Search

Neuromorphic computing, inspired by the brain's parallel and energy-efficient processing, offers a transformative approach to artificial intelligence. In this study, we fabricated optimized spin-transfer torque nano-oscillators (STNOs) and investigated their dynamic behaviors using a hybrid excitation scheme combining AC laser illumination and DC bias currents. Laser-induced thermal gradients generate pulsed thermoelectric voltages ($V_{\text{AC}}$) via the Tunnel Magneto-Seebeck (TMS) effect, while the addition of bias currents enhances this response, producing both $V_{\text{AC}}$ and a DC component ($V_{\text{DC}}$). Magnetic field sweeps reveal distinct switching between parallel (P) and antiparallel (AP) magnetization states in both voltage components, supporting multistate memory applications. Millivolt-range thermovoltage signals in open-circuit conditions demonstrate CMOS compatibility, enabling simplified, scalable neuromorphic systems. Under biased conditions, enhanced thermovoltage outputs exhibit intriguing phenomena, including spikes correlated with Barkhausen jumps and double-switching behavior, offering insights into magnetization dynamics and vortex transitions. These features resemble neural spiking behavior, suggesting applications in spiking neural networks, reservoir computing, multistate logic, analog computing, and high-resolution sensing. By bridging spintronic phenomena with practical applications, this work provides a versatile platform for next-generation AI technologies and adaptive computing architectures.

As nascent nonlinear oscillators, nano-constriction spin Hall nano-oscillators (SHNOs) represent a promising potential for integration into more complicated systems such as neural networks, magnetic field sensors, and radio frequency (RF) signal classification, their tunable high-frequency operating regime, easy synchronization, and CMOS compatibility can streamline the process. To implement SHNOs in any of these networks, the electrical features of a single device are needed before designing the signal detection CMOS circuitry. This study centers on presenting an empirical electrical model of the SHNO based on a comprehensive characterization of the output impedance of a single SHNO, and its available output power in the range of 2-10 GHz at various bias currents., Comment: 6 pages

Spintronic THz emitters have attracted much attention due to their desirable properties, such as affordability, ultra-wideband capability, high efficiency, and tunable polarization. In this study, we investigate the characteristics of THz signals, including their frequency, bandwidth, and amplitude, emitted from a series of heterostructures with ferromagnetic (FM) and nonmagnetic (NM) materials. The FM layer consists of a wedge-shaped CoFeB layer with a thickness of 0 to 5 nm, while the NM materials include various metals such as Pt, Au, W, Ru, Pt$_{\%92}$Bi$_{\%8}$, and Ag$_{\%90}$Bi$_{\%10}$ alloys. Our experiments show that the emitter with Pt-NM layer has the highest amplitude of the emitted THz signal. However, the PtBi-based emitter exhibits a higher central THz peak and wider bandwidth, making it a promising candidate for broadband THz emitters. These results pave the way for further exploration of the specific compositions of Pt$_{1-x}$Bi$_{x}$ for THz emitter design, especially with the goal of generating higher frequency and wider bandwidth THz signals. These advances hold significant potential for applications in various fields such as high-resolution imaging, spectroscopy, communications, medical diagnostics, and more.

We have developed and optimized two categories of spin transfer torque magnetic tunnel junctions (STT-MTJs) that exhibit a high tunnel magnetoresistance (TMR) ratio, low critical current, high outputpower in the micro watt range, and auto-oscillation behavior. These characteristics demonstrate the potential of STT-MTJs for low-power, high-speed, and reliable spintronic applications, including magnetic memory, logic, and signal processing. The only distinguishing factor between the two categories, denoted as A-MTJs and B-MTJs, is the composition of their free layers, 2 CoFeB/0.21 Ta/6 CoFeSiB for A-MTJs and 2 CoFeB/0.21 Ta/7 NiFe for B-MTJs. Our study reveals that B-MTJs exhibit lower critical currents for auto-oscillation than A-MTJs. We found that both stacks have comparable saturation magnetization and anisotropy field, suggesting that the difference in auto-oscillation behavior is due to the higher damping of A-MTJs compared to B-MTJs. To verify this hypothesis, we employed the all-optical time-resolved magneto-optical Kerr effect (TRMOKE) technique, which confirmed that STT-MTJs with lower damping exhibited auto-oscillation at lower critical current values. Additionally, our study aimed to optimize the STT-MTJ performance by investigating the impact of the capping layer on the device's response to electronic and optical stimuli.

Although Analog-to-digital converters (ADCs) are critical components in mixed-signal integrated circuits (IC), their performance has not been improved significantly over the last decade. To achieve a radical improvement (compact, low power and reliable ADCs), spintronics can be considered as a proper candidate due to its compatibility with CMOS and wide applications in storage, neuromorphic computing, and so on. In this paper, a proof-of-concept of a 3-bit spin-CMOS Flash ADC using in-plane-anisotropy magnetic tunnel junctions (i-MTJs) with spin-orbit torque (SOT) switching mechanism is designed, fabricated and characterized. The proposed ADC replaces the current mirrors and power-hungry comparators in the conventional Flash ADC with seven parallel i-MTJs with different heavy metal (HM) widths. Monte-Carlo simulations based on the experimental measurements show the process variations/mismatch limits the accuracy of the proposed ADC to 2 bits. Moreover, the maximum differential nonlinearity (DNL) and integral nonlinearity (INL) are 0.739 LSB (least significant bit) and 0.7319 LSB, respectively.

Therapeutic intervention in neurological disorders still relies heavily on pharmacological solutions, while the treatment of patients with drug resistance remains an open challenge. This is particularly true for patients with epilepsy, 30% of whom are refractory to medications. Implantable devices for chronic recording and electrical modulation of brain activity have proved a viable alternative in such cases. To operate, the device should detect the relevant electrographic biomarkers from Local Field Potentials (LFPs) and determine the right time for stimulation. To enable timely interventions, the ideal device should attain biomarker detection with low latency while operating under low power consumption to prolong the battery life. Neuromorphic networks have progressively gained reputation as low-latency low-power computing systems, which makes them a promising candidate as processing core of next-generation implantable neural interfaces. Here we introduce a fully-analog neuromorphic device implemented in CMOS technology for analyzing LFP signals in an in vitro model of acute ictogenesis. We show that the system can detect ictal and interictal events with ms-latency and with high precision, consuming on average 3.50 nW during the task. Our work paves the way to a new generation of brain implantable devices for personalized closed-loop stimulation for epilepsy treatment., Comment: 14 pages, 6 figures

With the recent developments in neuroscience and engineering, it is now possible to record brain signals and decode them. Also, a growing number of stimulation methods have emerged to modulate and influence brain activity. Current brain-computer interface (BCI) technology is mainly on therapeutic outcomes, it already demonstrated its efficiency as assistive and rehabilitative technology for patients with severe motor impairments. Recently, artificial intelligence (AI) and machine learning (ML) technologies have been used to decode brain signals. Beyond this progress, combining AI with advanced BCIs in the form of implantable neurotechnologies grants new possibilities for the diagnosis, prediction, and treatment of neurological and psychiatric disorders. In this context, we envision the development of closed loop, intelligent, low-power, and miniaturized neural interfaces that will use brain inspired AI techniques with neuromorphic hardware to process the data from the brain. This will be referred to as Brain Inspired Brain Computer Interfaces (BI-BCIs). Such neural interfaces would offer access to deeper brain regions and better understanding for brain's functions and working mechanism, which improves BCIs operative stability and system's efficiency. On one hand, brain inspired AI algorithms represented by spiking neural networks (SNNs) would be used to interpret the multimodal neural signals in the BCI system. On the other hand, due to the ability of SNNs to capture rich dynamics of biological neurons and to represent and integrate different information dimensions such as time, frequency, and phase, it would be used to model and encode complex information processing in the brain and to provide feedback to the users. This paper provides an overview of the different methods to interface with the brain, presents future applications and discusses the merger of AI and BCIs., Comment: 44 Pages, 12 Figures, In review

This paper presents an analytical design of an ultrasonic power transfer system based on piezoelectric micro-machined ultrasonic transducer (PMUT) for fully wireless brain implants in mice. The key steps like the material selection of each layer and the top electrode radius to maximize the coupling factor are well-detailed. This approach results in the design of a single cell with a high effective coupling coefficient. Furthermore, compact models are used to make the design process less time-consuming for designers. These models are based on the equivalent circuit theory for the PMUT. A cell of 107 um in radius, 5 um in thickness of Lead Zirconate Titanium (PZT), and 10 um in thickness of silicon (Si) is found to have a 4% of effective coupling coefficient among the highest values for a clamped edge boundary conditions. Simulation results show a frequency of 2.84 MHz as resonance. In case of an array, mutual impedance and numerical modeling are used to estimate the distance between the adjacent cells. In addition, the area of the proposed transducer and the number of cells are computed with the Rayleigh distance and neglecting the cross-talk among cells, respectively. The designed transducer consists of 7x7 cells in an area of 3.24 mm2. The transducer is able to deliver an acoustic intensity of 7.185 mW/mm2 for a voltage of 19.5 V for powering brain implants seated in the motor cortex and striatum of the mice's brain. The maximum acoustic intensity occurs at a distance of 2.5 mm in the near field which was estimated with the Rayleigh length equation.

Spintronics, the use of spin of an electron instead of its charge, has received huge attention from research communities for different applications including memory, interconnects, logic implementation, neuromorphic computing, and many other applications. Here, in this paper, we review the works within spintronics, more specifically on spin-orbit torque (SOT) within different research groups. We also provide researchers an insight into the future potentials of the SOT-based designs. This comprehensive review paper covers different aspects of SOT-based design from device and circuit to architecture level as well as more ambitious and futuristic applications of such technology., Comment: 17 pages, 13 figures