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Florini D, Gandolfi D, Mapelli J, Benatti L, Pavan P, Puglisi FM. A Hybrid CMOS-Memristor Spiking Neural Network Supporting Multiple Learning Rules. IEEE Trans Neural Netw Learn Syst 2024; 35:5117-5129. [PMID: 36099218 DOI: 10.1109/tnnls.2022.3202501] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Artificial intelligence (AI) is changing the way computing is performed to cope with real-world, ill-defined tasks for which traditional algorithms fail. AI requires significant memory access, thus running into the von Neumann bottleneck when implemented in standard computing platforms. In this respect, low-latency energy-efficient in-memory computing can be achieved by exploiting emerging memristive devices, given their ability to emulate synaptic plasticity, which provides a path to design large-scale brain-inspired spiking neural networks (SNNs). Several plasticity rules have been described in the brain and their coexistence in the same network largely expands the computational capabilities of a given circuit. In this work, starting from the electrical characterization and modeling of the memristor device, we propose a neuro-synaptic architecture that co-integrates in a unique platform with a single type of synaptic device to implement two distinct learning rules, namely, the spike-timing-dependent plasticity (STDP) and the Bienenstock-Cooper-Munro (BCM). This architecture, by exploiting the aforementioned learning rules, successfully addressed two different tasks of unsupervised learning.
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2
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Pazos S, Zheng W, Zanotti T, Aguirre F, Becker T, Shen Y, Zhu K, Yuan Y, Wirth G, Puglisi FM, Roldán JB, Palumbo F, Lanza M. Hardware implementation of a true random number generator integrating a hexagonal boron nitride memristor with a commercial microcontroller. Nanoscale 2023; 15:2171-2180. [PMID: 36628646 DOI: 10.1039/d2nr06222d] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The development of the internet-of-things requires cheap, light, small and reliable true random number generator (TRNG) circuits to encrypt the data-generated by objects or humans-before transmitting them. However, all current solutions consume too much power and require a relatively large battery, hindering the integration of TRNG circuits on most objects. Here we fabricated a TRNG circuit by exploiting stable random telegraph noise (RTN) current signals produced by memristors made of two-dimensional (2D) multi-layered hexagonal boron nitride (h-BN) grown by chemical vapor deposition and coupled with inkjet-printed Ag electrodes. When biased at small constant voltages (≤70 mV), the Ag/h-BN/Ag memristors exhibit RTN signals with very low power consumption (∼5.25 nW) and a relatively high current on/off ratio (∼2) for long periods (>1 hour). We constructed TRNG circuits connecting an h-BN memristor to a small, light and cheap commercial microcontroller, producing a highly-stochastic, high-throughput signal (up to 7.8 Mbit s-1) even if the RTN at the input gets interrupted for long times up to 20 s, and if the stochasticity of the RTN signal is reduced. Our study presents the first full hardware implementation of 2D-material-based TRNGs, enabled by the unique stability and figures of merit of the RTN signals in h-BN based memristors.
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Affiliation(s)
- Sebastian Pazos
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
- Unidad de Investigación y Desarrollo de las Ingenierías-CONICET, Facultad Regional, Buenos Aires, Universidad Tecnológica Nacional (UIDI-CONICET/FRBA-UTN), Medrano 951 (C1179AAQ), Buenos Aires, Argentina
| | - Wenwen Zheng
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
- Institute of Functional Nano & Soft Materials (FUNSOM), Collaborative Innovation Center of Suzhou Nanoscience and Technology, Soochow University, 199 Ren-Ai Road, Suzhou 215123, China
| | - Tommaso Zanotti
- Dipartimento di Ingegneria "Enzo Ferrari", Università di Modena e Reggio Emilia, Modena, 41125, Italy
| | - Fernando Aguirre
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
| | - Thales Becker
- Electrical Engineering Department, Federal University of Rio Grande do Sul, Porto Alegre, 90035-190, Brazil
| | - Yaqing Shen
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
- Institute of Functional Nano & Soft Materials (FUNSOM), Collaborative Innovation Center of Suzhou Nanoscience and Technology, Soochow University, 199 Ren-Ai Road, Suzhou 215123, China
| | - Kaichen Zhu
- MIND, Department of Electronic and Biomedical Engineering, Universitat de Barcelona, Martí i Franquès 1, E-08028 Barcelona, Spain
| | - Yue Yuan
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
| | - Gilson Wirth
- Electrical Engineering Department, Federal University of Rio Grande do Sul, Porto Alegre, 90035-190, Brazil
| | - Francesco Maria Puglisi
- Dipartimento di Ingegneria "Enzo Ferrari", Università di Modena e Reggio Emilia, Modena, 41125, Italy
| | - Juan Bautista Roldán
- Departamento de Electrónica y Tecnología de Computadores, Facultad de Ciencias, Universidad de Granada, Avd. Fuentenueva s/n, 18071 Granada, Spain
| | - Felix Palumbo
- Unidad de Investigación y Desarrollo de las Ingenierías-CONICET, Facultad Regional, Buenos Aires, Universidad Tecnológica Nacional (UIDI-CONICET/FRBA-UTN), Medrano 951 (C1179AAQ), Buenos Aires, Argentina
| | - Mario Lanza
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
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Gandolfi D, Puglisi FM, Serb A, Giugliano M, Mapelli J. Editorial: Brain-inspired computing: Neuroscience drives the development of new electronics and artificial intelligence. Front Cell Neurosci 2022; 16:1115395. [PMID: 36605614 PMCID: PMC9808067 DOI: 10.3389/fncel.2022.1115395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022] Open
Affiliation(s)
- Daniela Gandolfi
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Francesco Maria Puglisi
- Department of Engineering “Enzo Ferrari,” University of Modena and Reggio Emilia, Modena, Italy,Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy
| | - Alexander Serb
- Centre for Electronics Frontiers, School of Engineering, University of Edinburgh, Edinburgh, United Kingdom
| | - Michele Giugliano
- Neuroscience Area, International School of Advanced Studies (SISSA), Trieste, Italy
| | - Jonathan Mapelli
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy,Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy,*Correspondence: Jonathan Mapelli ✉
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Gandolfi D, Puglisi FM, Boiani GM, Pagnoni G, Friston KJ, D'Angelo EU, Mapelli J. Emergence of associative learning in a neuromorphic inference network. J Neural Eng 2022; 19. [PMID: 35508120 DOI: 10.1088/1741-2552/ac6ca7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/04/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE In the theoretical framework of predictive coding and active inference, the brain can be viewed as instantiating a rich generative model of the world that predicts incoming sensory data while continuously updating its parameters via minimization of prediction errors. While this theory has been successfully applied to cognitive processes - by modelling the activity of functional neural networks at a mesoscopic scale - the validity of the approach when modelling neurons as an ensemble of inferring agents, in a biologically plausible architecture, remained to be explored. APPROACH We modelled a simplified cerebellar circuit with individual neurons acting as Bayesian agents to simulate the classical delayed eyeblink conditioning protocol. Neurons and synapses adjusted their activity to minimize their prediction error, which was used as the network cost function. This cerebellar network was then implemented in hardware by replicating digital neuronal elements via a low-power microcontroller. MAIN RESULTS Persistent changes of synaptic strength - that mirrored neurophysiological observations - emerged via local (neurocentric) prediction error minimization, leading to the expression of associative learning. The same paradigm was effectively emulated in low-power hardware showing remarkably efficient performance compared to conventional neuromorphic architectures. SIGNIFICANCE These findings show that: i) an ensemble of free energy minimizing neurons - organized in a biological plausible architecture - can recapitulate functional self-organization observed in nature, such as associative plasticity, and ii) a neuromorphic network of inference units can learn unsupervised tasks without embedding predefined learning rules in the circuit, thus providing a potential avenue to a novel form of brain-inspired artificial intelligence.
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Affiliation(s)
- Daniela Gandolfi
- Department Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, Modena, Emilia-Romagna, 41121, ITALY
| | - Francesco Maria Puglisi
- DIEF, Universita degli Studi di Modena e Reggio Emilia, Via P. Vivarelli 10/1, Modena, MO, 41121, ITALY
| | - Giulia Maria Boiani
- Department Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, Modena, Emilia-Romagna, 41121, ITALY
| | - Giuseppe Pagnoni
- Department Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, Modena, Emilia-Romagna, 41121, ITALY
| | - Karl J Friston
- Institute of Neurology, University College London, 23 Queen Square, LONDON, WC1N 3BG, London, WC1N 3AR, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Egidio Ugo D'Angelo
- Department Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, Pavia, Pavia, Lombardia, 27100, ITALY
| | - Jonathan Mapelli
- Department Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, Modena, 41125, ITALY
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Lanza M, Waser R, Ielmini D, Yang JJ, Goux L, Suñe J, Kenyon AJ, Mehonic A, Spiga S, Rana V, Wiefels S, Menzel S, Valov I, Villena MA, Miranda E, Jing X, Campabadal F, Gonzalez MB, Aguirre F, Palumbo F, Zhu K, Roldan JB, Puglisi FM, Larcher L, Hou TH, Prodromakis T, Yang Y, Huang P, Wan T, Chai Y, Pey KL, Raghavan N, Dueñas S, Wang T, Xia Q, Pazos S. Standards for the Characterization of Endurance in Resistive Switching Devices. ACS Nano 2021; 15:17214-17231. [PMID: 34730935 DOI: 10.1021/acsnano.1c06980] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices in commercial circuits is related to variability and reliability issues, which are usually evaluated through switching endurance tests. However, we note that most studies that claimed high endurances >106 cycles were based on resistance versus cycle plots that contain very few data points (in many cases even <20), and which are collected in only one device. We recommend not to use such a characterization method because it is highly inaccurate and unreliable (i.e., it cannot reliably demonstrate that the device effectively switches in every cycle and it ignores cycle-to-cycle and device-to-device variability). This has created a blurry vision of the real performance of RS devices and in many cases has exaggerated their potential. This article proposes and describes a method for the correct characterization of switching endurance in RS devices; this method aims to construct endurance plots showing one data point per cycle and resistive state and combine data from multiple devices. Adopting this recommended method should result in more reliable literature in the field of RS technologies, which should accelerate their integration in commercial products.
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Affiliation(s)
- Mario Lanza
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Rainer Waser
- Peter-Grünberg-Institut (PGI-7), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
- Peter-Grünberg-Institut (PGI-10), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
- Institut für Werkstoffe der Elektrotechnik 2 (IWE2), RWTH Aachen University, Aachen 52074, Germany
| | - Daniele Ielmini
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IU.NET, Piazza L. da Vinci 32, Milano, 20133, Italy
| | - J Joshua Yang
- Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States
| | | | - Jordi Suñe
- Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona, Barcelona 08193, Spain
| | - Anthony Joseph Kenyon
- Department of Electronic and Electrical Engineering, University College London, Torrington Place, London WC1E 7JE, United Kingdom
| | - Adnan Mehonic
- Department of Electronic and Electrical Engineering, University College London, Torrington Place, London WC1E 7JE, United Kingdom
| | - Sabina Spiga
- CNR-IMM, Unit of Agrate Brianza, Via C. Olivetti 2, Agrate Brianza (MB) 20864, Italy
| | - Vikas Rana
- Peter-Grünberg-Institut (PGI-10), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Stefan Wiefels
- Peter-Grünberg-Institut (PGI-7), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Stephan Menzel
- Peter-Grünberg-Institut (PGI-7), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Ilia Valov
- Peter-Grünberg-Institut (PGI-7), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Marco A Villena
- Applied Materials Inc., Via Ruini, Reggio Emilia 74L 42122, Italy
| | - Enrique Miranda
- Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona, Barcelona 08193, Spain
| | - Xu Jing
- School of Materials Science and Engineering, Jiangsu Key Laboratory of Advanced Metallic Materials, Southeast University, Nanjing 211189, China
| | - Francesca Campabadal
- Institut de Microelectrònica de Barcelona-Centre Nacional de Microelectrònica, Consejo Superior de Investigaciones Científicas, Bellaterra 08193, Spain
| | - Mireia B Gonzalez
- Institut de Microelectrònica de Barcelona-Centre Nacional de Microelectrònica, Consejo Superior de Investigaciones Científicas, Bellaterra 08193, Spain
| | - Fernando Aguirre
- Unidad de Investigación y Desarrollo de las Ingenierías-CONICET, Facultad Regional Buenos Aires, Universidad Tecnológica Nacional (UIDI-CONICET/FRBA-UTN), Buenos Aires, Medrano 951(C1179AAQ), Argentina
| | - Felix Palumbo
- Unidad de Investigación y Desarrollo de las Ingenierías-CONICET, Facultad Regional Buenos Aires, Universidad Tecnológica Nacional (UIDI-CONICET/FRBA-UTN), Buenos Aires, Medrano 951(C1179AAQ), Argentina
| | - Kaichen Zhu
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Juan Bautista Roldan
- Departamento de Electrónica y Tecnología de Computadores, Facultad de Ciencias, Universidad de Granada, Avd. Fuentenueva s/n, Granada 18071, Spain
| | - Francesco Maria Puglisi
- Dipartimento di Ingegneria "Enzo Ferrari", Università di Modena e Reggio Emilia, Via P. Vivarelli 10/1, Modena 41125, Italy
| | - Luca Larcher
- Applied Materials Inc., Via Ruini, Reggio Emilia 74L 42122, Italy
| | - Tuo-Hung Hou
- Department of Electronics Engineering and Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Themis Prodromakis
- Centre for Electronics Frontiers, University of Southampton, Southampton SO171BJ, United Kingdom
| | - Yuchao Yang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), Department of Micro/nanoelectronics, Peking University, Beijing 100871, China
| | - Peng Huang
- Key Laboratory of Microelectronic Devices and Circuits (MOE), Department of Micro/nanoelectronics, Peking University, Beijing 100871, China
| | - Tianqing Wan
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Yang Chai
- Department of Applied Physics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Kin Leong Pey
- Engineering Product Development, Singapore University of Technology and Design (SUTD), 8 Somapah Road, 487372 Singapore
| | - Nagarajan Raghavan
- Engineering Product Development, Singapore University of Technology and Design (SUTD), 8 Somapah Road, 487372 Singapore
| | - Salvador Dueñas
- Department of Electronics, University of Valladolid, Paseo de Belén 15, Valladolid E-47011, Spain
| | - Tao Wang
- Institute of Functional Nano and Soft Materials (FUNSOM), Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University 199 Ren-Ai Road, Suzhou 215123, China
| | - Qiangfei Xia
- Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, Massachusetts 01003-9292, United States
| | - Sebastian Pazos
- Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
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6
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Wen C, Li X, Zanotti T, Puglisi FM, Shi Y, Saiz F, Antidormi A, Roche S, Zheng W, Liang X, Hu J, Duhm S, Roldan JB, Wu T, Chen V, Pop E, Garrido B, Zhu K, Hui F, Lanza M. Advanced Data Encryption using 2D Materials. Adv Mater 2021; 33:e2100185. [PMID: 34046938 DOI: 10.1002/adma.202100185] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 03/23/2021] [Indexed: 05/25/2023]
Abstract
Advanced data encryption requires the use of true random number generators (TRNGs) to produce unpredictable sequences of bits. TRNG circuits with high degree of randomness and low power consumption may be fabricated by using the random telegraph noise (RTN) current signals produced by polarized metal/insulator/metal (MIM) devices as entropy source. However, the RTN signals produced by MIM devices made of traditional insulators, i.e., transition metal oxides like HfO2 and Al2 O3 , are not stable enough due to the formation and lateral expansion of defect clusters, resulting in undesired current fluctuations and the disappearance of the RTN effect. Here, the fabrication of highly stable TRNG circuits with low power consumption, high degree of randomness (even for a long string of 224 - 1 bits), and high throughput of 1 Mbit s-1 by using MIM devices made of multilayer hexagonal boron nitride (h-BN) is shown. Their application is also demonstrated to produce one-time passwords, which is ideal for the internet-of-everything. The superior stability of the h-BN-based TRNG is related to the presence of few-atoms-wide defects embedded within the layered and crystalline structure of the h-BN stack, which produces a confinement effect that avoids their lateral expansion and results in stable operation.
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Affiliation(s)
- Chao Wen
- Institute of Functional Nano and Soft Materials (FUNSOM), Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, 199 Ren Ai Road, Suzhou, 215123, China
| | - Xuehua Li
- Institute of Functional Nano and Soft Materials (FUNSOM), Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, 199 Ren Ai Road, Suzhou, 215123, China
| | - Tommaso Zanotti
- Dipartimento di Ingegneria "Enzo Ferrari", Università di Modena e Reggio Emilia, Modena, 41125, Italy
| | - Francesco Maria Puglisi
- Dipartimento di Ingegneria "Enzo Ferrari", Università di Modena e Reggio Emilia, Modena, 41125, Italy
| | - Yuanyuan Shi
- IMEC, Kapeldreef 75, Heverlee, Leuven, B-3001, Belgium
| | - Fernan Saiz
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Aleandro Antidormi
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, Barcelona, E-08193, Spain
| | - Stephan Roche
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, Barcelona, E-08193, Spain
- ICREA, Institucio Catalana de Recerca i Estudis Avançats, Barcelona, E-08010, Spain
| | - Wenwen Zheng
- Institute of Functional Nano and Soft Materials (FUNSOM), Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, 199 Ren Ai Road, Suzhou, 215123, China
| | - Xianhu Liang
- Institute of Functional Nano and Soft Materials (FUNSOM), Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, 199 Ren Ai Road, Suzhou, 215123, China
| | - Jiaxin Hu
- Institute of Functional Nano and Soft Materials (FUNSOM), Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, 199 Ren Ai Road, Suzhou, 215123, China
| | - Steffen Duhm
- Institute of Functional Nano and Soft Materials (FUNSOM), Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, 199 Ren Ai Road, Suzhou, 215123, China
| | - Juan B Roldan
- Departamento de Electrónica y Tecnología de Computadores, Universidad de Granada, Facultad de Ciencias, Avd. Fuentenueva s/n, Granada, 18071, Spain
| | - Tianru Wu
- School of Physical Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai, 201210, China
| | - Victoria Chen
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Eric Pop
- Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA
| | - Blas Garrido
- Department of Electronic and Biomedical Engineering, Universitat de Barcelona, Martí i Franquès 1, Barcelona, E-08028, Spain
| | - Kaichen Zhu
- Institute of Functional Nano and Soft Materials (FUNSOM), Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, 199 Ren Ai Road, Suzhou, 215123, China
- Department of Electronic and Biomedical Engineering, Universitat de Barcelona, Martí i Franquès 1, Barcelona, E-08028, Spain
| | - Fei Hui
- Department of Materials Science and Engineering, Technion - Israel Institute of Technology, Haifa, 320003, Israel
| | - Mario Lanza
- Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
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Zagni N, Chini A, Puglisi FM, Pavan P, Verzellesi G. On the Modeling of the Donor/Acceptor Compensation Ratio in Carbon-Doped GaN to Univocally Reproduce Breakdown Voltage and Current Collapse in Lateral GaN Power HEMTs. Micromachines (Basel) 2021; 12:mi12060709. [PMID: 34208780 PMCID: PMC8235448 DOI: 10.3390/mi12060709] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 06/10/2021] [Accepted: 06/15/2021] [Indexed: 11/18/2022]
Abstract
The intentional doping of lateral GaN power high electron mobility transistors (HEMTs) with carbon (C) impurities is a common technique to reduce buffer conductivity and increase breakdown voltage. Due to the introduction of trap levels in the GaN bandgap, it is well known that these impurities give rise to dispersion, leading to the so-called “current collapse” as a collateral effect. Moreover, first-principles calculations and experimental evidence point out that C introduces trap levels of both acceptor and donor types. Here, we report on the modeling of the donor/acceptor compensation ratio (CR), that is, the ratio between the density of donors and acceptors associated with C doping, to consistently and univocally reproduce experimental breakdown voltage (VBD) and current-collapse magnitude (ΔICC). By means of calibrated numerical device simulations, we confirm that ΔICC is controlled by the effective trap concentration (i.e., the difference between the acceptor and donor densities), but we show that it is the total trap concentration (i.e., the sum of acceptor and donor densities) that determines VBD, such that a significant CR of at least 50% (depending on the technology) must be assumed to explain both phenomena quantitatively. The results presented in this work contribute to clarifying several previous reports, and are helpful to device engineers interested in modeling C-doped lateral GaN power HEMTs.
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Affiliation(s)
- Nicolò Zagni
- Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, via P. Vivarelli 10, 41125 Modena, Italy; (A.C.); (F.M.P.); (P.P.)
- Correspondence:
| | - Alessandro Chini
- Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, via P. Vivarelli 10, 41125 Modena, Italy; (A.C.); (F.M.P.); (P.P.)
| | - Francesco Maria Puglisi
- Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, via P. Vivarelli 10, 41125 Modena, Italy; (A.C.); (F.M.P.); (P.P.)
| | - Paolo Pavan
- Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, via P. Vivarelli 10, 41125 Modena, Italy; (A.C.); (F.M.P.); (P.P.)
| | - Giovanni Verzellesi
- Department of Sciences and Methods for Engineering (DISMI) and EN&TECH Center, University of Modena and Reggio Emilia, via G. Amendola, 2, 42122 Reggio Emilia, Italy;
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8
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Wang T, Shi Y, Puglisi FM, Chen S, Zhu K, Zuo Y, Li X, Jing X, Han T, Guo B, Bukvišová K, Kachtík L, Kolíbal M, Wen C, Lanza M. Electroforming in Metal-Oxide Memristive Synapses. ACS Appl Mater Interfaces 2020; 12:11806-11814. [PMID: 32036650 DOI: 10.1021/acsami.9b19362] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Memristors have shown an extraordinary potential to emulate the plastic and dynamic electrical behaviors of biological synapses and have been already used to construct neuromorphic systems with in-memory computing and unsupervised learning capabilities; moreover, the small size and simple fabrication process of memristors make them ideal candidates for ultradense configurations. So far, the properties of memristive electronic synapses (i.e., potentiation/depression, relaxation, linearity) have been extensively analyzed by several groups. However, the dynamics of electroforming in memristive devices, which defines the position, size, shape, and chemical composition of the conductive nanofilaments across the device, has not been analyzed in depth. By applying ramped voltage stress (RVS), constant voltage stress (CVS), and pulsed voltage stress (PVS), we found that electroforming is highly affected by the biasing methods applied. We also found that the technique used to deposit the oxide, the chemical composition of the adjacent metal electrodes, and the polarity of the electrical stimuli applied have important effects on the dynamics of the electroforming process and in subsequent post-electroforming bipolar resistive switching. This work should be of interest to designers of memristive neuromorphic systems and could open the door for the implementation of new bioinspired functionalities into memristive neuromorphic systems.
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Affiliation(s)
- Tao Wang
- Institute of Functional Nano and Soft Materials, Collaborative Innovation Center of Suzhou Nanoscience & Technology, Soochow University, 199 Ren-Ai Road, Suzhou 215123, China
| | - Yuanyuan Shi
- IMEC, Kapeldreef 75, Heverlee, B-3001 Leuven, Belgium
| | - Francesco Maria Puglisi
- Dipartimento di Ingegneria "Enzo Ferrari", Università degli Studi di Modena e Reggio Emilia, Via P. Vivarelli 10/1, 41125 Modena, Missouri, Italy
| | - Shaochuan Chen
- Institute of Functional Nano and Soft Materials, Collaborative Innovation Center of Suzhou Nanoscience & Technology, Soochow University, 199 Ren-Ai Road, Suzhou 215123, China
| | - Kaichen Zhu
- Institute of Functional Nano and Soft Materials, Collaborative Innovation Center of Suzhou Nanoscience & Technology, Soochow University, 199 Ren-Ai Road, Suzhou 215123, China
- Departament d'Enginyeria Electrònica i Biomèdica Universitat de Barcelona Martí i Franquès 1, E-08028 Barcelona, Spain
| | - Ying Zuo
- Institute of Functional Nano and Soft Materials, Collaborative Innovation Center of Suzhou Nanoscience & Technology, Soochow University, 199 Ren-Ai Road, Suzhou 215123, China
| | - Xuehua Li
- Institute of Functional Nano and Soft Materials, Collaborative Innovation Center of Suzhou Nanoscience & Technology, Soochow University, 199 Ren-Ai Road, Suzhou 215123, China
| | - Xu Jing
- Institute of Functional Nano and Soft Materials, Collaborative Innovation Center of Suzhou Nanoscience & Technology, Soochow University, 199 Ren-Ai Road, Suzhou 215123, China
| | - Tingting Han
- Institute of Functional Nano and Soft Materials, Collaborative Innovation Center of Suzhou Nanoscience & Technology, Soochow University, 199 Ren-Ai Road, Suzhou 215123, China
- Departament d'Enginyeria Electrònica i Biomèdica Universitat de Barcelona Martí i Franquès 1, E-08028 Barcelona, Spain
| | - Biyu Guo
- Institute of Functional Nano and Soft Materials, Collaborative Innovation Center of Suzhou Nanoscience & Technology, Soochow University, 199 Ren-Ai Road, Suzhou 215123, China
| | - Kristýna Bukvišová
- Central European Institute of Technology, Brno University of Technology, Purkyňova 123, 61200 Brno, Czech Republic
| | - Lukáš Kachtík
- Central European Institute of Technology, Brno University of Technology, Purkyňova 123, 61200 Brno, Czech Republic
| | - Miroslav Kolíbal
- Central European Institute of Technology, Brno University of Technology, Purkyňova 123, 61200 Brno, Czech Republic
- Institute of Physical Engineering, Brno University of Technology, Technická 2, Brno 61669, Czech Republic
| | - Chao Wen
- Institute of Functional Nano and Soft Materials, Collaborative Innovation Center of Suzhou Nanoscience & Technology, Soochow University, 199 Ren-Ai Road, Suzhou 215123, China
| | - Mario Lanza
- Institute of Functional Nano and Soft Materials, Collaborative Innovation Center of Suzhou Nanoscience & Technology, Soochow University, 199 Ren-Ai Road, Suzhou 215123, China
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