A5027422904- Advanced Memory and Neural Computing
- Magnetic properties of thin films
- Neural Networks and Reservoir Computing
- Quantum and electron transport phenomena
- Wireless Signal Modulation Classification
- Neural Networks and Applications
- Ferroelectric and Negative Capacitance Devices
- Theoretical and Computational Physics
- Semiconductor Quantum Structures and Devices
- Advanced Research in Systems and Signal Processing
- Electric Power Systems and Control
- Heat Transfer and Optimization
- Thermal Radiation and Cooling Technologies
- ZnO doping and properties
- Copper Interconnects and Reliability
- Magnetic Field Sensors Techniques
- Thermal properties of materials
- Electrochemical sensors and biosensors
- Advanced Thermodynamics and Statistical Mechanics
- Energy Harvesting in Wireless Networks
- Photosynthetic Processes and Mechanisms
- Enzyme-mediated dye degradation
- Magnetic Properties and Applications
- Wireless Power Transfer Systems
- Quantum-Dot Cellular Automata
International Iberian Nanotechnology Laboratory
2018-2024
Universidade do Porto
2017-2019
Abstract Artificial neural networks are a valuable tool for radio-frequency (RF) signal classification in many applications, but the digitization of analog signals and use general purpose hardware non-optimized training make process slow energetically costly. Recent theoretical work has proposed to nano-devices called magnetic tunnel junctions, which exhibit intrinsic RF dynamics, implement multiply accumulate (MAC) operation—a key building block networks—directly using signals. In this...
Magnetic tunnel junctions are nanoscale spintronic devices with microwave generation and detection capabilities. Here we use the rectification effect called "spin-diode" in a magnetic junction to wirelessly detect emission of another auto-oscillatory regime. We show that rectified spin-diode voltage measured at receiving end can be reconstructed from independently auto-oscillation spin diode spectra each junction. Finally adapt auto-oscillator model case spin-torque oscillator accurately...
Abstract Pinning at local defects is a significant road block for the successful implementation of technological paradigms which rely on dynamic properties non-trivial magnetic textures. Here, comprehensive study influence pinning sites non-homogeneous layers integrated as free layer tunnel junction presented, both experimentally and with corresponding micromagnetic simulations. The are found to be extremely detrimental frequency controllability devices, key requirement their use synapses in...
Abstract In this work, a new mechanism to combine non-volatile behaviour with the spin diode detection of vortex-based torque nano-oscillator (STVO) is presented. Experimentally, it observed that response oscillator depends on vortex chirality. Consequently, fixing frequency incoming signal and switching chirality results in different rectified voltage. way, can be deterministically controlled via application electrical signals injected locally device, resulting control output voltage for...
In this paper, the perpendicular magnetic anisotropy (PMA) is tailored by changing thickness of free layer with objective producing MTJ nano-pillars smooth linear resistance dependence both in-plane field and DC bias. We furthermore demonstrate how bias can be used to create a zero-threshold broadband voltage rectifier, feature which important for rectification in wireless charging energy harvesting applications. By carefully balancing amount PMA acting measured RF conversion efficiency made...
Abstract The synchronisation of magnetic tunnel junctions in the high frequency domain has attracted significant interest context novel computation paradigms, specifically neuromorphic spintronics and probabilistic computing. In this work, a design for coupling synchronization spin torque vortex oscillators (STVOs) is implemented. geometry comprises fabrication adjacent pairs STVO nanopillars (MgO-based junctions), with an edge-to-edge distance down to 100 nm, together individual top...
Pinning at local defects is a significant road block for the successful implementation of technological paradigms which rely on dynamic properties non-trivial magnetic textures. In this report comprehensive study influence pinning sites non-homogeneous layers integrated as free layer tunnel junction presented, both experimentally and with corresponding micromagnetic simulations. The are found to be extremely detrimental frequency controllability devices, key requirement their use synapses in...
Spin torque nano-oscillators (STNOs) have been shown to efficiently “lock” electrical signals whether these are from external sources, self-reflected signals, or other STNOs via mutual synchronization. Traditionally, the locked state of an STNO has considered digital, either “ON” “OFF.” In this report, we show how phase in can vary significantly as a function applied dc with strongly affecting emitted power. This dependence demonstrates analogue nature state, offering extra degree freedom...
Wireless sensor networks are a key aspect of emerging smart technologies, and while the diverse range potential applications leads to system requirements, one is power consumption node. In this report, we show how sensitivity radio frequency emission magnetic tunnel junction (MTJ) allows them replace traditional sensor-processor-transmitter CMOS paradigm with single MTJ nano-device. Furthermore demonstrate nominally identical can also be used wirelessly detect emitted signal, which extremely...
Spintronic nano-synapses and nano-neurons perform complex cognitive computations with high accuracy thanks to their rich, reproducible controllable magnetization dynamics. These dynamical nanodevices could transform artificial intelligence hardware, provided that they implement state-of-the art deep neural networks. However, there is today no scalable way connect them in multilayers. Here we show the flagship nano-components of spintronics, magnetic tunnel junctions, can be connected into...
For numerous Radio-Frequency applications such as medicine, RF fingerprinting or radar classification, it is important to be able apply Artificial Neural Network on signals. In this work we show that possible directly Multiply-And-Accumulate operations signals without digitalization, thanks Magnetic Tunnel Junctions (MTJs). These devices are similar the magnetic memories already industrialized and compatible with CMOS. We experimentally a chain of these MTJs can rectify simultaneously...
The spin-diode effect is studied both experimentally and with our original semi-analytical method. latter based on an improved version of the Thiele equation approach (TEA) that we combine to micromagnetic simulation data accurately model non-linear dynamics spin-torque vortex oscillator (STVO). This method, called data-driven (DD-TEA), absorbs difference between analytical simulations provide a ultra-fast quantitative model. DD-TEA predictions also agree very well experimental data....
Spintronic nano-synapses and nano-neurons are complex cognitive devices that have high accuracy due to their reproducible controllable magnetization dynamics. They the potential revolutionize artificial intelligence hardware, but currently, there is no scalable way connect them in multilayers. We show magnetic tunnel junctions can be used spintronics components multilayer neural networks, allowing function as both synapses neurons. build a two-layer hardware spintronic network using nine...
Magnetic tunnel junctions (MTJs) with a switching process based on the spin transfer torque (STT) effect are best candidates for new generation of non-volatile magnetic random-access memories (MRAMs) [1].
With the ever increasing power dissipation in electrical devices, new thermal management solutions are high demand to maintain an optimal operating temperature and efficient performance. In particular, recently developed magnetically-activated switches (MATSs) provide alternative existing using magnetic properties of superparamagnetic nanofluids dissipate heat a controlled manner. However, presence moving parts is major drawback those systems that must still be addressed. Herein, we present...
Artificial neural networks are a valuable tool for radio-frequency (RF) signal classification in many applications, but digitization of analog signals and the use general purpose hardware non-optimized training make process slow energetically costly. Recent theoretical work has proposed to nano-devices called magnetic tunnel junctions, which exhibit intrinsic RF dynamics, implement Multiply Accumulate (MAC) operation, key building block networks, directly using analogue signals. In this...