Multilayer Ferromagnetic Spintronic Devices for Neuromorphic Computing Applications

Spintronics has gone through substantial progress due to its applications in energy-efficient memory, logic and unconventional computing paradigms. Multilayer ferromagnetic thin films are extensively studied for understanding the domain wall and skyrmion dynamics. However, most of these studies are confined to the materials and domain wall/skyrmion physics. In this paper, we present the experimental and micromagnetic realization of a multilayer ferromagnetic spintronic device for neuromorphic computing applications. The device exhibits multilevel resistance states and the number of resistance states increases with lowering temperature. This is supported by the multilevel magnetization behavior observed in the micromagnetic simulations. Furthermore, the evolution of resistance states with spin-orbit torque is also explored in experiments and simulations. Using the multi-level resistance states of the device, we propose its applications as a synaptic device in hardware neural networks and study the linearity performance of the synaptic devices. The neural network based on these devices is trained and tested on the MNIST dataset using a supervised learning algorithm. The devices at the chip level achieve 90\% accuracy. Thus, proving its applications in neuromorphic computing. Furthermore, we lastly discuss the possible application of the device in cryogenic memory electronics for quantum computers

An interconnect-free micro-electromechanical 7-bit arithmetic device for multi-operand programmable computing

Abstract Computational power density and interconnection between transistors have grown to be the dominant challenges for the continued scaling of complementary metal–oxide–semiconductor (CMOS) technology due to limited integration density and computing power. Herein, we designed a novel, hardware-efficient, interconnect-free microelectromechanical 7:3 compressor using three microbeam resonators. Each resonator is configured with seven equal-weighted inputs and multiple driven frequencies, thus defining the transformation rules for transmitting resonance frequency to binary outputs, performing summation operations, and displaying outputs in compact binary format. The device achieves low power consumption and excellent switching reliability even after 3 × 103 repeated cycles. These performance improvements, including enhanced computational power capacity and hardware efficiency, are paramount for moderately downscaling devices. Finally, our proposed paradigm shift for circuit design provides an attractive alternative to traditional electronic digital computing and paves the way for multioperand programmable computing based on electromechanical systems