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Information technologies require entangling data stability with encryption for a next generation of secure data storage. Current magnetic memories, ranging from low-density stripes up to high-density hard drives, can ultimately be detected using routinely available probes or manipulated by external magnetic perturbations. Antiferromagnetic resistors feature unrivalled robustness but the stable resistive states reported scarcely differ by more than a fraction of a percent at room temperature. Here we show that the metamagnetic (ferromagnetic to antiferromagnetic) transition in intermetallic Fe0.50Rh0.50 can be electrically controlled in a magnetoelectric heterostructure to reveal or cloak a given ferromagnetic state. From an aligned ferromagnetic phase, magnetic states are frozen into the antiferromagnetic phase by the application of an electric field, thus eliminating the stray field and likewise making it insensitive to external magnetic field. Application of a reverse electric field reverts the antiferromagnetic state to the original ferromagnetic state. Our work demonstrates the building blocks of a feasible, extremely stable, non-volatile, electrically addressable, low-energy dissipation, magnetoelectric multiferroic memory.
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This paper presents a novel resistive-only Binary and Ternary Content Addressable Memory (B/TCAM) cell that consists of two Complementary Resistive Switches (CRSs). The operation of such a cell relies on a logic$rightarrow$ON state transition that enables this novel CRS application.
The magnetoelectric effects in multiferroics have a great potential in creating next-generation memory devices. We conceive a new concept of non-volatile memories based on a type of nonlinear magnetoelectric effects showing a butterfly-shaped hysteresis loop. The principle is to utilize the states of the magnetoelectric coefficient, instead of magnetization, electric polarization or resistance, to store binary information. Our experiments in a device made of the PMN-PT/Terfenol-D multiferroic heterostructure clearly demonstrate that the sign of the magnetoelectric coefficient can be repeatedly switched between positive and negative by applying electric fields, confirming the feasibility of this principle. This kind of non-volatile memory has outstanding practical virtues such as simple structure, easy operations in writing and reading, low power, fast speed, and diverse materials available.
Recent experiments on layered {alpha}-In2Se3 have confirmed its room-temperature ferroelectricity under ambient condition. This observation renders {alpha}-In2Se3 an excellent platform for developing two-dimensional (2D) layered-material based electronics with nonvolatile functionality. In this letter, we demonstrate non-volatile memory effect in a hybrid 2D ferroelectric field effect transistor (FeFET) made of ultrathin {alpha}-In2Se3 and graphene. The resistance of graphene channel in the FeFET is tunable and retentive due to the electrostatic doping, which stems from the electric polarization of the ferroelectric {alpha}-In2Se3. The electronic logic bit can be represented and stored with different orientations of electric dipoles in the top-gate ferroelectric. The 2D FeFET can be randomly re-written over more than $10^5$ cycles without losing the non-volatility. Our approach demonstrates a protype of re-writable non-volatile memory with ferroelectricity in van de Waals 2D materials.
DNA sequencing is the physical/biochemical process of identifying the location of the four bases (Adenine, Guanine, Cytosine, Thymine) in a DNA strand. As semiconductor technology revolutionized computing, modern DNA sequencing technology (termed Next Generation Sequencing, NGS)revolutionized genomic research. As a result, modern NGS platforms can sequence hundreds of millions of short DNA fragments in parallel. The sequenced DNA fragments, representing the output of NGS platforms, are termed reads. Besides genomic variations, NGS imperfections induce noise in reads. Mapping each read to (the most similar portion of) a reference genome of the same species, i.e., read mapping, is a common critical first step in a diverse set of emerging bioinformatics applications. Mapping represents a search-heavy memory-intensive similarity matching problem, therefore, can greatly benefit from near-memory processing. Intuition suggests using fast associative search enabled by Ternary Content Addressable Memory (TCAM) by construction. However, the excessive energy consumption and lack of support for similarity matching (under NGS and genomic variation induced noise) renders direct application of TCAM infeasible, irrespective of volatility, where only non-volatile TCAM can accommodate the large memory footprint in an area-efficient way. This paper introduces GeNVoM, a scalable, energy-efficient and high-throughput solution. Instead of optimizing an algorithm developed for general-purpose computers or GPUs, GeNVoM rethinks the algorithm and non-volatile TCAM-based accelerator design together from the ground up. Thereby GeNVoM can improve the throughput by up to 113.5 times (3.6); the energy consumption, by up to 210.9 times (1.36), when compared to a GPU (accelerator) baseline, which represents one of the highest-throughput implementations known.
Planar electrodes patterned on a ferroelectric substrate are shown to provide lateral control of the conductive state of a two-terminal graphene stripe. A multi-level and on-demand memory control of the graphene resistance state is demonstrated under low sub-coercive electric fields, with a susceptibility exceeding by more than two orders of magnitude those reported in a vertical gating geometry. Our example of reversible and low-power lateral control over 11 memory states in the graphene conductivity illustrates the possibility of multimemory and multifunctional applications, as top and bottom inputs remain accessible.
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