FS-06 Domain Wall and Skyrmion Devices -based Spiking Neural Networks
Creators: Aijaz H. Lone
Daniel N. Rahimi
Meng Tang
D. Divyanshu
Selma Amara and Gianluca Setti
Publisher: name: IEEE
Year: 2025

Using domain walls and skyrmions in Spintronic devices has emerged as a promising avenue for advancing energy-efficient data storage and non-conventional computing architectures[1-4]. Leveraging magnetic multilayer structures (shown in Fig. 1(a)), our study showcases the realization of a magnetic field-gated and current-controlled Leaky Integrate and Fire (LIF) neuron device. The measured magnetic hysteresis loop demonstrates a multidomain magnetic profile (see Fig. 1(b). The functionality of these devices resembles that of gate-controlled LIF neurons, as shown in Fig. 1(c), resembling spintronic Mem-transistor devices. The current pulses facilitate domain wall motion, whereas the external magnetic field serves as the bias for tuning neuron firing properties. Our investigation demonstrates the controllability of neuron spiking rates through current and magnetic field modulation, as shown in Fig. 2(c). We have developed a comprehensive LIF neuron model(shown in Fig. 1(c)) based on measurements, showcasing the seamless integration of these devices within system-level SNN frameworks as shown in Fig. 2. Furthermore, we propose a scaled version of multilayer spintronic domain wall MTJ as LIF neurons, LIF characteristics is achieved through a combination of spin-orbit torque (SOT) and manipulation of demagnetization energy within thin films. Subsequently, we integrate both experimentally derived and simulated neuron models into a 3-layer SNN (Fig. 2(a)) and Convolutional Spiking Neural Network (CSNN) (Fig. 2(b)) framework, demonstrating their efficacy in accurately classifying MNIST and FMNIST datasets, achieving classification accuracies surpassing 96% (shown in Fig. 2(d)). These findings underscore the potential of the presented devices as viable alternatives for neuromorphic computing applications, owing to their commendable system-level performance, Mem-transistor attributes, and prospects for scalability.References: References: [1] Kyung Mee Song et al., “Skyrmion-based artificial synapses for neuromorphic computing. Nat Electron,” 3(3):148–155, March 2020. [2] Abhronil Sengupta et al., “Spin-orbit torque induced spike-timing dependent plasticity,” Applied Physics Letters, 106(9):093704, March 2015. [3] Xing Chen et al. “A compact skyrmionic leaky–integrate–fire spiking neuron device,” Nanoscale, 10:6139–6146, 2018. [4] Aijaz H. Lone et al., “Skyrmion-Magnetic Tunnel Junction Synapse With Long-Term and Short-Term Plasticity for Neuromorphic Computing,” IEEE Trans. Electron Devices, 70(1):371–378, January 2023
Using domain walls and skyrmions in Spintronic devices has emerged as a promising avenue for advancing energy-efficient data storage and non-conventional computing architectures[1-4]. Leveraging magnetic multilayer structures (shown in Fig. 1(a)), our study showcases the realization of a magnetic field-gated and current-controlled Leaky Integrate and Fire (LIF) neuron device. The measured magnetic hysteresis loop demonstrates a multidomain magnetic profile (see Fig. 1(b). The functionality of these devices resembles that of gate-controlled LIF neurons, as shown in Fig. 1(c), resembling spintronic Mem-transistor devices. The current pulses facilitate domain wall motion, whereas the external magnetic field serves as the bias for tuning neuron firing properties. Our investigation demonstrates the controllability of neuron spiking rates through current and magnetic field modulation, as shown in Fig. 2(c). We have developed a comprehensive LIF neuron model(shown in Fig. 1(c)) based on measurements, showcasing the seamless integration of these devices within system-level SNN frameworks as shown in Fig. 2. Furthermore, we propose a scaled version of multilayer spintronic domain wall MTJ as LIF neurons, LIF characteristics is achieved through a combination of spin-orbit torque (SOT) and manipulation of demagnetization energy within thin films. Subsequently, we integrate both experimentally derived and simulated neuron models into a 3-layer SNN (Fig. 2(a)) and Convolutional Spiking Neural Network (CSNN) (Fig. 2(b)) framework, demonstrating their efficacy in accurately classifying MNIST and FMNIST datasets, achieving classification accuracies surpassing 96% (shown in Fig. 2(d)). These findings underscore the potential of the presented devices as viable alternatives for neuromorphic computing applications, owing to their commendable system-level performance, Mem-transistor attributes, and prospects for scalability.References: References: [1] Kyung Mee Song et al., “Skyrmion-based artificial synapses for neuromorphic computing. Nat Electron,” 3(3):148–155, March 2020. [2] Abhronil Sengupta et al., “Spin-orbit torque induced spike-timing dependent plasticity,” Applied Physics Letters, 106(9):093704, March 2015. [3] Xing Chen et al. “A compact skyrmionic leaky–integrate–fire spiking neuron device,” Nanoscale, 10:6139–6146, 2018. [4] Aijaz H. Lone et al., “Skyrmion-Magnetic Tunnel Junction Synapse With Long-Term and Short-Term Plasticity for Neuromorphic Computing,” IEEE Trans. Electron Devices, 70(1):371–378, January 2023