Chemical Wave Computing from Labware to Electrical Systems

Unconventional and, specifically, wave computing has been repeatedly studied in laboratory based experiments by utilizing chemical systems like a thin film of Belousov–Zhabotinsky (BZ) reactions. Nonetheless, the principles demonstrated by this chemical computer were mimicked by mathematical models to enhance the understanding of these systems and enable a more detailedinvestigation of their capacity. As expected, the computerized counterparts of the laboratory based experiments are faster and less expensive. A further step of acceleration in wave-based computingis the development of electrical circuits that imitate the dynamics of chemical computers. A key component of the electrical circuits is the memristor which facilitates the non-linear behavior of the chemical systems. As part of this concept, the road-map of the inspiration from wave-based computing on chemical media towards the implementation of equivalent systems on oscillating memristive circuits was studied here. For illustration reasons, the most straightforward example was demonstrated, namely the approximation of Boolean gates

Nanoelectronic devices that emulate the operation of neural networks

In the era of the rapidly expanding Internet of Things (IoT), interdisciplinary research efforts have led to the development of advanced hardware and software technologies for efficient data processing and connectivity. As the demand for power efficient processing units continues to rise, the limitations of conventional complementary metal oxide semiconductor (CMOS) technology necessitate novel design approaches and emerging materials. Among the promising alternatives, resistive random access memory (RRAM) technology has emerged as a key player, offering enhanced storage and computational capabilities, along with dynamic properties. In particular, the metal-insulator-metal (MIM) structure of RRAM, when fabricated in a crossbar array configuration, enables low-temperature material processing and facilitates unique three-dimensional integration possibilities. This study focuses on investigating the resistive switching behavior of a thin layer of SiO2 embedded with two-dimensional molybdenum disulfide (MoS2) in a Conductive-Bridging Random Access Memory (CBRAM) con guration. The proposed device exhibits remarkable characteristics, including improved conductance quantization, reduced variability resulting from suppressed stochastic filament formation, and synaptic properties. Notably, the devices operate in bipolar switching mode without the need for electroforming, showcasing eight quantized conductance states during DC operation and ten quantized states under pulse measurements. Additionally, the devices exhibit enhanced endurance and retention properties, as well as linearity in synaptic potentiating and depressing procedures. These advantageous features can be attributed to the controlled diffusion barrier of Ag ions achieved through the atomic sieve properties of MoS2.Furthermore, this research explores the fabrication of planar memory devices utilizing MoS2 as the active material, employing advanced techniques such as e-beam lithography. The planar memory devices present additional avenues for investigating the electronic properties and memory characteristics of MoS2-based devices, further contributing to the development of efficient and reliable memory technologies.Στην εποχή του ταχέως αναπτυσσόμενου Διαδικτύου (IoT), οι επιστημονικές ερευνητικές προσπάθειες οδήγησαν στην ανάπτυξη προηγμένων τεχνολογιών υλικών και λογισμικών για αποτελεσματική επεξεργασία δεδομένων και συνδεσιμότητας μεταξύ τους. Καθώς η ζήτηση για μονάδες επεξεργασίας χαμηλής ενεργειακής απόδοσης συνεχίζει να αυξάνεται, οι περιορισμοί της συμβατικής τεχνολογίας συμπληρωματικών ημιαγωγών οξειδίου μετάλλου (CMOS) απαιτούν νέες προσεγγίσεις αρχιτεκτονικού σχεδιασμού και χρήση καινοτόμων υλικών. Μεταξύ των πολλά υποσχόμενων εναλλακτικών λύσεων, η τεχνολογία μνήμης αντίστασης τυχαίας πρόσβασης (RRAM) έχει αναδειχθεί ως βασικός παράγοντας, προσφέροντας βελτιωμένες υπολογιστικές δυνατότητες όσον αφορά την ικανότητα αποθήκευσης καθώς και άλλες δυναμικές ιδιότητες. Για παράδειγμα, η δομή μετάλλου-μονωτή-μετάλλου (MIM) της RRAM, όταν κατασκευάζεται σε διάταξη διασταυρούμενων συστοιχιών, επιτρέπει την επεξεργασία σε χαμηλές θερμοκρασίες και παρουσιάζει μοναδικές δυνατότητες τρισδιάστατης ολοκλήρωσης. Η διατριβή αυτή εστιάζει στη διερεύνηση της συμπεριφοράς μεταβλητής αντίστασης ενός λεπτού στρώματος SiO2 με ενσωματωμένο το δισδιάστατο υλικό, διθειούχο μολυβδαίνιο (MoS2) σε μια διάταξη μνήμης αγώγιμης γέφυρας (CBRAM). Η προτεινόμενη διάταξη παρουσιάζει αξιοσημείωτα χαρακτηριστικά, συμπεριλαμβανομένης της βελτιωμένης κβαντικής αγωγιμότητας, μειωμένη μεταβλητότητα (variability) που προκύπτει από τον περιορισμένο σχηματισμό στοχαστικών αγώγιμων νημάτων καθώς και συναπτικές ιδιότητες. Συγκεκριμένα, οι διατάξεις παρουσιάζουν διπολική συμπεριφορά χωρίς την ανάγκη ηλεκτροδιαμόρφωσης (electroforming), παρουσιάζοντας εφτά κβαντισμένες καταστάσεις αγωγιμότητας κατά την εφαρμογή συνεχούς τάσης DC και εφτά κβαντισμένες καταστάσεις υπό μετρήσεις παλμών. Επιπλέον, οι διατάξεις παρουσιάζουν βελτιωμένες ιδιότητες αντοχής και συγκράτησης (endurance and retention), καθώς και ενισχυμένη γραμμικότητα στις διαδικασίες ενίσχυσης και καταστολής των συναπτικών χαρακτηριστικών. Αυτά τα χαρακτηριστικά μπορούν να αποδοθούν στο ελεγχόμενο φράγμα διάχυσης των ιόντων Ag που επιτυγχάνεται μέσω του ατομικού πλέγματος των στρωμάτων του MoS2. Επιπλέον, διερευνήθηκε και η κατασκευή επίπεδων διατάξεων μνήμης που χρησιμοποιούν MoS2 ως ενεργό υλικό, χρησιμοποιώντας τεχνικές όπως η λιθογραφία δέσμης ηλεκτρονίων. Οι διατάξεις μνήμης στο επίπεδο παρουσιάζουν πρόσθετους τρόπους για τη διερεύνηση των ηλεκτρονικών ιδιοτήτων και των χαρακτηριστικών μνήμης των διατάξεων που βασίζονται στην ύπαρξη του MoS2, συμβάλλοντας περαι- τέρω στην ανάπτυξη μιας αποτελεσματικής και αξιόπιστης διάταξης μνήμης

Digital Service Platform and Innovation in Healthcare: Measuring Users’ Satisfaction and Implications

When it comes to scheduling health consultations, e-appointment systems are helpful for patients. Non-attendance is a common obstacle that many medical practitioners must endure when it comes to the management of appointments in healthcare facilities and outpatient health settings. Prior surveys have found that many users are open to use such mechanisms and that patients would be likely to schedule an online appointment with their doctor if such a system was made accessible. Few studies have sought to determine how well e-appointment systems work, how well they are received by their users, and whether or not they increase the number of appointments booked. The purpose of this research was to collect information that would help executives of a state hospital in Thessaloniki, Greece, to improve their electronic appointment system by measuring the level of satisfaction their patients have with it. The results show that the level of service provided by the electronic appointment system is not satisfactory. The quality of the website is another significant factor that does not contribute to the level of satisfaction experienced by patients

Emulating Artificial Synaptic Plasticity Characteristics from SiO2-Based Conductive Bridge Memories with Pt Nanoparticles

The quick growth of information technology has necessitated the need for developing novel electronic devices capable of performing novel neuromorphic computations with low power consumption and a high degree of accuracy. In order to achieve this goal, it is of vital importance to devise artificial neural networks with inherent capabilities of emulating various synaptic properties that play a key role in the learning procedures. Along these lines, we report here the direct impact of a dense layer of Pt nanoparticles that plays the role of the bottom electrode, on the manifestation of the bipolar switching effect within SiO2-based conductive bridge memories. Valuable insights regarding the influence of the thermal conductivity value of the bottom electrode on the conducting filament growth mechanism are provided through the application of a numerical model. The implementation of an intermediate switching transition slope during the SET transition permits the emulation of various artificial synaptic functionalities, such as short-term plasticity, including paired-pulsed facilitation and paired-pulse depression, long-term plasticity and four different types of spike-dependent plasticity. Our approach provides valuable insights toward the development of multifunctional synaptic elements that operate with low power consumption and exhibit biological-like behavior

Material design strategies for emulating neuromorphic functionalities with resistive switching memories

Nowadays, the huge power consumption and the inability of the conventional circuits to deal with real-time classification tasks have necessitated the devising of new electronic devices with inherent neuromorphic functionalities. Resistive switching memories arise as an ideal candidate due to their low footprint and small leakage current dissipation, while their intrinsic randomness is smoothly leveraged for implementing neuromorphic functionalities. In this review, valence change memories or conductive bridge memories for emulating neuromorphic characteristics are demonstrated. Moreover, the impact of the device structure and the incorporation of Pt nanoparticles is thoroughly investigated. Interestingly, our devices possess the ability to emulate various artificial synaptic functionalities, including paired-pulsed facilitation and paired-pulse depression, long-term plasticity and four different types of spike-dependent plasticity. Our approach provides valuable insights from a material design point of view towards the development of multifunctional synaptic elements that operate with low power consumption and exhibit biological-like behavior

Wave cellular automata for computing applications

There is a continuous urge for higher efficiency in conventional computing systems, driven by an ever-growing demand for these systems’ complexity to be able to match the one of convoluted and challenging problems. However, this type of problems has formulated the benchmarks for unconventional computing systems to validate their emerging applicability and prove their effectiveness. Towards this path, Cellular Automata (CAs) have been established as a promising mathematical tool for simulating physical processes and demonstrated a favourable methodology for effectively implementing computations in hardware by taking advantage of their inherent parallelism. Representing CAs with oscillating memristive networks could further enhance the performance of these systems, by incorporating the rich dynamics evident in memristors and their strong memory and computing features. In this work, a wave generator circuit has been designed with low-voltage fabricated CBRAM devices, that is able to act as a Wave Cellular Automaton (WCA). These wave generation units are located on a grid with adjusting multi-directional interconnections between neighbors. In addition to that, the ability to reconFigure the amount of such units that influence each other, facilitates the propagation of voltage signals through the grid following wave propagation features. An example of this computational domain is presented with the realization of complex logic gates on the grid of WCAs.Peer ReviewedPostprint (published version

Unconventional memristive nanodevices

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.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.Postprint (author's final draft

Unconventional computing with memristive nanocircuits

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.Postprint (author's final draft

Compact thermo-diffusion based physical memristor model

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The threshold switching effect is critical in memristor devices for a range of applications, from crossbar design reliability to simulating neuromorphic features using artificial neural networks. The rich inherit dynamics of a metallic conductive filament (CF) formation are thought to be linked to this characteristic. Simulating these dynamics is necessary to develop an accurate memristor model. In this work we present a compact memristor model that utilizes the drift, diffusion and thermo-diffusion effects. These three effects are taken into consideration to derive the switching behavior of a memristor. The resistance of a memristor is calculated based on the evolution of a truncated cone shaped filament. The objective of this model is to achieve a realistic integration of switching mechanisms of the memristor device, while minimizing the overhead on computing resources and being compatible with circuit design tools. The model incorporates the effect of thermo-diffusion on the switching pattern, providing a different perception of the ionic transport processes, which enable the unipolar switching. SPICE simulation results provide an exact match with experimental results of Metal-Insulator-Metal (MIM) memristive devices of Ag/Si2/SiO2.07/Pt nanoparticles (NPs) configuration.Peer ReviewedPostprint (published version