arXiv subjects

Kerem Y. Camsari

Publications and source records attributed to Kerem Y. Camsari.

64 records · Page 4Linked to original sources

arXiv2019

Demonstration of a strain-mediated magnetoelectric write and read unit in a Co60Fe20B20/ Pb(Mg1/3Nb2/3)0.7Ti0.3O3 heterostructure

Taking advantage of the Magnetoelectric (ME) and its inverse effect, this article demonstrates strain-mediated magnetoelectric write and read operations simultaneously in Co60Fe20B20/ Pb(Mg1/3Nb2/3)0.7Ti0.3O3 heterostructures without using any symmetry breaking magnetic field at room temperature. By applying an external DC-voltage across a (011)-cut PMN-PT substrate, the ferroelectric polarization is re-oriented, which results in an anisotropic in-plane strain that transfers to the CoFeB thin film and changes its magnetic anisotropy Hk. The change in Hk in-turn results in a 90o rotation of the magnetic easy axis for sufficiently high voltages. Simultaneously, the inverse effect is employed to read changes of the magnetic properties. Because the Piezoelectric (PE)/FerroMagnetic (FM) system is fully coupled, the change of magnetization in FM induces an elastic stress in the PE layer, which generates a piezoelectric potential in the system that can be used to readout the magnetic state of the FM layer. Our experimental results are in excellent qualitative agreement with a recently proposed, experimentally benchmarked equivalent circuit model that considers how magnetic properties are electrically controlled in such ME/PE heterostructure and how a back-voltage is generated due to changing magnetic properties in a self-consistent model.

physics.app-ph↗

arXiv2018

Analog Signal Processing Using Stochastic Magnets

We present a low barrier magnet based compact hardware unit for analog stochastic neurons and demonstrate its use as a building-block for neuromorphic hardware. By coupling circular magnetic tunnel junctions (MTJs) with a CMOS based analog buffer, we show that these units can act as leaky-integrate-and fire (LIF) neurons, a model of biological neural networks particularly suited for temporal inferencing and pattern recognition. We demonstrate examples of temporal sequence learning, processing, and prediction tasks in real time, as a proof of concept demonstration of scalable and adaptive signal-processors. Efficient non von-Neumann hardware implementation of such processors can open up a pathway for integration of hardware based cognition in a wide variety of emerging systems such as IoT, industrial controls, bio- and photo-sensors, and Unmanned Autonomous Vehicles.

cs.ET↗

arXiv2018

Composable Probabilistic Inference Networks Using MRAM-based Stochastic Neurons

Magnetoresistive random access memory (MRAM) technologies with thermally unstable nanomagnets are leveraged to develop an intrinsic stochastic neuron as a building block for restricted Boltzmann machines (RBMs) to form deep belief networks (DBNs). The embedded MRAM-based neuron is modeled using precise physics equations. The simulation results exhibit the desired sigmoidal relation between the input voltages and probability of the output state. A probabilistic inference network simulator (PIN-Sim) is developed to realize a circuit-level model of an RBM utilizing resistive crossbar arrays along with differential amplifiers to implement the positive and negative weight values. The PIN-Sim is composed of five main blocks to train a DBN, evaluate its accuracy, and measure its power consumption. The MNIST dataset is leveraged to investigate the energy and accuracy tradeoffs of seven distinct network topologies in SPICE using the 14nm HP-FinFET technology library with the nominal voltage of 0.8V, in which an MRAM-based neuron is used as the activation function. The software and hardware level simulations indicate that a $784\times200\times10$ topology can achieve less than 5% error rates with $\sim400 pJ$ energy consumption. The error rates can be reduced to 2.5% by using a $784\times500\times500\times500\times10$ DBN at the cost of $\sim10\times$ higher energy consumption and significant area overhead. Finally, the effects of specific hardware-level parameters on power dissipation and accuracy tradeoffs are identified via the developed PIN-Sim framework.

cs.ET↗

arXiv2018

Weighted p-bits for FPGA implementation of probabilistic circuits

Probabilistic spin logic (PSL) is a recently proposed computing paradigm based on unstable stochastic units called probabilistic bits (p-bits) that can be correlated to form probabilistic circuits (p-circuits). These p-circuits can be used to solve problems of optimization, inference and also to implement precise Boolean functions in an "inverted" mode, where a given Boolean circuit can operate in reverse to find the input combinations that are consistent with a given output. In this paper we present a scalable FPGA implementation of such invertible p-circuits. We implement a "weighted" p-bit that combines stochastic units with localized memory structures. We also present a generalized tile of weighted p-bits to which a large class of problems beyond invertible Boolean logic can be mapped, and how invertibility can be applied to interesting problems such as the NP-complete Subset Sum Problem by solving a small instance of this problem in hardware.

cs.ET↗

arXiv2018

Modular Compact Modeling of Magnetic Tunnel Junction Devices

This paper describes a robust, modular, and physics- based circuit framework to model conventional and emerging Magnetic Tunnel Junction (MTJ) devices. Magnetization dynamics are described by the stochastic Landau-Lifshitz-Gilbert (sLLG) equation whose results are rigorously benchmarked with a Fokker-Planck Equation (FPE) description of magnet dynamics. We then show how sLLG is coupled to transport equations of MTJ-based devices in a unified circuit platform. Step by step, we illustrate how the physics-based MTJ model can be extended to include different spintronics phenomena, including spin-transfer-torque (STT), voltage-control of magnetic anisotropy (VCMA) and spin-orbit torque (SOT) phenomena by experimentally benchmarked examples. To demonstrate how our approach can be used in the exploration of novel MTJ-based devices, we also present a recently proposed MEMS resonator- driven spin-torque nano oscillator (STNO) that can reduce the phase noise of STNOs. We briefly elaborate on the use of our framework beyond conventional devices.

cond-mat.mes-hall↗

arXiv2018

Direct Observation of Valley Coupled Topological Current in MoS$_2$

The valley degree of freedom of electrons in two-dimensional transition metal dichalcogenides has been extensively studied by theory, optical and optoelectronic experiments. However, generation and detection of pure valley current without relying on optical selection have not yet been demonstrated in these materials. Here, we report that valley current can be electrically induced and detected through the valley Hall effect and inverse valley Hall effect, respectively, in monolayer molybdenum disulfide. Specifically, long-range valley transport is observed over half a micron distance at room temperature. Our findings will enable a new generation of electronic devices utilizing the valley degree of freedom, which can be used for future novel valleytronic applications.

cond-mat.mes-hall↗

arXiv2018

Equivalent Circuit for Magnetoelectric Read and Write Operations

We describe an equivalent circuit model applicable to a wide variety of magnetoelectric phenomena and use SPICE simulations to benchmark this model against experimental data. We use this model to suggest a different mode of operation where the "1" and "0'" states are not represented by states with net magnetization (like $m_x$, $m_y$ or $m_z$) but by different easy axes, quantitatively described by ($m_x^2 - m_y^2$) which switches from "0" to "1" through the write voltage. This change is directly detected as a read signal through the inverse effect. The use of ($m_x^2 - m_y^2$) to represent a bit is a radical departure from the standard convention of using the magnetization ($m$) to represent information. We then show how the equivalent circuit can be used to build a device exhibiting tunable randomness and suggest possibilities for extending it to non-volatile memory with read and write capabilities, without the use of external magnetic fields or magnetic tunnel junctions.

cond-mat.mes-hall↗

arXiv2018

Implementing Bayesian Networks with Embedded Stochastic MRAM

Magnetic tunnel junctions (MTJ's) with low barrier magnets have been used to implement random number generators (RNG's) and it has recently been shown that such an MTJ connected to the drain of a conventional transistor provides a three-terminal tunable RNG or a $p$-bit. In this letter we show how this $p$-bit can be used to build a $p$-circuit that emulates a Bayesian network (BN), such that the correlations in real world variables can be obtained from electrical measurements on the corresponding circuit nodes. The $p$-circuit design proceeds in two steps: the BN is first translated into a behavioral model, called Probabilistic Spin Logic (PSL), defined by dimensionless biasing (h) and interconnection (J) coefficients, which are then translated into electronic circuit elements. As a benchmark example, we mimic a family tree of three generations and show that the genetic relatedness calculated from a SPICE-compatible circuit simulator matches well-known results.

cs.ET↗

arXiv2018

Magneto Acoustic Spin Hall Oscillators

This paper introduces a novel oscillator that combines the tunability of spin Hall-driven nano oscillators with the high quality factor (Q) of high overtone bulk acoustic wave resonators (HBAR), integrating both reference and tunable oscillators on the same chip with CMOS. In such magneto acoustic spin Hall (MASH) oscillators, voltage oscillations across the magnetic tunnel junction (MTJ) that arise from a spin-orbit torque (SOT) are shaped by the transmission response of the HBAR that acts as a multiple peak-bandpass filter and a delay element due to its large time constant, providing delayed feedback. The filtered voltage oscillations can be fed back to the MTJ via a) strain, b) current, or c) magnetic field. We develop a SPICE-based circuit model by combining experimentally benchmarked models including the stochastic Landau-Lifshitz-Gilbert (sLLG) equation for magnetization dynamics and the Butterworth Van Dyke (BVD) circuit for the HBAR. Using the self-consistent model, we project up to $\sim$ 50X enhancement in the oscillator linewidth with Q reaching up to 52825 at 3 GHz, while preserving the tunability by locking the STNO to the nearest high Q peak of the HBAR. We expect that our results will inspire MEMS-based solutions to spintronic devices by combining attractive features of both fields for a variety of applications.

cond-mat.mes-hall↗

arXiv2017

Reservoir Computing using Stochastic p-Bits

We present a general hardware framework for building networks that directly implement Reservoir Computing, a popular software method for implementing and training Recurrent Neural Networks and are particularly suited for temporal inferencing and pattern recognition. We provide a specific example of a candidate hardware unit based on a combination of soft-magnets, spin-orbit materials and CMOS transistors that can implement these networks. Efficient non von-Neumann hardware implementation of reservoir computers can open up a pathway for integration of temporal Neural Networks in a wide variety of emerging systems such as Internet of Things (IoTs), industrial controls, bio- and photo-sensors, and self-driving automotives.

cs.ET↗