Double-Free-Layer Stochastic Magnetic Tunnel Junctions with Synthetic Antiferromagnets

Abstract

Stochastic magnetic tunnel junctions (sMTJ) using low-barrier nanomagnets have shown promise as fast, energy-efficient, and scalable building blocks for probabilistic computing. Despite recent experimental and theoretical progress, sMTJs exhibiting the ideal characteristics necessary for probabilistic bits (p-bit) are still lacking. Ideally, the sMTJs should have (a) voltage bias independence preventing read disturbance (b) uniform randomness in the magnetization angle between the free layers, and (c) fast fluctuations without requiring external magnetic fields while being robust to magnetic field perturbations. Here, we propose a new design satisfying all of these requirements, using double-free-layer sMTJs with synthetic antiferromagnets (SAF). We evaluate the proposed sMTJ design with experimentally benchmarked spin-circuit models accounting for transport physics, coupled with the stochastic Landau-Lifshitz-Gilbert equation for magnetization dynamics. We find that the use of low-barrier SAF layers reduces dipolar coupling, achieving uncorrelated fluctuations at zero-magnetic field surviving up to diameters exceeding ($D\approx 100$ nm) if the nanomagnets can be made thin enough ($\approx 1$-$2$ nm). The double-free-layer structure retains bias-independence and the circular nature of the nanomagnets provides near-uniform randomness with fast fluctuations. Combining our full sMTJ model with advanced transistor models, we estimate the energy to generate a random bit as $\approx$ 3.6 fJ, with fluctuation rates of $\approx$ 3.3 GHz per p-bit. Our results will guide the experimental development of superior stochastic magnetic tunnel junctions for large-scale and energy-efficient probabilistic computation for problems relevant to machine learning and artificial intelligence.

References in corpus (23)

• Measurement of the Spin-Transfer-Torque Vector in Magnetic Tunnel Junctions
• Non-collinear Magnetoelectronics
• Stochastic p-bits for Invertible Logic
• Massively Parallel Probabilistic Computing with Sparse Ising Machines
• Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing
• A full-stack view of probabilistic computing with p-bits: devices, architectures and algorithms
• Demonstration of nanosecond operation in stochastic magnetic tunnel junctions
• Hardware-aware $insitu$ Boltzmann machine learning using stochastic magnetic tunnel junctions
• Energy-efficient stochastic computing with superparamagnetic tunnel junctions
• CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning
• Low Barrier Magnet Design for Efficient Hardware Binary Stochastic Neurons
• Scalable Emulation of Sign-Problem$-$Free Hamiltonians with Room Temperature p-bits
• Subnanosecond Fluctuations in Low-Barrier Nanomagnets
• Modular Compact Modeling of Magnetic Tunnel Junction Devices
• Double Free-Layer Magnetic Tunnel Junctions for Probabilistic Bits
• Efficient Probabilistic Computing with Stochastic Perovskite Nickelates
• Quantitative Evaluation of Hardware Binary Stochastic Neurons
• Stochastic magnetic actuated random transducer devices based on perpendicular magnetic tunnel junctions
• Accelerated Quantum Monte Carlo with Probabilistic Computers
• A Performance Study of the 2D Ising Model on GPUs
• Mutual control of stochastic switching for two electrically coupled superparamagnetic tunnel junctions
• Lanthanide molecular nanomagnets as probabilistic bits
• Emulating Quantum Interference with Generalized Ising Machines

Cited by in corpus (3)

• Voltage-insensitive stochastic magnetic tunnel junctions with double free layers
• Connecting physics to systems with modular spin-circuits
• Metrics for spin-based computing