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Register a new userDesign of a self-adaptive neuron with advanced cognitive abilities utilizing a reconfigurable skyrmion lattice
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Artificial Intelligence (AI) promises to fundamentally transform society but faces multiple challenges in doing so. In particular, state-of-the-art neuromorphic devices used to implement AI typically lack processes like neuromodulation and neural oscillations that are critical for enabling many advanced cognitive abilities shown by the brain. Here, we utilize smart materials, that adapt their structure and properties in response to external stimuli, to emulate the modulatory behaviour of neurons called neuromodulation. Leveraging these materials, we have designed and simulated the dynamics of a self-adaptive artificial neuron, which comprises five magnetic skyrmions hosted in a bilayer of thulium iron garnet (TmIG) and platinum (Pt). Micromagnetic simulations show that both the amplitudes and frequencies of neuronal dynamics can be modified by reconfiguring the skyrmion lattice, thereby actualizing neuromodulation. Further, we demonstrate that this neuron achieves a significant advancement over state-of-the-art by realizing the advanced cognitive abilities of context-awareness, cross-frequency coupling as well as information fusion, while utilizing ultra-low power and being ultra-compact. Building advanced cognition into AI can fundamentally transform a wide array of fields including personalized medicine, neuro-prosthesis, human-machine interaction and help realize the next-generation of context-aware AI.
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An exponential increase in the performance of silicon microelectronics and the demand to manufacture in great volumes has created an ecosystem that requires increasingly complex tools to fabricate and characterize the next generation of chips. However, the cost to develop and produce the next generation of these tools has also risen exponentially, to the point where the risk associated with progressing to smaller feature sizes has created pain points throughout the ecosystem. The present challenge includes shrinking the smallest features from nanometers to atoms (10 nm corresponds to 30 silicon atoms). Relaxing the requirement for achieving scalable manufacturing creates the opportunity to evaluate ideas not one or two generations into the future, but at the absolute physical limit of atoms themselves. This article describes recent advances in atomic precision advanced manufacturing (APAM) that open the possibility of exploring opportunities in digital electronics. Doing so will require advancing the complexity of APAM devices and integrating APAM with CMOS.
Many promising applications of single crystal diamond and its color centers as sensor platform and in photonics require free-standing membranes with a thickness ranging from several micrometers to the few 100 nm range. In this work, we present an approach to conveniently fabricate such thin membranes with up to about one millimeter in size. We use commercially available diamond plates (thickness 50 $mu$m) in an inductively coupled reactive ion etching process which is based on argon, oxygen and SF$_6$. We thus avoid using toxic, corrosive feed gases and add an alternative to previously presented recipes involving chlorine-based etching steps. Our membranes are smooth (RMS roughness <1 nm) and show moderate thickness variation (central part: <1 $mu$m over $approx ,$200x200 $mu$m$^2$). Due to an improved etch mask geometry, our membranes stay reliably attached to the diamond plate in our chlorine-based as well as SF$_6$-based processes. Our results thus open the route towards higher reliability in diamond device fabrication and up-scaling.
Magnetic skyrmions are topological solitons with a nanoscale winding spin texture that hold promise for spintronics applications. Until now, skyrmions have been observed in a variety of magnets that exhibit nearly parallel alignment for the neighbouring spins, but theoretically, skyrmions with anti-parallel neighbouring spins are also possible. The latter, antiferromagnetic skyrmions, may allow more flexible control compared to the conventional ferromagnetic skyrmions. Here, by combining neutron scattering and Monte Carlo simulations, we show that a fractional antiferromagnetic skyrmion lattice with an incipient meron character is stabilized in MnSc$_2$S$_4$ through anisotropic couplings. Our work demonstrates that the theoretically proposed antiferromagnetic skyrmions can be stabilized in real materials and represents an important step towards implementing the antiferromagnetic-skyrmion based spintronic devices.
We theoretically investigate the plasmonic properties of mid-infrared graphene-based metamaterials and apply deep learning of a neural network for the inverse design. These artificial structures have square periodic arrays of graphene plasmonic resonators deposited on dielectric thin films. Optical spectra vary significantly with changes in structural parameters. Our numerical results are in accordance with previous experiments. Then, the theoretical approach is employed to generate data for training and testing deep neural networks. By merging the pre-trained neural network with the inverse network, we implement calculations for inverse design of the graphene-based metameterials. We also discuss the limitation of the data-driven approach.
We report the isolation of thin flakes of cylindrite, a naturally occurring van der Waals superlattice, by means of mechanical and liquid phase exfoliation. We find that this material is a heavily doped p-type semiconductor with a narrow gap (<0.85 eV) with intrinsic magnetic interactions that are preserved even in the exfoliated nanosheets. Due to its environmental stability and high electrical conductivity, cylindrite can be an interesting alternative to the existing two-dimensional magnetic materials.
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