Keywords: Nanomaterials
Abstract: The rapidly evolving field of two-dimensional (2D) and layered materials is driving progress across various disciplines. Recent advancements in 2D material-based inks have broadened the scope of additive electronics, benefiting applications such as sensors, energy harvesting, and energy storage. The integration of 2D materials is also making strides in healthcare and water treatment, particularly with flowing electrode capacitive deionization and tissue engineering. Notable developments include Ti3C2Tx MXene inks for aerosol jet printing, which produce high-performance supercapacitors with excellent capacitance. Incorporating MXenes into polymer matrices enhances energy output in extrusion-printed triboelectric nanogenerators (TENGs), while MXene-based electrodes improve ion removal and energy efficiency in capacitive deionization systems. Moreover, applying direct electrical stimulation to 3D graphene foam bioscaffolds has shown improvements in mechanical properties and cellular integration for cartilage regeneration.

Keywords: Modeling & Simulation, Nanomaterials, Nanoelectronics: Emerging material and device challenges in futuristic systems
Abstract: The control of light–matter interaction at the nanoscale is a grand challenge that cuts across modern electronics, photonics, plasmonics, and optoelectronics. The development of predictive simulation tools is key to successfully tackling this challenge. Such tools must self-consistently couple electrodynamics with quantum electronic transport in order to capture light–matter interaction at the nanoscale with full account of the phenomena arising from emerging materials, low dimensionality, small size, edge topography, quantum geometries, substrate choice, and impurity placement. A critical element in the successful coupling between electrodynamics and transport is the calculation of electromagnetic potentials that enter the electronic Hamiltonian in every quantum transport solver, but are not routinely computed via commonly employed electrodynamics solvers, either commercial or open source. The results of recent efforts at implementing second-order equations for the magnetic vector and electric scalar potentials in the Lorenz gauge are not suitable for coupling with transport solvers for reasons of (gauge-related) spurious numerical blocking of the current flow in the near field, as well as issues with necessary memory storage and a dearth of good-enough absorbing boundary conditions. Here, we report on the development of DuPo FDTD, a computational electromagnetics technique suitable for coupling with quantum transport solvers within multiphysics simulation frameworks. It is based on the dual-potentials (DuPo) formulation of classical electrodynamics and the finite-difference time-domain (FDTD) numerical technique. DuPo FDTD stems from first-order equations governing the time evolution of the magnetic vector potential (A) and the electric vector potential (C), both in the Coulomb gauge, and the electric scalar potential φ. The equations for A and C are coupled and resemble Maxwell’s curl equations (Faraday’s and Ampere’s laws), only sourced by a precursor to the solenoidal part of the current density. The conservative part of the current density is connected to the charge density and the electric scalar potential only. This separation into the solenoidal and conservative tracks enables us to use a state-of-the-art CFS-CPML absorbing boundary condition in essentially unchanged form for the curl equations. We demonstrate the coupling between DuPo FDTD and a simple ballistic NEGF code on several examples, one of them being a metallic bowtie nanoantenna where we show the expected time evolution of the tunnelling current across the 1-nm-wide antenna gap when the solver is sourced with a voltage pulse between the two metallic patches. DuPo FDTD, coupled with a state-of-the-art quantum transport solver, may allow those interested in quantum transport to study time-dependent phenomena and generally look at device response to broadband electromagnetic excitation.

Keywords: Modeling & Simulation, Nanomaterials, Nanofabrication
Abstract: The value of Young’s modulus of ultra-thin metal films is reported for copper, ruthenium, and cobalt using molecular dynamics simulations, and a methodology is proposed which can be used to examine any other metal or dielectric films of interest. Synopsys’ QuantumATK software is used to perform this study and metal films with thicknesses varying from 17A ̊- 1000 A ̊ are studied to show convergence to the bulk Young’s modulus values for these metals.

Keywords: Modeling & Simulation
Abstract: The terahertz (THz) band occupies a frequency range between high-millimeter waves and infrared optical frequencies. In this band, amplification can be achieved using conventional methods at either signal or optical level. However, these traditional technologies exhibit significant limitations in providing adequate gain at THz frequencies, due to high costs, significant losses, limited bandwidth, and poor scalability. To overcome these limitations, alternative approaches involving amplification mediated by surface plasmon polaritons (SPP) might play a key role. In this study, we investigate the quantum Čerenkov emission to describe the coupling of charge carriers with THz plasmons in graphene monolayer. This theoretical model of electromagnetic amplification is particularly interesting for coherent transport, where the velocity of the carriers is approximately the Fermi velocity (vF ≈ 106). By considering a device where graphene is integrated into a metal-confined structure (see Fig Figure 1a), two main beneficial effects can be achieved: (i) slowing the plasmonic wave and (ii) confine the electromagnetic energy in the out-of-plane direction. The first is obtained due to the linearization of the plasmon dispersion q(ω) in such a structure, under the assumption of small signals, losses and thicknesses compared to plasmon wavelength. Slow waves extend the frequency range for Čerenkov radiation to the low-THz band, whereas previous studies were primarily focused on plasmon emission at infrared wavelengths [1]. From the Fermi’s golden rule, as demonstrated in our previous work [2], it is possible to obtain Eq. 1, which define the total photon emission for a frequency between ω and ω + dω at a certain angle between θ and θ + dθ.

Keywords: Modeling & Simulation, DNA Nanotechnology, Nanoelectronics: Emerging material and device challenges in futuristic systems
Abstract: Utilizing DNA as an electronic device opens the door for a variety of applications in nanoelectronics, disease detection, sensing biological events, and sequencing techniques. The common experimental setup attaches electrodes to the two ends of a double-strand DNA and applies voltage to measure the conductance. However, literature has shown variations in the measured conductance values and current voltage characteristics of the DNA. Modeling of charge transport is required to understand, predict, and design the electrical properties of DNA. The non rigid fluctuating nature of this soft matter, where the atoms are constantly moving, increases the possibility of scattering events. The atomic details of the contact-molecule interface affects the contact coupling strength to the DNA. Further, the energy levels distribution along the strand depends on the sequence, which in turn alters the local density of states (DOS). Finally, the content of the solvent environment affects the conformation of the DNA structure. Several modeling approaches were developed to try incorporating the effects of the abovementioned factors into the charge transport model. One major approach utilizes the Green’s function to model charge transport through nanoscale structures. This approach uses the Hamiltonian obtained from quantum mechanical calculation methods such as DFT to calculate the electron transmission probability. In addition, it allows us to add the effect of the contacts through self-energy terms within the formalism, removing the need to explicitly add the contact atoms into the quantum mechanical simulations. Further, it can adapt decoherence probes through self-energy to account for scattering events that cause decoherence into the charge transport. From a modeling perspective, decoherence broadens the DOS in the DNA, causing the off-resonant transmission to increase, which in turn allows us to obtain conductance values within the range of experiment. This feat was not obtainable if only using coherent transport model. The decoherence probes are phenomenological probes that use spatially dependent scattering rates to account for all events that can cause the electron to lose phase information (elastic scattering) or gain and lose energy (inelastic scattering). In our prior research, the decoherence parameter employed had the same energy dependence at every resonant energy level of the DNA base-wise partitioned system. This energy dependence further depended on two parameters: a maximum decoherence rate (Γ𝐵) and the decay of the decoherence rate (𝜆) away from the resonant energy [1]. To decrease the dependence on the number of phenomenological parameters, we heuristically propose a decoherence probe model here, where the decoherence self-energy depends on the local density of states, much like what one would assume in Fermi’s rule. We use a self-consistent approach to obtain a spatially-and-energydependent decoherence rate based on the DOS of the DNA. The transmission and conductance resultsobtained from this new model are compared to previous models to develop a deeper understanding of charge transport in DNA.

Keywords: Modeling & Simulation, Nanoelectronics: Emerging material and device challenges in futuristic systems, Nanomaterials
Abstract: This work presents a small-signal model of a top-gate graphene field-effect transistor working in a 77 K environment. The proposed model is able to describe the experimental high-frequency operation of the device up to tens of GHz. The experimentally-calibrated small-signal GFET model has been used to design single- and double-stage amplifiers at 2.15 GHz, 2.2 GHz and 2.4 GHz with high-gain and high-selectivity. A discussion is provided regarding the feasibility of using such circuits as a second stage in microwave applications based on Josephson Junctions working at the same cryogenic temperature as the designs proposed here.

Keywords: MEMS, NEMS, Modeling & Simulation
Abstract: The gas-assisted atomization process exhibits unstable phenomena, such as high velocity and fragmentation, leading to an unclear understanding of the atomization mechanism related to liquid fragmentation and gas-liquid interaction. Until now, there are few researches focused on the structural design of microchip nozzles and the gas-assisted atomization process for low Reynolds number liquid flows. In this study, a three-dimensional gas-liquid-gas annular nozzle structure was designed, and the influence of control parameters and physical parameters on atomization performance was explored. The structure parameters of the nozzle were optimized to achieve uniform droplet size distribution and stable flow. To reveal the atomization process and characteristics of low Reynolds number gas-assisted liquid flow, coupled with discrete phases (DPM) and integrated with adaptive mesh refinement (AMR) technique, we studied the effects of gas pressure, liquid volume flow, and liquid physical property parameters on atomization effect. It was found that at air pressures of 0.8 bar and 1.37 bar, there was a large number of atomized droplets with small and evenly distributed SMD. A smaller liquid mass flow rate resulted in better atomization effect due to greater relative speed of initial contact between gas and liquid. However, excessively small liquid speed under high pressure led to reduced atomization performance due to liquid reflux. This study provide valuable insights for optimizing and developing gas-assisted injection microspray chip.

Keywords: MEMS/NEMS, Nanosensors & Nanoactuatuators
Abstract: Palladium thin-film resistive hydrogen sensors have been widely studied for application such as hydrogen leak detection and emergency fault warning in oil-immersed electrical equipment. Response rate is a critical parameter for these sensors. Nickel doping is considered an effective method to improve the response speed of sensors. This study combines experimental research and theoretical calculations to further clarify these two issues. In this study, palladium-nickel alloy nanofilms with varying doping concentrations (0 wt%, 1 wt%, 2 wt% and 4 wt%) but identical thicknesses were prepared using a multi-target magnetron co-sputtering technique. The sensors were fabricated using photolithography and lift-off techniques. A performance testing platform for gas sensors was set up to study the effects of nickel doping concentration on the response time and sensitivity of the sensors in low hydrogen concentration scenarios. The results show that as the nickel doping concentration increases, the sensitivity of the palladium thin-film resistive hydrogen sensors decreases approximately linearly, while the response time initially decreases rapidly and then stabilizes. It means that low concentration nickel doping can significantly enhance the response rate of the sensors while retaining adequate sensitivity. Furthermore, density functional theory (DFT) was employed to investigate the mechanisms behind the performance changes caused by nickel doping. The study constructed crystal models of palladium and palladium-nickel alloys and calculated the system energy before and after hydrogen atom embedding using the GGA-PBE functional. The Nudged Elastic Band (NEB) method was employed to perform transition state calculations for the hydrogen atom diffusion process. The study found that nickel doping increases the Gibbs free energy change for hydrogen molecules dissociating into hydrogen atoms and embedding into the metal lattice, leading to reduced sensitivity and shorter response times. Simultaneously, nickel doping decreases the diffusion activation energy of hydrogen atoms within the metal lattice, creating fast diffusion pathways for hydrogen atoms, thus shortening the response time of the sensors.

Keywords: MEMS/NEMS, Modeling & Simulation, Nano-biomedicine
Abstract: This paper reports on the numerical modeling and experimental evaluation of the behaviors of biomolecules immobilized on the surface of blood vessels on microchip under pulsatile flow conditions, which include velocity profiles of the fluids and shear stress profiles around the biomolecules. Using fluorescent BSA, FITC conjugates mimicking biomolecules, the behaviors of the biomolecules can be optically observed, facilitating the quantitative studies and analysis.

Keywords: Quantum, Neuromorphic & Unconventional Computing
Abstract: Unconventional methods for the solution of NP problems is, nowadays, a strong direction of research. The main objectives are obtaining accurate solutions and reducing the computational time. In this work, we show the performances of an Ising Machine based on the synchronization of oscillators for the solution of the Max-Cut Problem (MCP) in very large graphs. Our machine is a model inspired by physics, based on the interaction of oscillators, and we solve it numerically including a heuristic annealing scheme. Interacting oscillators are analytically defined through two different models, the Kuramoto [1] and the Slavin model [2], where the latter is one of the most famous models used, in particular, for spin-torque oscillators. In both cases, the algorithms have been tested with MCPs scaling the number of nodes and with fixed size and variable density. The main results are solutions higher, and therefore more accurate, than the state-of-the-art conventional algorithms. In addition, we can solve cubic problems with millions of nodes, which represents the largest graphs solved so far in literature [3]-[4].

Keywords: Quantum, Neuromorphic & Unconventional Computing, Nanofabrication, Nanoelectronics: Emerging material and device challenges in futuristic systems
Abstract: Li-based memristors have been found to have both volatile and non-volatile resistive switching behavior, which can be reconfigured for computation tasks like neuromorphic computing, showing its potential in novel energy efficient computing architecture. However, the incompatibility of Li-based materials with current CMOS production facility makes it almost impossible to have foundry fabricate Li-based memristors. We hereby propose a BEOL method to integrate Li-based memristors with foundry CMOS chips using an academic fabrication facility. The proposed BEOL integration method can also be generalized to other CMOS process-incompatible devices.

Keywords: Quantum, Neuromorphic & Unconventional Computing, Modeling & Simulation, Nanoelectronics: Emerging material and device challenges in futuristic systems
Abstract: This work explores the impact of temperature on the performance of Fully Depleted Silicon on Insulator (FDSOI) MOSFETs used as single transistor neurons in neuromorphic computing systems. Key aspects analyzed include spiking frequency response, energy per spike, and latch voltage outputs. The findings reveal that FDSOI MOSFETs maintain stable performance across a temperature range of 300K to 380K (27°C to 107°C), especially under a large negative gate bias of -1V. This stability is crucial for practical applications, ensuring reliable performance despite thermal variations. The single transistor latch (STL) function in these MOSFETs is enabled by thermally insensitive band-to-band tunneling (BTBT), unlike the temperature-sensitive Impact Ionization (II). This makes neuronal operations stable regardless of temperature changes. Such stability and efficiency highlight the potential of FDSOI MOSFETs in neuromorphic computing, which can be applied spiking neural networks (SNN).

Keywords: Quantum, Neuromorphic & Unconventional Computing
Abstract: In neuromorphic computing, the most common model for artificial neurons is leaky, integrate, and fire (LIF), capturing membrane potential integration over time, firing above a threshold, and leaking back to the initial state when not stimulated. While IF behavior has been initially demonstrated using artificial neurons based on domain walls, reliable IF operation is lacking, with field resets needed between cycles or careful containment of the domain wall back-and-forth in the racetrack. Additionally, to-date leaking behavior in domain wallbased neurons has been limited to simulation. Here, we present our results demonstrating reliable IF behavior over 100 cycles in domain wall-magnetic tunnel junction nanodevices fabricated from perpendicular magnetic anisotropy thin film stacks, with domain wall(s) driven by current. We also show tunable leaking behavior to fully capture the LIF artificial neuron model. These results are important advancements for using spintronic devices in spiking neural networks.

Keywords: Quantum, Neuromorphic & Unconventional Computing, Modeling & Simulation, Spintronics
Abstract: Semiconductor technologies are currently undergoing multiple paradigm shifts: following extreme miniaturization of components, atomistic and quantum effects become increasingly prominent. While these features are often considered as detrimental to conventional transistors, they become operating principles for emerging quantum technologies. Consequently, the last decades have witnessed spectacular progress in the development of qubits based on semiconductor technologies. Compared to other qubit implementations, semiconductor qubits have the advantage of displaying extended coherence times, elevated operation temperature of a few K compared with competing solid-state technologies, and nanometre-scale footprint enabling in principle to fit millions of qubits on a single chip, a decisive advantage for fault-tolerant quantum computing through quantum error correction [1]. Finally, semiconductor qubits can be produced using CMOScompatible techniques whose high yield and precision may then be leveraged for improved scalability [2]. In semiconductor qubits, single charge carriers (electrons or holes) are electrically isolated from their environment and quantum-mechanically confined at precise locations using either gated or dopant quantum dots (QDs) [3]. Typically, an individual qubit is then implemented in the spin states of one or a few electrons or holes. Coherent manipulation of spin qubits is then achieved through electric and magnetic fields, by leveraging various quantum-mechanical properties of quantum dots in semiconductor materials like the spin-orbit interaction, valley splitting, or the exchange interaction. In gated QDs, electrons or holes are confined by imposing external electric potentials through nanometre-scale electrostatic gates. In dopant QDs, a single electron is bound by the confinement potential of the dopant in its semiconductor host lattice. While gated QDs typically offer the most flexible control, dopant QDs deliver extended coherence times and improved device uniformity [4]. Design, fabrication, and characterization of classical semiconductor devices benefits from a mature set of technology computer-aided design (TCAD) tools that are routinely used by nanoelectronics engineers to predict device performance before fabrication, thus significantly mitigating development costs by reducing trial and error. As quantum technologies mature and gain industrial relevance, specialized quantum TCAD tools become increasingly necessary, as existing computational tools for classical chips are inadequate due to fundamental differences in their operating principles. In this talk, we will present the latest advances in the development of design software for spin qubits in semiconductors. We will show how algorithmic advances and systematic approximations have led us to the development of efficient simulation workflows for spin qubits based on gated QDs [5]. These workflows encompass the definition of 3D TCAD models from gate layouts [Fig. 1(a-b)], electronic structure calculations within effective-mass or k•p models [Fig. 1(c-d)], charge stability diagrams [Fig. 1(e)], and Rabi oscillations in single-qubit and two-qubit gates [Fig. 1(f)]. In addition, we will present how advances in multiscale simulations including both atomistic and effective-mass techniques may be leveraged for first-principles calculations of important characteristics of dopant QDs [Fig. 1(g)], such as the ground-state Kohn-Sham orbital [Fig. 1(h)] and addition energies [Fig. (i-j)]. This work opens the door to streamlined TCAD simulation workflows for stateof-the-art semiconductor qubits employed in academic research groups and in the emerging quantum hardware industry.

Keywords: Nano-optics, Nano-photonics & Nano-optoelectronics, Modeling & Simulation, Nanofabrication
Abstract: Silicon avalanche photodiodes (APDs) play a key role in low-photon count electromagnetic wave detection applications, including single-photon (SPAD), night vision, and light detection and ranging. There is enormous potential to perfect existing APD designs by addressing limitations such as high-voltage breakdown, response time, and poor noise performance. We present a sensitivity-analysis-based method to capture the impact of temperature, bias voltage, and illumination power fluctuations on the noise performance of APDs. The study uses a well-calibrated ATLAS Silvaco framework utilizing our in-house experimental data. Using this well-calibrated framework, we study the impact of temperature, bias voltage, and illumination power variation on the response time of the APD. We show that a change of 10°C in temperature results in a ~28% change in the fall time, a parameter that defines the operational 3dB bandwidth of the APD. The variation in bias voltage and illumination power is shown to impact the intensity of the response with ~3% variation in the fall time. A study of this nature would enable the robust design of APD devices, ensuring reliable and efficient photon detection under low-light conditions.

Keywords: Nano-optics, Nano-photonics & Nano-optoelectronics, Nanoelectronics: Emerging material and device challenges in futuristic systems, Modeling & Simulation
Abstract: This paper proposes optimized reconfigurable ultra-compact mode converter devices capable of converting between transverse electric (TE) modes TE0, TE1, and TE2. The mode conversion is facilitated through the state manipulation of an antimony selenide (Sb2Se3) layer, which can be either added on top of a silicon core or embedded within a silicon waveguide. The performance of these devices was numerically investigated at a wavelength of 1550 nm, comparing the Sb2Se3 on-top configuration with various embedding strategies, including partially, halfway, and fully etched silicon waveguides. Results indicate that embedding Sb2Se3 within partially or halfway etched waveguides outperforms both the on-top and fully etched configurations. The Sb2Se3 layer’s complex refractive index distribution was optimized using topology optimization to enable phase transformation, allowing switching between amorphous and crystalline states. The study further examines the ideal thickness of the Sb2Se3 layer and its impact on key performance metrics such as coupling efficiency, extinction ratios, insertion loss, and overall figure of merit for both amorphous and crystalline states. These findings provide a pathway for enhancing the functionality and efficiency of integrated photonic devices through advanced material engineering and design optimization.

Keywords: Nano-optics, Nano-photonics & Nano-optoelectronics, Nanofabrication, Modeling & Simulation
Abstract: Today, serial transmission links can reach speeds of up to 100 GBaud per channel, surpassing the capabilities of purely electrical methods. This highlights the increasing importance of opto-electronic systems. A key element in these systems is the optical modulator, which encodes electrical data onto an optical carrier for transmission through optical fibers. Silicon photonic (SiP) devices, vital in information and communication technologies, currently only partially meet the high bandwidth requirements for integrated SiP chips. Essential components such as photodetectors and modulators need to be improved to achieve speed (> 25 Gbs-1), size (< 1 mm2), insertion loss (< 4dB), and power consumption (< 1 pJbit-1) targets. No existing system meets all these criteria, highlighting the urgent need for technology that addresses bandwidth and power consumption requirements. One promising approach is combining silicon with materials that have superior optoelectronic properties. Graphene has emerged as a leading electro-optical material due to its unique optical and electrical characteristics. Notably, graphene has consistent absorption across visible and infrared wavelengths and high nonlinearity in its refractive index, making it suitable for optical switches and modulators. Additionally, its conductivity can be adjusted by applying external voltage, allowing control over its electronic properties. Given the importance of electro-optic modulation in optical data communication, graphene is a strong candidate for enhancing photonic integrated circuits. Although several graphene-based modulators have been developed as stand-alone devices in recent years, they remain at the laboratory scale and are not yet suitable for integrated device manufacturing. Therefore, integrating graphene modulators into standard silicon technology platforms (e.g., CMOS) for practical manufacturing is critical. This work addresses the challenges of graphene growth, transfer, contacting, and passivation in a pilot line environment on a 200 mm wafer scale, focusing on the design, fabrication, and measurement of graphene-based modulators. Additionally, simulations and fabrication of graphene ring modulators have been carried out at both the component and device levels, incorporating realistic graphene properties. These results show a modulation depth of 1.6 dB/μm and a 3dB bandwidth of 7 GHz, demonstrating the potential of graphene-based photonic devices for high-speed communication applications.

Keywords: Nano-optics, Nano-photonics & Nano-optoelectronics, Nanometrology & Characterization, Nanomaterials
Abstract: We report the calculation of fine-structure splitting (FSS) in four nitride quantum dot-in-wire devices for use in entangled photon pair generators (EPPG). For reliable operations, such non-classical photon generators must feature a vanishingly small FSS. In our recent work on InGaN/GaN systems, it was found that the non-polar m-plane structures exhibit a small FSS (in the range of 10 μeV) compared to the polar counterparts. In this work, we investigate design improvements via tuning the confinement potential. For the numerical simulation, a multiscale many-body framework is employed, where the excitonic energy is calculated by coupling a full configuration interaction (FCI) method with an atomistic multi-band tight-binding (TB) model.

Keywords: Nano-optics, Nano-photonics & Nano-optoelectronics, Nanosensors & Nanoactuatuators, Modeling & Simulation
Abstract: Extinction in thin polymer films containing nanoparticles is important to photovoltaics, sensors, and interconnects. Extinction measured in 1-millimeter-thin films containing plasmonic nanoparticles increased with nanoparticle density to levels higher than predicted. Yet, enhancement of extinction was not measured in <100-nanometer-thin films containing high-density plasmonic nanoparticles. The present study examined extinction in 80 micrometer films that contained plasmonic nanoparticles at increasing volume fractions. Optical images and spectra were integrated in a new hyperspectral method to quantitate visual attributes to use nanoparticle-containing films in e.g., colorimetric assays. Measured extinction was compared with values predicted by the exact Mie solution to Maxwell’s equation and by the Maxwell-Garnett effective medium theory. Extinction measured in 80-micron films was found to increase with nanoparticle volume fraction from a low value predicted by Mie theory to a high value predicted by Maxwell-Garnett effective medium theory. Results of the present study are useful to specify the volume fraction of nanoparticles in thin polymer films to obtain desired spectral characteristics for photovoltaics, sensors, interconnects, and colorimetric point-of-care assays.

Keywords: Quantum, Neuromorphic & Unconventional Computing
Abstract: We briefly review the current state of development of spin-transfer torque (STT) MRAM. This is then followed by discussion of electric-field-controlled MTJs that use the voltagecontrolled magnetic anisotropy (VCMA) effect to realize denser and more energy-efficient MRAM arrays, as well as faster and more scalable probabilistic bits. We also discuss progress in developing all-antiferromagnetic tunnel junctions, which rely on the unconventional spin-dependent transport properties of noncollinear antiferromagnets. Finaly, we discuss MTJ applications in energy-efficient artificial intelligence, combinatorial optimization, and hardware security.

Keywords: Quantum, Neuromorphic & Unconventional Computing, Spintronics
Abstract: Spin waves are promising tools for data processing and storage as they propagate with low energy losses and promise high operational speed. This work focuses on spin dynamics in antiferromagnets, which differ significantly from those in ferromagnetic systems. Insights gained from studying longitudinal excitations can lead to the development of new magnetic storage technologies and spintronic devices, which rely on the manipulation of spin rather than charge for data processing and storage.

Keywords: Quantum, Neuromorphic & Unconventional Computing, Modeling & Simulation, Nanoelectronics: Emerging material and device challenges in futuristic systems
Abstract: Operational characteristics of highly scaled devices are greatly affected by localized atomiclevel structural rearrangements. Energy generation/dissipation determining such atomic changes depends on the time duration of a given operation process, hence, circuitry operation frequency rather than conventionally used overall long-term operational time. Thus, novel technologies need to be assessed by combining electrical measurements, material structure and charge transport simulations results while explicitly considering their functional interconnectivity in real time. The employed description of these processes should directly link device electrical and material properties, thus enabling identifying structural features controlling device performance. Here we consider an application of this framework to neuromorphic computing - a new computational paradigm centered around devices that can integrate multiple functionalities, such as computing and storing information in the same functional unit. We focus on memory formation processes in a promising family of such devices – resistive non-volatile memristors – employed in various neuromorphic computing implementations. The effectiveness of neuromorphic systems, which are required to achieve several orders of magnitude in energy savings, is defined by the properties of employed memristors while application conditions may introduce additional constraints (energy consumption, speed, environmental stability, etc.) to imitate adaptive synaptic changes. The question of practical importance is whether considered devices can meet use conditions and circuitry operation requirements. Projection of this analysis onto brain operations provides insight into possible internal drivers affecting people’s preferences in social and personal choices.

Keywords: Nanofabrication, Nano-optics, Nano-photonics & Nano-optoelectronics, Fundamentals and applications of nanotubes, nanowires, quantum dots and other low dimensional materials
Abstract: The NanoFrazor technology is utilizing fabrication capabilities of thermal scanning probe lithography (t-SPL) and direct laser sublimation (DLS) to perform nanopatterning, either physical or chemical in nature, on a diversity of materials and substrates. With such diversity, there are still several challenges to overcome such as performing high-resolution DLS patterning on heterogeneous surfaces as well as low-fill factor nanopatterning that are often used in metasurface designs. This presentation will discuss work that is being performed to address such topics as well as an introduction to the fundamentals and workings of t-SPL and DLS for the NanoFrazor technology.

Keywords: Nanofabrication, Nanomaterials, Nanopackaging
Abstract: A flexible, transparent metamaterial absorber (MMA) based on Indium Tin Oxide (ITO) is proposed, covering the X, Ku, K, and Ka bands. The design begins with the fabrication of a flexible, transparent PDMS layer, whose microwave absorption properties are analyzed. The PDMS layer exhibits a low loss tangent (0.02–0.04), indicating minimal absorption. To enhance absorption, a patterned ITO-PET metasurface is combined with the PDMS layer to realize MMA using CST microwave studio tool. The conductive patterned ITO layer creates resonances and generates ohmic losses, significantly improving overall microwave absorption. This MMA achieves an ultrawideband 10-dB absorption bandwidth (ABW) of 27.23 GHz (7.47–34.7 GHz) with maximum absorption reaching up to 99.99% at 22.42 GHz, all within a thickness of just 1.85 mm. This proposed MMA shows promise for applications in microwave absorption for stealth technology, 5G, and space.

Keywords: Nanofabrication, Nanomaterials, Modeling & Simulation
Abstract: An efficient Si-SiC electronic and photonic co-design laser fabrication process is demonstrated. The product material surface condition and device electrical properties are well characterized. This work also presents comparable results between the simulated and measured data.

Keywords: Nanofabrication, Nanorobotics & Nanomanufacturing, Nanoelectronics: Emerging material and device challenges in futuristic systems
Abstract: The recent prospective field of application for terahertz (THz) technology includes imaging, sensor systems, and high-speed wireless communication. The design and fabrication, as stated, of terahertz antennas, put the design engineer in various challenges, such as material selection, signal attenuation, and fabrication with highest accuracy of the smallest features. This paper presents the design, simulation, and fabrication of a compact monopole terahertz antenna with physical dimensions of 0.25mm x 0.25mm x 0.44mm. The antenna is developed through Ansys High-Frequency Structure Simulator, and the fabrication process utilizes Two-Photon Polymerization (2PP). The designed antenna was properly simulated in order to achieve good features of the reflection coefficient (S11) and the radiation patterns. This antenna was fabricated with high microscale accuracy by applying advanced 2PP technology to imitate complex THz structures at the required resolution. The simulation results showed a good impedance match along a wide bandwidth operating in the THz range with low levels of S11 and good power transmission. The high-resolution features and integrity of the fabricated antenna were verified using electron microscope. This project demonstrates the ability to build complex THz-antennas designs with highaccuracy and full functionality for diverse applications.

Keywords: Nano-fluidics and integrated bio-chips, MEMS/NEMS, Nano-biomedicine
Abstract: This paper reports on a new assay to enhance the immobilization of biomolecules on the surface of a blood vessel microchip, which will render the significantly improved binding of the biomolecules to the surface of the microfluidic channels, thereby allowing the studies of the behaviors of the biomolecules such as platelets under a broad range of flowing shear stresses from normal physiological to extreme pathological conditions.

Keywords: Nanomaterials, Nanofabrication, Nanoenergy, Environment & Safety
Abstract: As the threat of climate change and global warming becomes more alarming, the need to mitigate its effects becomes increasingly urgent. Hence, lowering atmospheric carbon dioxide (CO2) levels through developing sustainable and efficient plays a crucial role. Magnesium Oxide has been identified as a promising adsorbent material due to its wide operation temperature range, abundancy and high theoretical CO2 capture capacity [1]. However, due carbonate blocking effect through formation of MgCO3 on the surface of MgO obstructs further adsorption of CO2 by inner layers of MgO [2]. To mitigate and control carbonate blocking effect, we explored the synthesis of mesoporous MgO nanoparticles with a flowerlike structure by integrating electrospinning synthesis with a straightforward steaming process, followed by adehydration step under inert conditions. The synthesized samples underwent characterization utilizing X-ray diffraction (XRD), scanning electron microscopy (SEM), and Brunauer-Emmett-Teller (BET) analysis to gain a thorough insight into their crystal structure, morphology, and surface properties. Additionally, the CO2 capture capacity was evaluated using a Thermogravimetric Analyzer (TGA). During the synthesis process, we observed a morphological transformation from spherical MgO nanoparticles to flowerlike mesoporous MgO nanosheets. The average thickness of these mesoporous MgO sheets was approximately 20nm. Compared to the spherical electrospun MgO particles, the mesoporous MgO flowerlike structure increased the CO2 capture capacity by 42.5 %. Additionally, the rate of adsorption was increased of significantly, suggesting a significant enhancement in CO2 diffusion post-grafting of the mesoporous flowerlike structure. These findings directly tackle the environmental challenge by presenting an approach to synthesize facile MgObased CO2 absorbents and contributing to the development of more efficient and environmentally friendly carbon capture solutions.