Modeling electrical conduction in resistive-switching memory through machine learning


Published in AIP Advances on July 13, 2021. Read the full paper (open access). Abstract Traditional physical-based models have generally been used to model the resistive-switching behavior of resistive-switching memory (RSM). Recently, vacancy-based conduction-filament (CF) growth models have been used to model device characteristics of a wide range of RSM devices. However, few have focused o... » read more

SMASH: Synchronized Many-sided Rowhammer Attacks from JavaScript


Authors: Finn de Ridder, ETH Zurich and VU Amsterdam; Pietro Frigo, Emanuele Vannacci, Herbert Bos, and Cristiano Giuffrida, VU Amsterdam; Kaveh Razavi, ETH Zurich Abstract: "Despite their in-DRAM Target Row Refresh (TRR) mitigations, some of the most recent DDR4 modules are still vulnerable to many-sided Rowhammer bit flips. While these bit flips are exploitable from native code, tri... » read more

Memory Technology: Innovations needed for continued technology scaling and enabling advanced computing systems


Abstract: "An increasing demand for data generation, storage, and intelligence generation from data is driving advances in memory technology and advanced computing applications. Memory performance is starting to define modern day computing in both mobile and server environments. There is an absolute need to continue the tremendous pace of memory technology improvements to deliver performanc... » read more

Dynamic Flash Memory with Dual Gate Surrounding Gate Transistor (SGT)


Abstract: "This paper proposes an ultra-scaled memory device, called `Dynamic Flash Memory (DFM)'. With a dual-gate Surrounding Gate Transistor (SGT), a capacitorless 4F2 cell can be achieved. Similar to DRAM [1], refresh is needed, but high speed block refresh can improve the duty ratio. Analogous to Flash [2], three fundamental operations of “0” Erase, “1” Program, and Read are nee... » read more

Convolutional Compaction-Based MRAM Fault Diagnosis


Abstract: "Spin-transfer torque magnetoresistive random-access memories (STT-MRAMs) are gradually superseding conventional SRAMs as last-level cache in System-on-Chip designs. Their manufacturing process includes trimming a reference resistance in STT-MRAM modules to reliably determine the logic values of 0 and 1 during read operations. Typically, an on-chip trimming routine consists of mult... » read more

MBIST-supported Trim Adjustment to Compensate Thermal Behavior of MRAM


Abstract: "Spin Transfer Torque Magnetic Random Access Memory (STT-MRAM) is one of the most promising candidates to replace conventional embedded memory such as Static RAM and Dynamic RAM. However, due to the small on/off ratio of MRAM cells, process variations may reduce the operating margin of a chip. Reference trimming was suggested as one of the ways to reduce variation impact to the chi... » read more

Intermittent Undefined State Fault in RRAMs


Abstract: " Industry is prototyping and commercializing Resistive Random Access Memories (RRAMs). Unfortunately, RRAM devices introduce new defects and faults. Hence, high-quality test solutions are urgently needed. Based on silicon measurements, this paper identifies a new RRAM unique fault, the Intermittent Undefined State Fault (IUSF); this fault causes the RRAM device to intermittently c... » read more

A Compact Model For Scalable MTJ Simulation


Read the full technical paper. Published June 9, 2021. Abstract This paper presents a physics-based modeling framework for the analysis and transient simulation of circuits containing Spin-Transfer Torque (STT) Magnetic Tunnel Junction (MTJ) devices. The framework provides the tools to analyze the stochastic behavior of MTJs and to generate Verilog-A compact models for their simulation in lar... » read more

2D materials–based homogeneous transistor-memory architecture for neuromorphic hardware


Abstract "In neuromorphic hardware, peripheral circuits and memories based on heterogeneous devices are generally physically separated. Thus exploring homogeneous devices for these components is an important issue for improving module integration and resistance matching. Inspired by ferroelectric proximity effect on two-dimensional materials, we present a tungsten diselenide-on-LiNbO3 cascaded... » read more

FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator


Abstract: "Recent work demonstrated the promise of using resistive random access memory (ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ matrix-vector multiplication—the intensive and key computation in deep neural networks (DNNs). One key problem is the weights that are signed values. However, in a ReRAM crossbar, weights are stored as conductance of... » read more

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