اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدImpact of Magnetic Coupling and Density on STT-MRAM Performance
142
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
As a unique mechanism for MRAMs, magnetic coupling needs to be accounted for when designing memory arrays. This paper models both intra- and inter-cell magnetic coupling analytically for STT-MRAMs and investigates their impact on the write performance and retention of MTJ devices, which are the data-storing elements of STT-MRAMs. We present magnetic measurement data of MTJ devices with diameters ranging from 35nm to 175nm, which we use to calibrate our intra-cell magnetic coupling model. Subsequently, we extrapolate this model to study inter-cell magnetic coupling in memory arrays. We propose the inter-cell magnetic coupling factor Psi to indicate coupling strength. Our simulation results show that Psi=2% maximizes the array density under the constraint that the magnetic coupling has negligible impact on the devices performance. Higher array densities show significant variations in average switching time, especially at low switching voltages, caused by inter-cell magnetic coupling, and dependent on the data pattern in the cells neighborhood. We also observe a marginal degradation of the data retention time under the influence of inter-cell magnetic coupling.
قيم البحث
اقرأ أيضاً
As one of the most promising emerging non-volatile memory (NVM) technologies, spin-transfer torque magnetic random access memory (STT-MRAM) has attracted significant research attention due to several features such as high density, zero standby leakag
e, and nearly unlimited endurance. However, a high-quality test solution is required prior to the commercialization of STT-MRAM. In this paper, we present all STT-MRAM failure mechanisms: manufacturing defects, extreme process variations, magnetic coupling, STT-switching stochasticity, and thermal fluctuation. The resultant fault models including permanent faults and transient faults are classified and discussed. Moreover, the limited test algorithms and design-for-testability (DfT) designs proposed in the literature are also covered. It is clear that test solutions for STT-MRAMs are far from well established yet, especially when considering a defective part per billion (DPPB) level requirement. We present the main challenges on the STT-MRAM testing topic at three levels: failure mechanisms, fault modeling, and test/DfT designs.
The realistic modeling of STT-MRAM for the simulations of hybrid CMOS/Spintronics devices in comprehensive simulation environments require a full description of stochastic switching processes in state of the art STT-MRAM. Here, we derive an analytica
l formulation that takes into account the spin-torque asymmetry of the spin polarization function of magnetic tunnel junctions studying. We studied its validity range by comparing the analytical formulas with results achieved numerically within a full micromagnetic framework. We also find that a reasonable fit of the probability density function (PDF) of the switching time is given by a Pearson Type IV PDF. The main results of this work underlines the need of data-driven design of STT-MRAM that uses a full micromagnetic simulation framework for the statistical proprieties PDF of switching processes.
In this work, we propose FUSE, a novel GPU cache system that integrates spin-transfer torque magnetic random-access memory (STT-MRAM) into the on-chip L1D cache. FUSE can minimize the number of outgoing memory accesses over the interconnection networ
k of GPUs multiprocessors, which in turn can considerably improve the level of massive computing parallelism in GPUs. Specifically, FUSE predicts a read-level of GPU memory accesses by extracting GPU runtime information and places write-once-read-multiple (WORM) data blocks into the STT-MRAM, while accommodating write-multiple data blocks over a small portion of SRAM in the L1D cache. To further reduce the off-chip memory accesses, FUSE also allows WORM data blocks to be allocated anywhere in the STT-MRAM by approximating the associativity with the limited number of tag comparators and I/O peripherals. Our evaluation results show that, in comparison to a traditional GPU cache, our proposed heterogeneous cache reduces the number of outgoing memory references by 32% across the interconnection network, thereby improving the overall performance by 217% and reducing energy cost by 53%.
We present an analytical model for calculating energy barrier for the magnetic field-driven domain wall-mediated magnetization reversal of a magneto-resistive random access memory (MRAM) cell and apply it to study thermal stability factor $Delta$ for
various thicknesses of W layers inserted into the free layer (FL) as a function of the cell size and temperature. We find that, by increasing W thickness, the effective perpendicular magnetic anisotropy (PMA) energy density of the FL film monotonically increases, but at the same time, $Delta$ of the cell mainly decreases. Our analysis shows that, in addition to saturation magnetization $M_s$ and exchange stiffness constant $A_mathrm{ex}$ of the FL film, the parameter that quantifies the $Delta$ of the cell is its coercive field $H_c$, rather than the net PMA field $H_k$ of the FL film comprising the cell.
Numerous neural network circuits and architectures are presently under active research for application to artificial intelligence and machine learning. Their physical performance metrics (area, time, energy) are estimated. Various types of neural net
works (artificial, cellular, spiking, and oscillator) are implemented with multiple CMOS and beyond-CMOS (spintronic, ferroelectric, resistive memory) devices. A consistent and transparent methodology is proposed and used to benchmark this comprehensive set of options across several application cases. Promising architecture/device combinations are identified.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
