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The geometrical and performance scaling of silicon CMOS integrated circuit technology over the past 50 years has enabled many affordable new products for business and consumer applications. Recognizing that Flash is approaching its ultimate…
In this work, we propose SEE-MCAM, scalable and compact multi-bit CAM (MCAM) designs that utilize the three-terminal ferroelectric FET (FeFET) as the proxy. By exploiting the multi-level-cell characteristics of FeFETs, our proposed SEE-MCAM…
Ferroelectric field effect transistor (FeFET) memory has shown the potential to meet the requirements of the growing need for fast, dense, low-power, and non-volatile memories. In this paper, we propose a memory architecture named…
The low complexity quantization of channel state information (CSI) and the utilization of vertical freedom of three dimension (3D) channels are two critical issues in the limited feedback design of the \emph{full dimension…
Main memories play an important role in overall energy consumption of embedded systems. Using conventional memory technologies in future designs in nanoscale era causes a drastic increase in leakage power consumption and temperature-related…
Diamond defects are among the most promising qubits. Modelling their properties through accurate quantum mechanical simulations can further their development into robust units of information. We use the recently developed capped density…
Implementing optical-based memory and utilizing it for computation on the nanoscale remains an attractive but still a challenging task. While significant progress was achieved in nanophotonics, allowing to explore nonlinear optical effects…
Bioelectrical properties of cells such as relative permittivity, conductivity, and characteristic time constants vary significantly between healthy and malignant cells across different frequencies. These distinctions provide a promising…
Atomically thin layered two-dimensional materials, including transition-metal dichacolgenide (TMDC) and black phosphorus (BP), (1) have been receiving much attention, because of their promising physical properties and potential applications…
The superior density of passive analog-grade memristive crossbars may enable storing large synaptic weight matrices directly on specialized neuromorphic chips, thus avoiding costly off-chip communication. To ensure efficient use of such…
In this letter, we demonstrate a non-volatile memory device in a graphene FET structure using ferroelectric gating. The binary information, i.e. "1" and "0", is represented by the high and low resistance states of the graphene working…
A low-power Content-Addressable-Memory (CAM) is introduced employing a new mechanism for associativity between the input tags and the corresponding address of the output data. The proposed architecture is based on a recently developed…
We report a proof-of-concept demonstration of negative capacitance effect in a nanoscale ferroelectric-dielectric heterostructure. In a bilayer of ferroelectric, Pb(Zr0.2Ti0.8)O3 and dielectric, SrTiO3, the composite capacitance was…
The study investigates the influence of negative capacitance on the transfer characteristics of vdW FETs on the heterophase of CIPS ferroelectric. Notably, a less pronounced NC resulting from the spatial distribution of the ferroelectric…
Resistive-switching memories are alternative to Si-based ones, which face scaling and high power consumption issues. Tetrahedral amorphous carbon (ta-C) shows reversible, non-volatile resistive switching. Here we report polarity independent…
In neuromorphic photonic systems, device operations are typically governed by analog signals, necessitating digital-to-analog converters (DAC) and analog-to-digital converters (ADC). However, data movement between memory and these…
Compute-in-memory accelerators built upon non-volatile memory devices excel in energy efficiency and latency when performing deep neural network (DNN) inference, thanks to their in-situ data processing capability. However, the stochastic…
Typical mammal brains have some form of random connectivity between neurons. Reservoir computing, a neural network approach, uses random weights within its processing layer along with built-in recurrent connections and short-term, fading…
Compression and efficient storage of neural network (NN) parameters is critical for applications that run on resource-constrained devices. Despite the significant progress in NN model compression, there has been considerably less…
Existing causal speech separation models often underperform compared to non-causal models due to difficulties in retaining historical information. To address this, we propose the Time-Frequency Attention Cache Memory (TFACM) model, which…