Graphene nanoribbons (GNRs) are the most important emerging Graphene structures for nanoelectronic and sensor applications. GNRs with perfect lattices have been extensively studied, but fabricated GNRs contain lattice defects the effect of which on their electronic properties has not been studied extensively enough. In this paper, we apply the Non-Equilibrium Green's function (NEGF) method combined with tight-binding Hamiltonians to investigate the effect of lattice defects on the conductance of GNRs. We specifically study, butterfly shaped GNRs, which operate effectively as switches, and have been used in CMOS-like architectures. The cases of the most usual defects, namely the single and double vacancy have been analytically examined. The effect of these vacancies was computed by placing them in different regions and with various numbers on GNR nano-devices, namely edges, main body, contacts and narrow regions. The computation results are presented in the form of energy dispersion diagrams as well as diagrams of maximum conductance as a function of the number of lattice defects. We also present results on the defect tolerance of the butterfly shaped GNR devices.

Computing demands are growing rapidly as bigdata and artificial intelligence applications become increasingly tasking. Bio-inspired and quantum-based techniques are proving to be quite promising for the development of novel circuits and systems. These systems can contribute to the resolution of a wider variety of problems while also providing improvements to existing techniques. As the von Neumann architecture’s expected performance, which has been dominant for the past several decades, is now hindered by physical limitations, novel computing architectures, assisted by novel materials and circuit devices, are starting to emerge and provide promising results. The topic of this work is to examine the memory and computing capabilities of emergent memristor-based nanocircuits and demonstrate their advantages compared to their classical counterparts.

One of the most enticing candidates for next-generation computing systems is the memristor. Memristor-based novel architectures have demonstrated considerable promise in replacing or augmenting traditional computing platforms based on the Von Neumann architecture, which faces many issues in the big-data era, as well as in newly developed neuromorphic tasks. Although the current classical computing architecture is unlikely to be abandoned in the foreseeable future, the growing trend of neuromorphic, quantum, and bio-inspired computing schemes calls for more specialized beyond Von Neumann platforms. Memristors showcase multiple advantages in terms of small area footprint, energy efficiency, high endurance, bio-compatibility, and their inherent synaptic and neuromorphic behavior. The topic of this work is to present the memristive devices that meet the requirements for the implementation of the novel beyond Von Neumann applications and examine their switching mechanism and material selection, as well as to conduct a performance comparison between the fabricated devices paving the way for future computing applications.

Graphene nanoribbons with non-magnetic contacts have been extensively studied and are widely used in graphene based devices. In these devices, the nanoribbon conductance is modulated and controlled by applying potentials through top and back gates. Graphene nanoribbons with magnetic contacts have been studied and used as spin valves, mostly at liquid-helium temperatures. The operation of graphene nanoribbons with magnetic contacts at room temperature, where nanoelectronic devices and circuits are expected to operate, has received little attention. Here we study and simulate the operation of graphene nanoribbons with magnetic contacts using tight-binding Hamiltonians and the non-equilibrium Green's functions(NEGF) method, which we extend to incorporate the effect of magnetic contacts. Our results are in very good agreement with experimental measurements of the conductance of graphene nanoribbons with NiFe ferromagnetic contacts. We also perform extensive parametric analysis of the dependence of the nanoribbon conductance on the relative orientations of the contact magnetic polarizations, combined with potentials applied through top and back gates. Our results revealed a very rich parametric space that can be exploited to develop multifunctional platforms which, among others, can be used for digital, analog and neuromorphic computations.

In this paper, taking into consideration the exceptional technological properties of a unique 2-D material, namely Graphene, we are envisioning its usage as the structure material of a non-back-gated re-programmable switching device. The proposed topology is analyzed in depth, not only by verifying its operation and re-programmability as a 2-input XOR, 3-input XOR and 3-input Majority gate, but also by examining its computing performance in terms of area, delay and power dissipation. More specifically, we are utilizing L-shaped Graphene Nanoribbons (GNRs) to develop comb-shaped Graphene based switching devices. These devices are in position with effective programming through biasing to design any combinatorial circuit as resulting from the aforementioned universal set of Boolean gates. The resulting figures of merit regarding the area with a universal footprint of $\text{2.53}~nm^{2}$ for every gate independently of the number of inputs, the propagation delay with $2.05\times {10^{-2}}ps$ and, last but not least, the power dissipation with only $10.204nW$ for the gates with greater number of inputs, are quite encouraging and promising. Moreover, the ability of the proposed topology to pave the way towards the implementation of basic circuits has been further investigated, by demonstrating an example of a 1-bit full adder cell and its sufficient operation arriving from the corresponding successful SPICE simulation results.

Precision viticulture (PV) represents a specialized branch of precision agriculture, focusing on the application of advanced technologies, such as recent advances in electronics, artificial intelligence (AI), and automation, to make informed decisions that improve land productivity and crop quality and optimize vineyard operations. Through real-time sensing, predictive analytics, and automated intervention, PV enhances both the efficiency and sustainability of grape production. By integrating predictive analytics into the field of viticulture, real-time monitoring, timely decision-making, and intervention are enabled, improving resource management efficiency while also improving grapevine yield and quality. This article provides an in-depth review of recent advances in PV, focusing on the role of AI-driven analytics, sensing, and automation in vineyard management. In addition, emerging trends and challenges in the field are presented, highlighting their potential to drive sustainable and high-efficiency agricultural practices.