Lang Zeng

Origin of interfacial perpendicular magnetic anisotropy in MgO/CoFe/metallic capping layer structures

Spin-transfer-torque magnetic random access memory (STT-MRAM) attracts extensive attentions due to its non-volatility, high density and low power consumption. The core device in STT-MRAM is CoFeB/MgO-based magnetic tunnel junction (MTJ), which possesses a high tunnel magnetoresistance ratio as well as a large value of perpendicular magnetic anisotropy (PMA). It has been experimentally proven that a capping layer coating on CoFeB layer is essential to obtain a strong PMA. However, the physical mechanism of such effect remains unclear. In this paper, we investigate the origin of the PMA in MgO/CoFe/metallic capping layer structures by using a first-principles computation scheme. The trend of PMA variation with different capping materials agrees well with experimental results. We find that interfacial PMA in the three-layer structures comes from both the MgO/CoFe and CoFe/capping layer interfaces, which can be analyzed separately. Furthermore, the PMAs in the CoFe/capping layer interfaces are analyzed through resolving the magnetic anisotropy energy by layer and orbital. The variation of PMA with different capping materials is attributed to the different hybridizations of both d and p orbitals via spin-orbit coupling. This work can significantly benefit the research and development of nanoscale STT-MRAM.

All Spin Artificial Neural Networks Based on Compound Spintronic Synapse and Neuron

Artificial synaptic devices implemented by emerging post-CMOS non-volatile memory technologies such as Resistive RAM (RRAM) have made great progress recently. However, it is still a big challenge to fabricate stable and controllable multilevel RRAM. Benefitting from the control of electron spin instead of electron charge, spintronic devices, e.g., magnetic tunnel junction (MTJ) as a binary device, have been explored for neuromorphic computing with low power dissipation. In this paper, a compound spintronic device consisting of multiple vertically stacked MTJs is proposed to jointly behave as a synaptic device, termed as compound spintronic synapse (CSS). Based on our theoretical and experimental work, it has been demonstrated that the proposed compound spintronic device can achieve designable and stable multiple resistance states by interfacial and materials engineering of its components. Additionally, a compound spintronic neuron (CSN) circuit based on the proposed compound spintronic device is presented, enabling a multi-step transfer function. Then, an All Spin Artificial Neural Network (ASANN) is constructed with the CSS and CSN circuit. By conducting system-level simulations on the MNIST database for handwritten digital recognition, the performance of such ASANN has been investigated. Moreover, the impact of the resolution of both the CSS and CSN and device variation on the system performance are discussed in this work.

Energy-efficient neuromorphic computation based on compound spin synapse with stochastic learning

Recently, magnetic tunnel junction with in-plane magnetization (i-MTJ) has been exploited to behave as a binary stochastic synapse. However, it suffers from its limited level of synaptic weight, resulting in an inaccurate learning. In this work, a compound synapse that employs multiple perpendicular MTJs (p-MTJs) in series is proposed. It possesses an analog-like synaptic weight under weak programming conditions, which leads to a stochastic learning rule and low power consumption per synaptic event. By performing system-level simulations on the MNIST database, it has been demonstrated that such compound spin synapses can realize stochastic neuromorphic computation with high accuracy and low energy consumption.

Large influence of capping layers on tunnel magnetoresistance in magnetic tunnel junctions

It has been reported in experiments that capping layers, which enhance the perpendicular magnetic anisotropy (PMA) of magnetic tunnel junctions (MTJs), induce a great impact on the tunnel magnetoresistance (TMR). To explore the essential influence caused by the capping layers, we carry out ab initio calculations on TMR in the X(001)|CoFe(001)|MgO(001)|CoFe(001)|X(001) MTJ, where X represents the capping layer material, which can be tungsten, tantalum, or hafnium. We report TMR in different MTJs and demonstrate that tungsten is an ideal candidate for a giant TMR ratio. The transmission spectrum in Brillouin zone is presented. It can be seen that in the parallel condition of MTJ, sharp transmission peaks appear in the minority-spin channel. This phenomenon is attributed to the resonant tunnel transmission effect, and we explained it by the layer-resolved density of states. In order to explore transport properties in MTJs, the density of scattering states was studied from the point of band symmetry. It has b...

Stochastic spintronic device based synapses and spiking neurons for neuromorphic computation

Spintronics devices such as magnetic tunnel junction (MTJ) have been investigated for the neuromorphic computation. However, there are still a number of challenges for hardware implementation of the bio-inspired computing, for instance how to use the binary MTJ to mimic the analog synapse. In this paper, a compound scheme is firstly proposed, which employs multiple MTJs connected in parallel operating in the stochastic regime to jointly behave a single synapse, aiming to achieve an analog-like weight spectrum. To further exploit its stochastic switching property for the bio-inspired computing, we present a MTJ based stochastic spiking neuron (SSN) circuit, which can also realize the neural rate coding scheme. A case study is made on the MNIST database for handwritten digital recognition with the proposed compound magnetoresistive synapse (CMS) and SSN. System-level simulation results show that the proposed CMS and SSN can implement neuromorphic computation with high accuracy and immunity to device variation.

High-Speed, Low-Power, and Error-Free Asynchronous Write Circuit for STT-MRAM and Logic

Benefiting from its simple switching scheme (only a bidirectional current source), high-speed and low-power spin-transfer torque (STT) has been regarded as one of the most promising switching mechanisms for a magnetic tunnel junction (MTJ)-based non-volatile memory and logic circuits. However, it suffers from a number of reliability issues like write error induced by its intrinsic stochasticity, process variation, and so on. In order to reduce the write error rate, the mainstream solution is to enlarge the write pulse duration at the expense of write energy dissipation. Some self-terminated write circuits have been proposed to avoid the wasted write energy. But the hardware cost of these write circuits is especially large. In this paper, a novel cost-efficient self-terminated write circuit is proposed using two simple built-in sensing circuits. The proposed write circuit is simulated with a physics-based STT-MTJ compact model and a commercial CMOS 40 nm design kit. The simulation result shows about 35% reduction of circuit area and 10% lower energy consumption in comparison with that in prior work. In addition, the Error-Free write operation under process variation of both the CMOS transistor and the STT-MTJ is achieved due to its large sense margin (~320 mV).

Spin wave based synapse and neuron for ultra low power neuromorphic computation system

In this work, we have proposed that the neural synapses and neurons can be realized by utilizing spin waves (SWs) as information carrier. The SWs is excited by spin torque nano-oscillator (STNO), and detected with several different physical mechanisms: 1) tunneling magnetic-resistance 2) spin pumping and 3) inverse spin hall effect. The proposed SWs based synapses and neurons can be further combined together to form a neuromorphic computation system with crossbar structure. Possible ultra low power consumption and ultra high speed are the advantage of our proposed SWs based synapses and neurons.

Channel Modeling and Reliability Enhancement Design Techniques for STT-MRAM

Spin transfer torque magnetic random access memory (STT-MRAM) has emerged as a potential candidate for the next-generation universal memory technology. However, many challenges still exist that block its commercialization and application. One of the main challenges is the reliability concerns, especially as technology scales down to nanometer nodes. For example, the intrinsic stochastic switching characteristics and asymmetry of the STT driven magnetic tunnel junction (MTJ), as well as the process, voltage and temperature (PVT) variations of the manufacturing process. Generally, there exists a technical gap between the device-level designers and system-level circuit or system designers for the STT-MRAM optimizations. To our knowledge, no efficient evaluation tools have been proposed to provide an effective link between them until now. In this paper, we aim to firstly give quantitative analyses of the reliability issues of STT-MRAM, taking into consideration the physical properties and manufacturing process. Secondly, based on these analysis results, a channel model from informative perspective would then be proposed. This model provides an effective simulation tool to evaluate the reliability performance of STT-MRAM. Finally, reliability enhancement design considerations and strategies are presented for the STT-MRAM circuit and system designers.

Compact modeling of high spin transfer torque efficiency double-barrier magnetic tunnel junction

The considerable power consumption on logic and memory circuit system will be an unavoidable bottleneck with the shrinking of complementary metal oxide semiconductor (CMOS) technology size. One promising solution is to build non-volatile spintronic device, e.g. spin transfer torque magnetic random access memory (STT-MRAM). The basic storage unit of STT-MRAM, i.e. magnetic tunnel junction (MTJ), has thus been extensively studied. Double-barrier MTJ (DMTJ), as an optimized structure, enhances the STT effect with a second MgO barrier layer and reduces its critical switching current. In this paper, we present a physics-based compact model of CoFeB/MgO DMTJ with perpendicular magnetic anisotropy (PMA). The modeling results show a great agreement with experimental results. More efficient STT switching and similar magnetoresistance features compared with single-barrier MTJ (SMTJ) can be realized. Mixed circuits simulations have also been carried out to validate its functionality. This SPICE-compatible compact model will be useful for high-performance hybrid DMTJ/CMOS circuit and system designs.

Frequency modulation of spin torque nano oscillator with voltage controlled magnetic anisotropy effect

In this work, a novel Spin Torque Nano Oscillator (STNO) device which can sustain high frequency oscillation without demand of bias magnetic field is proposed. Voltage Controlled Magnetic Anisotropy (VCMA) effect is employed to increase perpendicular anisotropy field leading to high oscillation frequency in the absence of bias magnetic field. In addition, VCMA effect also can be utilized as frequency modulation approach to realize signal modulation for STNO used in communication system.