Magnon scattering modulated by omnidirectional hopfion motion in antiferromagnets for meta-learning.

Neuromorphic computing is expected to achieve human-brain performance by reproducing the structure of biological neural systems. However, previous neuromorphic designs based on synapse devices are all unsatisfying for their hardwired network structure and limited connection density, far from their biological counterpart, which has high connection density and the ability of meta-learning. Here, we propose a neural network based on magnon scattering modulated by an omnidirectional mobile hopfion in antiferromagnets. The states of neurons are encoded in the frequency distribution of magnons, and the connections between them are related to the frequency dependence of magnon scattering. Last, by controlling the hopfion's state, we can modulate hyperparameters in our network and realize the first meta-learning device that is verified to be well functioning. It not only breaks the connection density bottleneck but also provides a guideline for future designs of neuromorphic devices.

Anomalous Thermal-Assisted Spin-Orbit Torque-Induced Magnetization Switching for Energy-Efficient Logic-in-Memory.

Spin-orbit torque (SOT) is widely considered as an effective route to manipulate magnetic order in spintronic devices. The low power consumption and long endurance demands from future computer architectures urgently require a reduction of the critical SOT switching current density, jsw. However, except for searching for a SOT source with a high-spin Hall angle, few efficient mechanisms to reduce jsw have been proposed. In this work, we achieved an anomalous thermal-assisted (TA) jsw reduction in a Pt/Co/Tb heterostructure through engineering a ferrimagnetic Co/Tb interface. This jsw reduction tendency is demonstrated to be strongly dependent on the thickness of Tb, tTb. When tTb reaches an optimal point (3 nm), a 74 K temperature increase will reduce jsw by more than an order of magnitude (17 times). Comparison experiments and theoretical simulations indicate that this anomalous TA reduction behavior goes beyond the conventional SOT framework and originates from the temperature-sensitive ferrimagnetic interface. We further propose a multifunctional logic-in-memory device, where six different Boolean logic gates can be implemented, to demonstrate the application potential and energy efficiency of this TA SOT switching mechanism. Our work provides an effective alternative to reduce jsw in SOT devices and may inspire future spintronic memory, logic, and high-frequency devices.

High On/Off Ratio Spintronic Multi-Level Memory Unit for Deep Neural Network.

Spintronic devices are considered as one of the most promising technologies for non-volatile memory and computing. However, two crucial drawbacks, that is, lack of intrinsic multi-level operation and low on/off ratio, greatly hinder their further application for advanced computing concepts, such as deep neural network (DNN) accelerator. In this paper, a spintronic multi-level memory unit with high on/off ratio is proposed by integrating several series-connected magnetic tunnel junctions (MTJs) with perpendicular magnetic anisotropy (PMA) and a Schottky diode in parallel. Due to the rectification effect on the PMA MTJ, an on/off ratio over 100, two orders of magnitude higher than intrinsic values, is obtained under proper proportion of alternating current and direct current. Multiple resistance states are stably achieved and can be reconfigured by spin transfer torque effect. A computing-in-memory architecture based DNN accelerator for image classification with the experimental parameters of this proposal to evidence its application potential is also evaluated. This work can satisfy the rigorous requirements of DNN for memory unit and promote the development of high-accuracy and robust artificial intelligence applications.