Al2O3 interlayer primarily serves to mitigate the presence of oxygen vacancies at the interface, while the introduction of Al dopants elevates the concentration of oxygen vacancies within the bulk material. This modulation of oxygen vacancy concentration proves instrumental in facilitating the formation of a ferroelectric o-III phase within the HZO-based films, thereby further augmenting their ferroelectric performance. This innovative and effective strategy offers an alternative avenue for enhancing the ferroelectric properties of materials characterized by a fluorite crystal structure.

Phase-field theory study on the modulation mechanism of oxygen vacancy concentration on charged domain wall in ferroelectric thin films

This study analyzes the regulatory mechanism of oxygen vacancy concentration on tail-to-tail charged domain walls (T–T CDWs), along with the writing time, conduction current magnitude, and retention performance of through-type T–T CDWs. The research results show that the highest density and length of T–T CDWs are achieved when the oxygen vacancy concentration is 1 × 1020 cm−3. Moreover, the successful writing of through-type T–T CDWs is limited to a certain electric field range, which is controlled by oxygen vacancy concentration. An increase in the oxygen vacancy concentration leads to a decrease in the maximum and minimum threshold electric fields required for writing through-type charged domain walls. The writing time and conductivity of through-type T–T CDWs determine the information writing speed and signal strength of domain wall memories, and the oxygen vacancy concentration also plays a regulatory role in both aspects. When the oxygen vacancy concentration is 1 × 1020 cm−3, the through-type T–T CDW exhibits the fastest writing speed, requiring only 8 ns. The magnitude of the conduction current of through-type T–T CDWs is directly proportional to the oxygen vacancy concentration. The through-type T–T CDWs formed by the aggregation of oxygen vacancies exhibit excellent retention performance, making them highly promising for applications in ferroelectric domain wall memories. Our research demonstrates that oxygen vacancies have a significant regulatory effect on the morphology and current response of charged domain walls, opening up new avenues for the study of domain wall memories.

Electro-Optically Tunable Passivated Double-Cation Perovskite-Based ReRAM for Low-Power Memory Applications

Electro-Optically Tunable Passivated Double-Cation Perovskite-Based ReRAM for Low-Power Memory Applications

Application of Long Short-Term Memory (LSTM) in Stock Price Prediction

No doubt, the stock market has been one of the most volatile and this makes it very difficult to carry out forecast or prediction about the movement in share prices. However, the advent of machine learning is gradually changing the narratives as there are a number of neural networks which could be used to develop models to effectively predict the stock prices. This study examined the application of Long Short-Term Memory algorithms in predicting stock prices using time series data on Apple shares from 2017 to 2022. The closing price of the stocks was adopted and the data split into 98% for training and 2% for test because the study only wanted to make prediction for the last 20 days. TensorFlow together with its subset Keras libraries were employed with other libraries like Dropout for regularization and sklearn for normalization. NumPy to build array, pandas for data analysis were also imported with matplotlib for the purpose of plotting the result graph. The LSTM model was trained to learn from the sequential data and predict the share price. The outcome revealed that the machine learnt very effectively and has a predictive ability for share prices. The model was able to predict the trend or pattern in prices almost accurately.

Design and performance analysis of tri-layered strained Si/Si1–x Ge x /Si heterostructure DG feedback FET

This work presents the design and performance analysis of a tri-layered strained Si/Si1−x Ge x /Si heterostructure double gate feedback field-effect transistor (DG FBFET). The proposed DG FBFET is designed by introducing biaxial strain in the device by sandwiching a Si1−x Ge x layer between two thin Si layers to provide high ON current as well as ultra-steep switching characteristics. The device offers a significantly high ON current (3.4 x 10−3 A/μm), high I ON /I OFF ratio (∼1010), a large memory window of 1.06 V, and an extremely low subthreshold swing (∼0.3 μ V/decade), which can be very useful for memory and neuromorphic applications. Furthermore, the ON/OFF switching of the device has been accomplished at a lower threshold voltage (0.287 V), allowing it to be utilized in low-power electronics. Synopsys TCAD tool has been used to create the device structure and analyze the electrical performances of the device.

Ferroelectric polarization and conductance filament coupling for large window and high-reliability resistive memory and energy-efficient synaptic devices

Ferroelectric capacitors hold great promise for non-volatile memory applications. However, the challenge lies in fabricating resistive switching devices with a high on/off ratio, excellent non-volatility, and a simple manufacturing process. Here, a novel approach is introduced by demonstrating the efficacy of the coupling effect between ferroelectric polarization and oxygen vacancy-based conductive filaments in Hf0.5Zr0.5O2 (HZO) films for the creation of non-volatile resistive switching memory devices, achieving an impressive on/off ratio of 6.8 × 103 at +1.8 V. An in-depth exploration of the resistive switching mechanism is provided and subsequently the outstanding durability and retention characteristics of these devices for resistive switching is validated. Furthermore, the device’s capacity to emulate non-volatile synaptic functionalities is assessed. Our results reveal that under pulsed conditions of 1 V/–2 V with 1 µs pulses spaced 50 ms apart, the device can robustly achieve potentiation/depression synaptic plasticity, while exhibiting energy consumption (0.16 fJ for potentiation, 0.12 fJ for depression) reduced by 1–2 orders of magnitude compared to biological synapses. This work holds significant value as a reference for the fabrication of energy-efficient, non-volatile memory and synaptic devices.

Multifunctionality in Electrically Poled PMN–PT/Ni–Mn–In Multiferroic Heterostructure for Flexible Magnetic Field Sensing and Nonvolatile Memory Applications

Multifunctionality in Electrically Poled PMN–PT/Ni–Mn–In Multiferroic Heterostructure for Flexible Magnetic Field Sensing and Nonvolatile Memory Applications

Investigation of phonon lifetimes and magnon–phonon coupling in YIG/GGG hybrid magnonic systems in the diffraction limited regime

Quantum memories facilitate the storage and retrieval of quantum information for on-chip and long-distance quantum communications. Thus, they play a critical role in quantum information processing and have diverse applications ranging from aerospace to medical imaging fields. Bulk acoustic wave (BAW) phonons are attractive candidates for quantum memories because of their long lifetimes and high operating frequencies. In this study, we establish a modeling approach to design hybrid magnonic high-overtone bulk acoustic wave resonator (HBAR) structures for high-density, long-lasting quantum memories, and efficient quantum transduction devices. We illustrate the approach by investigating a hybrid magnonic system, consisting of a gadolinium iron garnet (GGG) thick film and a patterned yttrium iron garnet (YIG) thin film. The BAW phonons are excited in GGG thick film via coupling with magnons in the YIG thin film. We present theoretical and numerical analyses of the diffraction-limited BAW phonon lifetimes, modeshapes, and magnon–phonon coupling strengths in YIG/GGG planar and confocal HBAR (CHBAR) structures. We utilize Fourier beam propagation and Hankel transform eigenvalue problem methods and compare the two methods. We discuss strategies to improve the phonon lifetimes in the diffraction-limited regime, since increased lifetimes have direct implications on the storage times of quantum states for quantum memory applications. We find that ultra-high cooperativities and phonon lifetimes on the order of ∼105 and ∼10 milliseconds, respectively, could be achieved using a CHBAR structure with 10μm YIG lateral area. Additionally, high integration density of on-chip memory or transduction centers is naturally desired for high-density memory or transduction devices. The proposed CHBAR structure will offer more than 100-fold improvement of integration density relative to a recently demonstrated YIG/GGG device. Our results will have direct applicability for devices operating in the cryogenic or milliKelvin regimes. For example, our study will inform the design of HBAR devices that could couple with superconducting qubits for promising quantum information platforms.

Optical Modulation of MoTe2/Ferroelectric Heterostructure via Interface Doping.

Optical modulation through interface doping offers a convenient and efficient way to control ferroelectric polarization, thereby advancing the utilization of ferroelectric heterostructures in nanoelectronic and optoelectronic devices. In this work, we fabricated heterostructures of MoTe2/BaTiO3/La0.7Sr0.3MnO3 (MoTe2/BTO/LSMO) and demonstrated opposite ultraviolet (UV) light-induced polarization switching behaviors depending on the varied thicknesses of MoTe2. The thickness-dependent band structure of MoTe2 film results in interface doping with opposite polarity in the respective heterostructures. The polarization field of BTO interacts with the interface charges, and an enhanced effective built-in field (Ebi) can trigger the transfer of massive UV light-induced carriers in both MoTe2 and BTO films. As a result, the interplay among the contact field of MoTe2/BTO, the polarization field, and the optically excited carriers determines the UV light-induced polarization switching behavior of the heterostructures. In addition, the electric transport characteristics of MoTe2/BTO/LSMO heterostructures reveal the interface barrier height and Ebi under opposite polarization states, as well as the presence of inherent in-gap trap states in MoTe2 and BTO films. These findings represent a further step toward achieving multifield modulation of the ferroelectric polarization and promote the potential applications in optoelectronic, logic, memory, and synaptic ferroelectric devices.

Bistable magnetic nanowires: A new approach to non-volatile memory with single readout and automatic deletion

A novel approach for a non-volatile destructive readout memory application using bistable magnetic nanowire arrays is presented. The encoded information is stored as binary 1 and 0 by groups of NWs magnetized in the positive and negative states, respectively. We leverage the naturally occurring switching field distribution of the NW array and a tailored alternating decreasing magnetic field to program remanent magnetic states. To retrieve the information, the measured remagnetization curve exhibits a star-like behavior with jumps and plateaus and its derivative converts this information to a binary-type format. Two encoding and readout schemes are proposed and validated: binary bits and barcodes. For each case, the implementation and optimization procedures are illustrated, along with the required processing to obtain a useful readout signal. This strategy holds potential for non-volatile memory applications in which the stored information is erased during reading and can be reused indefinitely.Graphical abstract

Fault tolerant micro-programmed control unit for SEU and MBU mitigation in space based digital systems

The Micro-Programmed Control Unit (MPCU) offers an alternative to conventional hardwired control circuits by employing a control store for transmitting control signals through stored control bits. Traditionally, Hamming codes are used for Single-Event Upset (SEU) protection in memory components. However, these Error Control Codes (ECC) encounter challenges, such as inconsistent code rates and limited adaptability for Multiple Bit Upsets (MBU). To address MBU protection, ECC codes with soft decoding logic are commonly utilized, leading to increased hardware complexity. This paper introduces a lightweight encoding and hard-decoding logic capable of detecting and correcting up to n2∗ upsets, achieving a code rate of ≈ 0.37. Through evaluations against both traditional and recent ECC methods, the proposed approach demonstrates enhanced fault correction capabilities compared to Hamming codes. Furthermore, it maintains a consistent code rate, incurs lower area overhead, and exhibits less hardware complexity than MBU codes by employing hard decoding logic. The suggested methodology effectively mitigates both MBU and SEUs in control stores, providing reduced delay and making it well-suited for ensuring reliability in high-speed memory applications like MPCUs.

Physical characterization, magnetic interactions and DC electrical resistivity properties of La3+ substituted NiZnCd nano ferrites

In this article, we substituted La3+ ions to explore the structural, magnetic, and DC electrical resistivity properties of Ni0.5Zn0.4Cd0.1Fe2-xLaxO4 (x = 0.0, 0.02, 0.04, 0.06, and 0.08) nano-structured spinel ferrites. We employed X-ray diffraction (XRD), Field-emission scanning electron microscopy (FESEM) with Energy dispersive X-ray (EDX), Fourier transform infrared spectroscopy (FTIR), Vibrating sample magnetometry (VSM), and electrical measurements to characterize the studied samples, which were synthesized using the sol–gel auto-combustion process. XRD analysis confirmed that the prepared materials formed a single-phase nanostructure. Using Debye-Scherer's formula, we calculated the average crystallite size, which ranged from 29.01 to 21.52 nm. FESEM images demonstrated that each sample exhibited spherical nanocrystalline behavior. The variations of the prepared samples were confirmed constitutionally by EDX. The FTIR data revealed two absorption bands for all synthesized materials at υ1 = 562.18 to 590.51 cm−1 and υ2 = 410.18 to 430.51 cm−1, corresponding to the tetrahedral and octahedral sites of the spinel structure, respectively. Vibrant sample magnetometry spectra were utilized to investigate how La3+-doping affects the magnetic characteristics of NiZnCd ferrite. Due to enhanced La3+-doping, saturation magnetization, and coercivity values decreased. The La3+ doping of NiZnCd nano ferrites resulted in changes in saturation magnetization (from 83.66 to 69.57 emu/g), coercivity (from 33.75 to 60.51 Oe), and remanence magnetization (from 29.62 to 33.12 emu/g). These variations suggest the potential utility of the samples for magnetic recording and memory applications. The semiconducting behavior has been confirmed by the DC electrical resistivity measured values using a two-probe method. Data on DC electrical resistivity were also utilized to determine carrier concentration and activation energy, establishing a connection with La3+ replacement.

Low-power and area-efficient memristor based non-volatile D latch and flip-flop: Design and analysis.

In recent years, non-volatile memory elements have become highly appealing for memory applications to implement a new class of storage memory that could replace flash memories in sequential logic applications, with features such as compactness, low power, fast processing speed, high endurance, and retention. The memristor is one such non-volatile element that fits the fundamental blocks of sequential logic circuits, the latch and flip-flop; hence, in this article, a non-volatile latch architecture using memristor ratioed logic (MRL) inverter and CMOS components is focused, with an additional memristor as a memory element. A Verilog-A model was used to create the memristor element. The simulation findings validated the compact, low-voltage, and reliable design of the latch design. We evolved in technology enough to create a master-slave flip-flop and arrange it to function as a counter and a shift register. Power, number of elements, cell size, energy, programming time, and robustness are compared to comparable non-volatile topologies. The proposed non-volatile latch proves non-volatility and can store data with a 24% reduction in power consumption and a near 10% reduction in area.

The application of transactive memory in the learning organization and the role of credibility

Studies have shown that transactive memory plays a significant role in how learning occurs within organizations. While many studies have looked at what influences the growth of transactive memory, not much research has been done on how the thought processes of people working in groups are formed. This area of study examines the importance of how knowledge is organized in people's minds at work for the growth of transactive memory and how this process is affected by individual characteristics. Therefore, this study aims to fill this gap by looking at how trust and skill, known as credibility dimensions, affect the growth of transactive memory. For this purpose, information was gathered from 239 people working at a paint and coating company in Saudi Arabia. The results indicate that both trust and skill significantly influence the development of transactive memory.

Probability distribution of write failure in a memory cell array consisting of magnetic tunnel junction elements with distributed write error rates

Write failure (WF) is a major reliability issue for applications of magnetoresistive random access memory (MRAM), and much effort has been devoted to reducing the write error rate (WER), which is the probability of write failures of a memory cell. Recently, it was shown that the WER of MRAM obeys a skewed probability distribution even though the variation in material parameters obeys a normal distribution. However, little is known about the effect of WER distribution on WF in a memory cell array. Here, we study WF in a memory cell array consisting of magnetic tunnel junction elements with distributed WERs based on numerical simulations. We simulated Bernoulli trials of writing, assuming that the WER obeys a beta distribution. The results show that for typical writing patterns, WF in a memory cell array obeys a binomial distribution, with the mean of the WER as the probability of success. The statistical properties of WF in a memory cell array are not affected by the variance and skewness of the WER. The results provide a basic understanding of the statistical properties of WF in a memory cell array and will be useful for the development of computing systems that exploit erroneous memories.

Carrier-doping-driven insulator-metal transition in disordered materials for memristive switching with high uniformity

Attaining highly uniform operations in a disordered system presents a persistent challenge. The utilization of ion migration in amorphous materials to trigger the resistive switching process of the material usually results in inferior uniformity of the memristive device. Here, we demonstrate that the resistive switching behavior can be activated through carrier doping in the disorder system, and highly ordered resistance modulation is achieved in Ag-doped albumen. By manipulating the doping level of the carrier, the localization of the free electron wavefunction can be tuned, leading to multi-level variations in resistance. This memristive switching behavior is in all electronic and displays excellent switching uniformity, holding great potential for applications in high-density memories and neuromorphic computing chips.

The Generalized Long-Term Fault Memory Model and Applications to Paleoseismic Records

ABSTRACT Paleoseismic studies show large variability in earthquake interevent times along a fault, with short intervals often separated by long quiescent periods. Some paleoseismologists have interpreted this variability as a product of an earthquake’s partial strain release with the next earthquake occurring sooner than expected because of the remaining residual strain. However, commonly used probabilistic large earthquake recurrence models attribute this variability purely to chance, not the state of strain on the fault. Here, we present an alternative probabilistic model, built on the long-term fault memory model framework that better reflects the strain accumulation and release process. This generalized long-term fault memory model (GLTFM) predicts that this interevent time variability arises from both chance and the state of strain on the fault. Specifically, it estimates when residual strain is likely present and its impact on the timing of the next earthquake in the sequence. In addition, GLTFM assumes that additional accumulated strain always increases earthquake probability. In contrast, the commonly used lognormal and Brownian passage time models predict that the probability of a large earthquake stays constant or even decreases after it is “overdue” (past the observed average recurrence interval) so additional accumulated strain does not make an earthquake more likely. GLTFM’s simple implementation and versatility should make it a powerful tool in earthquake forecasting.