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Kim J, Lee S, Seo Y, Kim S. Emulating biological synaptic characteristics of HfOx/AlN-based 3D vertical resistive memory for neuromorphic systems. J Chem Phys 2024; 160:144703. [PMID: 38587228 DOI: 10.1063/5.0202610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 03/21/2024] [Indexed: 04/09/2024] Open
Abstract
Here, we demonstrate double-layer 3D vertical resistive random-access memory with a hole-type structure embedding Pt/HfOx/AlN/TiN memory cells, conduct analog resistive switching, and examine the potential of memristors for use in neuromorphic systems. The electrical characteristics, including resistive switching, retention, and endurance, of each layer are also obtained. Additionally, we investigate various synaptic characteristics, such as spike-timing dependent plasticity, spike-amplitude dependent plasticity, spike-rate dependent plasticity, spike-duration dependent plasticity, and spike-number dependent plasticity. This synapse emulation holds great potential for neuromorphic computing applications. Furthermore, potentiation and depression are manifested through identical pulses based on DC resistive switching. The pattern recognition rates within the neural network are evaluated, and based on the conductance changing linearly with incremental pulses, we achieve a pattern recognition accuracy of over 95%. Finally, the device's stability and synapse characteristics exhibit excellent potential for use in neuromorphic systems.
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Affiliation(s)
- Juri Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Subaek Lee
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Yeongkyo Seo
- Department of Information and Communication Engineering, Inha University, Incheon 22212, Republic of Korea
| | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
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2
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Ferro M, Falagario UG, Barone B, Maggi M, Crocetto F, Busetto GM, Giudice FD, Terracciano D, Lucarelli G, Lasorsa F, Catellani M, Brescia A, Mistretta FA, Luzzago S, Piccinelli ML, Vartolomei MD, Jereczek-Fossa BA, Musi G, Montanari E, Cobelli OD, Tataru OS. Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement. Diagnostics (Basel) 2023; 13:2308. [PMID: 37443700 DOI: 10.3390/diagnostics13132308] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 07/03/2023] [Accepted: 07/05/2023] [Indexed: 07/15/2023] Open
Abstract
Artificial intelligence is highly regarded as the most promising future technology that will have a great impact on healthcare across all specialties. Its subsets, machine learning, deep learning, and artificial neural networks, are able to automatically learn from massive amounts of data and can improve the prediction algorithms to enhance their performance. This area is still under development, but the latest evidence shows great potential in the diagnosis, prognosis, and treatment of urological diseases, including bladder cancer, which are currently using old prediction tools and historical nomograms. This review focuses on highly significant and comprehensive literature evidence of artificial intelligence in the management of bladder cancer and investigates the near introduction in clinical practice.
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Affiliation(s)
- Matteo Ferro
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
| | - Ugo Giovanni Falagario
- Department of Urology and Organ Transplantation, University of Foggia, 71121 Foggia, Italy
| | - Biagio Barone
- Urology Unit, Department of Surgical Sciences, AORN Sant'Anna e San Sebastiano, 81100 Caserta, Italy
| | - Martina Maggi
- Department of Maternal Infant and Urologic Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, 00161 Rome, Italy
| | - Felice Crocetto
- Department of Neurosciences and Reproductive Sciences and Odontostomatology, University of Naples Federico II, 80131 Naples, Italy
| | - Gian Maria Busetto
- Department of Urology and Organ Transplantation, University of Foggia, 71121 Foggia, Italy
| | - Francesco Del Giudice
- Department of Maternal Infant and Urologic Sciences, Policlinico Umberto I Hospital, Sapienza University of Rome, 00161 Rome, Italy
| | - Daniela Terracciano
- Department of Translational Medical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Giuseppe Lucarelli
- Urology, Andrology and Kidney Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari, 70124 Bari, Italy
| | - Francesco Lasorsa
- Urology, Andrology and Kidney Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari, 70124 Bari, Italy
| | - Michele Catellani
- Department of Urology, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
| | - Antonio Brescia
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
| | - Francesco Alessandro Mistretta
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Stefano Luzzago
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Mattia Luca Piccinelli
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
| | | | - Barbara Alicja Jereczek-Fossa
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
- Division of Radiation Oncology, IEO-European Institute of Oncology IRCCS, 20141 Milan, Italy
| | - Gennaro Musi
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Emanuele Montanari
- Department of Urology, Foundation IRCCS Ca' Granda-Ospedale Maggiore Policlinico, 20122 Milan, Italy
- Department of Clinical Sciences and Community Health, University of Milan, 20122 Milan, Italy
| | - Ottavio de Cobelli
- Department of Urology, IEO-European Institute of Oncology, IRCCS-Istituto di Ricovero e Cura a Carattere Scientifico, 20141 Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Octavian Sabin Tataru
- Department of Simulation Applied in Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mures, 540142 Târgu Mures, Romania
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Galinsky VL, Frank LR. Critically synchronized brain waves form an effective, robust and flexible basis for human memory and learning. Sci Rep 2023; 13:4343. [PMID: 36928606 PMCID: PMC10020450 DOI: 10.1038/s41598-023-31365-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 03/10/2023] [Indexed: 03/18/2023] Open
Abstract
The effectiveness, robustness, and flexibility of memory and learning constitute the very essence of human natural intelligence, cognition, and consciousness. However, currently accepted views on these subjects have, to date, been put forth without any basis on a true physical theory of how the brain communicates internally via its electrical signals. This lack of a solid theoretical framework has implications not only for our understanding of how the brain works, but also for wide range of computational models developed from the standard orthodox view of brain neuronal organization and brain network derived functioning based on the Hodgkin-Huxley ad-hoc circuit analogies that have produced a multitude of Artificial, Recurrent, Convolution, Spiking, etc., Neural Networks (ARCSe NNs) that have in turn led to the standard algorithms that form the basis of artificial intelligence (AI) and machine learning (ML) methods. Our hypothesis, based upon our recently developed physical model of weakly evanescent brain wave propagation (WETCOW) is that, contrary to the current orthodox model that brain neurons just integrate and fire under accompaniment of slow leaking, they can instead perform much more sophisticated tasks of efficient coherent synchronization/desynchronization guided by the collective influence of propagating nonlinear near critical brain waves, the waves that currently assumed to be nothing but inconsequential subthreshold noise. In this paper we highlight the learning and memory capabilities of our WETCOW framework and then apply it to the specific application of AI/ML and Neural Networks. We demonstrate that the learning inspired by these critically synchronized brain waves is shallow, yet its timing and accuracy outperforms deep ARCSe counterparts on standard test datasets. These results have implications for both our understanding of brain function and for the wide range of AI/ML applications.
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Affiliation(s)
- Vitaly L Galinsky
- Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, CA, 92037-0854, USA.
| | - Lawrence R Frank
- Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, CA, 92037-0854, USA
- Center for Functional MRI, University of California at San Diego, La Jolla, CA, 92037-0677, USA
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Review on the Basic Circuit Elements and Memristor Interpretation: Analysis, Technology and Applications. JOURNAL OF LOW POWER ELECTRONICS AND APPLICATIONS 2022. [DOI: 10.3390/jlpea12030044] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Circuit or electronic components are useful elements allowing the realization of different circuit functionalities. The resistor, capacitor and inductor represent the three commonly known basic passive circuit elements owing to their fundamental nature relating them to the four circuit variables, namely voltage, magnetic flux, current and electric charge. The memory resistor (or memristor) was claimed to be the fourth basic passive circuit element, complementing the resistor, capacitor and inductor. This paper presents a review on the four basic passive circuit elements. After a brief recall on the first three known basic passive circuit elements, a thorough description of the memristor follows. Memristor sparks interest in the scientific community due to its interesting features, for example nano-scalability, memory capability, conductance modulation, connection flexibility and compatibility with CMOS technology, etc. These features among many others are currently in high demand on an industrial scale. For this reason, thousands of memristor-based applications are reported. Hence, the paper presents an in-depth overview of the philosophical argumentations of memristor, technologies and applications.
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Ternary Neural Networks Based on on/off Memristors: Set-Up and Training. ELECTRONICS 2022. [DOI: 10.3390/electronics11101526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Neuromorphic systems based on hardware neural networks (HNNs) are expected to be an energy and time-efficient computing architecture for solving complex tasks. In this paper, we consider the implementation of deep neural networks (DNNs) using crossbar arrays of memristors. More specifically, we considered the case where such devices can be configured in just two states: the low-resistance state (LRS) and the high-resistance state (HRS). HNNs suffer from several non-idealities that need to be solved when mapping our software-based models. A clear example in memristor-based neural networks is conductance variability, which is inherent to resistive switching devices, so achieving good performance in an HNN largely depends on the development of reliable weight storage or, alternatively, mitigation techniques against weight uncertainty. In this manuscript, we provide guidelines for a system-level designer where we take into account several issues related to the set-up of the HNN, such as what the appropriate conductance value in the LRS is or the adaptive conversion of current outputs at one stage to input voltages for the next stage. A second contribution is the training of the system, which is performed via offline learning, and considering the hardware imperfections, which in this case are conductance fluctuations. Finally, the resulting inference system is tested in two well-known databases from MNIST, showing that is competitive in terms of classification performance against the software-based counterpart. Additional advice and insights on system tuning and expected performance are given throughout the paper.
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Lin J, Liu H, Wang S, Wang D, Wu L. The Image Identification Application with HfO 2-Based Replaceable 1T1R Neural Networks. NANOMATERIALS 2022; 12:nano12071075. [PMID: 35407193 PMCID: PMC9000711 DOI: 10.3390/nano12071075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/11/2022] [Accepted: 03/23/2022] [Indexed: 11/16/2022]
Abstract
This paper mainly studies the hardware implementation of a fully connected neural network based on the 1T1R (one-transistor-one-resistor) array and its application in handwritten digital image recognition. The 1T1R arrays are prepared by connecting the memristor and nMOSFET in series, and a single-layer and a double-layer fully connected neural network are established. The recognition accuracy of 8 × 8 handwritten digital images reaches 95.19%. By randomly replacing the devices with failed devices, it is found that the stuck-off devices have little effect on the accuracy of the network, but the stuck-on devices will cause a sharp reduction of accuracy. By using the measured conductivity adjustment range and precision data of the memristor, the relationship between the recognition accuracy of the network and the number of hidden neurons is simulated. The simulation results match the experimental results. Compared with the neural network based on the precision of 32-bit floating point, the difference is lower than 1%.
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Aguirre FL, Suñé J, Miranda E. SPICE Implementation of the Dynamic Memdiode Model for Bipolar Resistive Switching Devices. MICROMACHINES 2022; 13:330. [PMID: 35208454 PMCID: PMC8874538 DOI: 10.3390/mi13020330] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/11/2022] [Accepted: 02/14/2022] [Indexed: 11/25/2022]
Abstract
This paper reports the fundamentals and the SPICE implementation of the Dynamic Memdiode Model (DMM) for the conduction characteristics of bipolar-type resistive switching (RS) devices. Following Prof. Chua's memristive devices theory, the memdiode model comprises two equations, one for the electron transport based on a heuristic extension of the quantum point-contact model for filamentary conduction in thin dielectrics and a second equation for the internal memory state related to the reversible displacement of atomic species within the oxide film. The DMM represents a breakthrough with respect to the previous Quasi-static Memdiode Model (QMM) since it describes the memory state of the device as a balance equation incorporating both the snapback and snapforward effects, features of utmost importance for the accurate and realistic simulation of the RS phenomenon. The DMM allows simple setting of the initial memory condition as well as decoupled modeling of the set and reset transitions. The model equations are implemented in the LTSpice simulator using an equivalent circuital approach with behavioral components and sources. The practical details of the model implementation and its modes of use are also discussed.
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Affiliation(s)
- Fernando Leonel Aguirre
- Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain;
| | | | - Enrique Miranda
- Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain;
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Wang Z, Qi G. Modeling and Analysis of a Three-Terminal-Memristor-Based Conservative Chaotic System. ENTROPY 2021; 23:e23010071. [PMID: 33406791 PMCID: PMC7823887 DOI: 10.3390/e23010071] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 12/21/2020] [Accepted: 12/28/2020] [Indexed: 01/15/2023]
Abstract
In this paper, a three-terminal memristor is constructed and studied through changing dual-port output instead of one-port. A new conservative memristor-based chaotic system is built by embedding this three-terminal memristor into a newly proposed four-dimensional (4D) Euler equation. The generalized Hamiltonian energy function has been given, and it is composed of conservative and non-conservative parts of the Hamiltonian. The Hamiltonian of the Euler equation remains constant, while the three-terminal memristor’s Hamiltonian is mutative, causing non-conservation in energy. Through proof, only centers or saddles equilibria exist, which meets the definition of the conservative system. A non-Hamiltonian conservative chaotic system is proposed. The Hamiltonian of the conservative part determines whether the system can produce chaos or not. The non-conservative part affects the dynamic of the system based on the conservative part. The chaotic and quasiperiodic orbits are generated when the system has different Hamiltonian levels. Lyapunov exponent (LE), Poincaré map, bifurcation and Hamiltonian diagrams are used to analyze the dynamical behavior of the non-Hamiltonian conservative chaotic system. The frequency and initial values of the system have an extensive variable range. Through the mechanism adjustment, instead of trial-and-error, the maximum LE of the system can even reach an incredible value of 963. An analog circuit is implemented to verify the existence of the non-Hamiltonian conservative chaotic system, which overcomes the challenge that a little bias will lead to the disappearance of conservative chaos.
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Improving Prediction of Springback in Sheet Metal Forming Using Multilayer Perceptron-Based Genetic Algorithm. MATERIALS 2020; 13:ma13143129. [PMID: 32674296 PMCID: PMC7412272 DOI: 10.3390/ma13143129] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 07/01/2020] [Accepted: 07/08/2020] [Indexed: 01/22/2023]
Abstract
This paper presents the results of predictions of springback of cold-rolled anisotropic steel sheets using an approach based on a multilayer perceptron-based artificial neural network (ANN) coupled with a genetic algorithm (GA). A GA was used to optimise the number of input parameters of the multilayer perceptron that was trained using different algorithms. In the investigations, the mechanical parameters of sheet material determined in uniaxial tensile tests were used as input parameters to train the ANN. The springback coefficient, determined experimentally in the V-die air bending test, was used as an output variable. It was found that specimens cut along the rolling direction exhibit higher values of springback coefficient than specimens cut transverse to the rolling direction. An increase in the bending angle leads to an increase in the springback coefficient. A GA-based analysis has shown that Young’s modulus and ultimate tensile stress are variables having no significant effect on the coefficient of springback. Multilayer perceptrons trained by back propagation, conjugate gradients and Lavenberg–Marquardt algorithms definitely favour punch bend depth under load as the most important variables affecting the springback coefficient.
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