This paper presents the design and implementation of a miniaturized, low-noise Magnetic Tunnel Junction (MTJ)-based proximity sensor with a high-performance readout channel. The MTJ-based proximity sensor consists of 1102 circular pillars of 100um diameter arranged in series, providing accurate detection of subtle interactions, such as a finger approaching the sensor. The system exhibits a 54 dB gain and a bandwidth of 1 kHz, with a noise power density of less than 30 nV/√Hz at 100 Hz, ensuring high precision. The proximity sensor demonstrated linear behavior for distances from 18 mm to 45 mm, with a sensitivity sufficient to detect low magnetic field variations. Experimental validation of the sensor shows a high degree of accuracy (R2= 0.9715), confirming its potential for use in touchless control, mobile technology, and industrial applications.

This paper presents a novel approach to reservoir computing (RC) using Granular Vortex-Based Magnetic Tunnel Junctions (GV-MTJs) for temporal applications. GV-MTJs, with their unique magnetic domain configurations and granular structures, provide the necessary fading memory and non-linear dynamics essential for RC. The vortex core’s oscillatory motion within the device allows for temporal correlation of inputs, giving fading memory, while grain-induced non-linear resistance and frequency variations enhance data dimensionality. Our findings indicate that varying device parameters can affect the relaxation time and gyrotropic frequency in both simulation and experiments. Relaxation times range from 100-140 ns and frequencies from 250-100 MHz. Through experiments, the classification error was reduced by 27% for the best sample, others showed limited potential. Due to signal application speed constraints, the fading memory is not fully utilized. However, the inherent RC capabilities of GV-MTJs are validated. This paper highlights the promise of GV-MTJs in neuromorphic computing and suggests avenues for future research to optimise their use in practical applications.

With the rapidly evolving internet of things (IoT) era, the ever-rising demand for data transfer and storage has put a knotty problem on conventional computers, known as the von Neumann bottleneck and memory wall problem. Slow scaling of CMOS transistors due to physical and economical limitations further exacerbates the situation. It is only logical to mimic what has been known so far as the most energy-efficient system, the human brain. The brain-inspired neuromorphic computing systems compute and store the data locally, which dramatically reduces area and energy consumption. In this work, we demonstrate thermal-induced multi-state memristors for neuromorphic engineering applications. We show that in a neural network that uses a memristor-spintronic nano oscillator connection to implement the synapse-neuron pair, with increased temperature, the total power consumption could be reduced by more than 50 % without degrading the output power of a spintronic-based neuron.

Many classification applications rely on real-time processing and detection of RF signals at high frequencies. RF signal sampling requires sophisticated hardware, i.e., broadband analog front-ends and high-speed analog-to-digital converters according to the well-known Shannon-Nyquist theorem. Such devices either are expensive or suffer from limited detection bandwidths and sampling rates. Here, we proposed a novel spintronic-based classification front-end for real-time analysis and classification of RF signals. In comparison to the conventional CMOS-based systems, the proposed system can increase the classification speed dramatically while consuming an order of magnitude less power.