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Sobre

Sobre

Vítor Grade Tavares obteve a sua licenciatura e mestrado pela Universidade de Aveiro, Portugal, e o doutoramento no Computational NeuroEngineering Laboratory, University of Florida, Gainesville, USA, em 2001, ambos em engenharia eletrotécnica. É atualmente Professor Auxiliar na Universidade do Porto e Investigador Sénior do INESC-TEC, Porto. Em 2010 foi Professor Visitante na Carnegie Mellon University, EUA. Os seus interesses de investigação incluem desenho de circuitos integrados de baixa potência, de sinal misto e computação neuro-mórfica e bio-mimética, projeto de circuitos integrados CMOS RF para redes de sensores sem fio e eletrónica transparente. Tem coordenado vários projetos nacionais, bem como tem coordenado localmente projetos europeus. Os prêmios mais recentes incluem codestinatário do prêmio de melhor artigo no IEEE ICUWB 2014 e primeiro lugar no concurso de desenho TSMC em 90nm LP MS / RF em 2009. Também foi premiado com um certificado de agradecimento por contribuições para o avanço do IEEE e das Profissões de Engenharia como Presidente da secção da Sociedade de Educação - Portugal, que cofundou.

Tópicos
de interesse
Detalhes

Detalhes

008
Publicações

2022

Characterization and modeling of resistive switching phenomena in IGZO devices

Autores
Carvalho, G; Pereira, ME; Silva, C; Deuermeier, J; Kiazadeh, A; Tavares, V;

Publicação
AIP ADVANCES

Abstract
This study explores the resistive switching phenomena present in 4 mu m(2) amorphous Indium-Gallium-Zinc Oxide (IGZO) memristors. Despite being extensively reported in the literature, not many studies detail the mechanisms that dominate conduction on the different states of IGZO-based devices. In this article, we demonstrate that resistive switching occurs due to the modulation of the Schottky barrier present at the bottom interface of the device. Furthermore, thermionic field emission and field emission regimes are identified as the dominant conduction mechanisms at the high resistive state of the device, while the bulk-limited ohmic conduction is found at the low resistive state. Due to the high complexity associated with creating compact models of resistive switching, a data-driven model is drafted taking systematic steps. (C) 2022 Author(s).

2022

Flexible Active Crossbar Arrays Using Amorphous Oxide Semiconductor Technology toward Artificial Neural Networks Hardware

Autores
Pereira, ME; Deuermeier, J; Figueiredo, C; Santos, A; Carvalho, G; Tavares, VG; Martins, R; Fortunato, E; Barquinha, P; Kiazadeh, A;

Publicação
ADVANCED ELECTRONIC MATERIALS

Abstract
Memristor crossbar arrays can compose the efficient hardware for artificial intelligent applications. However, the requirements for a linear and symmetric synaptic weight update and low cycle-to-cycle (C2C) and device-to-device variability as well as the sneak-path current issue have been delaying its further development. This study reports on a thin-film amorphous oxide-based 4x4 1-transistor 1-memristor (1T1M) crossbar. The a-IGZO crossbar is built on a flexible polyimide substrate, enabling IoT and wearable applications. In the novel framework, the thin-film transistor and memristor are fabricated at the same level, with the same processing steps and sharing the same materials for all layers. The 1T1M cells show linear and symmetrical plasticity characteristic with low C2C variability. The memristor performs like an analog dot product engine and vector-matrix multiplications in the 4x4 crossbars is demonstrated experimentally, in which the sneak-path current issue is successfully suppressed, resulting in a proof-of-concept for a cost-effective, flexible artificial neural networks hardware.

2022

All-Standard-Cell-Based Analog-to-Digital Architectures Well-Suited for Internet of Things Applications

Autores
Correia, A; Tavares, VG; Barquinha, P; Goes, J;

Publicação
JOURNAL OF LOW POWER ELECTRONICS AND APPLICATIONS

Abstract
In this paper, the most suited analog-to-digital (A/D) converters (ADCs) for Internet of Things (IoT) applications are compared in terms of complexity, dynamic performance, and energy efficiency. Among them, an innovative hybrid topology, a digital-delta (& UDelta;) modulator (& UDelta;M) ADC employing noise shaping (NS), is proposed. To implement the active building blocks, several standard-cell-based synthesizable comparators and amplifiers are examined and compared in terms of their key performance parameters. The simulation results of a fully synthesizable Digital-& UDelta;M with NS using passive and standard-cell-based circuitry show a peak of 72.5 dB in the signal-to-noise and distortion ratio (SNDR) for a 113 kHz input signal and 1 MHz bandwidth (BW). The estimated FoMWalden is close to 16.2 fJ/conv.-step.

2021

A Neural Network Approach towards Generalized Resistive Switching Modelling

Autores
Carvalho, G; Pereira, M; Kiazadeh, A; Tavares, VG;

Publicação
MICROMACHINES

Abstract
Resistive switching behaviour has been demonstrated to be a common characteristic to many materials. In this regard, research teams to date have produced a plethora of different devices exhibiting diverse behaviour, but when system design is considered, finding a 'one-model-fits-all' solution can be quite difficult, or even impossible. However, it is in the interest of the community to achieve more general modelling tools for design that allows a quick model update as devices evolve. Laying the grounds with such a principle, this paper presents an artificial neural network learning approach to resistive switching modelling. The efficacy of the method is demonstrated firstly with two simulated devices and secondly with a 4 mu m(2) amorphous IGZO device. For the amorphous IGZO device, a normalized root-mean-squared error (NRMSE) of 5.66 x 10(-3) is achieved with a [2, 50,50 ,1] network structure, representing a good balance between model complexity and accuracy. A brief study on the number of hidden layers and neurons and its effect on network performance is also conducted with the best NRMSE reported at 4.63 x 10(-3). The low error rate achieved in both simulated and real-world devices is a good indicator that the presented approach is flexible and can suit multiple device types.

2021

A Switching-Mode Power Recycling System for a Radio-Frequency Outphasing Transmitter

Autores
Saraiva, B; Duarte, C; Tavares, VG;

Publicação
2021 XXXVI CONFERENCE ON DESIGN OF CIRCUITS AND INTEGRATED SYSTEMS (DCIS21)

Abstract
This paper reports the development of a power recycling network for a wireless radio-frequency (RF) transmitter combiner. The transmitter makes use of two RF power amplifiers (PAs) in an outphasing architecture, connected at the output by a 180-degree hybrid combiner. In general, to provide isolation between the PAs and prevent nonlinear distortion, an isolation resistor is usually applied at the four-port combiner. However, the main drawback of such approach is the power dissipated at the isolation port, which drastically reduces the overall power efficiency of the outphasing transmitter. In the present work, the isolation port is replaced by an active network that provides the required input impedance for isolation, at the same time it converts the RF signal into dc, feeding it back to the transmitter power supply. Hence, this way, one recycles the power that would be lost in the isolating resistor. The proposed active network comprises a circulator, a resonant rectifier and a dc-dc converter that can be regulated by a maximum power point tracking (MPPT) algorithm. Simulation results for this power recycling system are provided, denoting 61-percent maximum efficiency achieved for an increase of 22-percent peak efficiency for QAM signals with a bandwidth of 250-kHz and carrier frequency equal to 250-MHz when operating at 41-miliwatt output power. © 2021 IEEE.

Teses
supervisionadas

2021

Cellular Neural Networks design for sensor networks.

Autor
João Luís Fernandes Ferreira

Instituição
UP-FEUP

2021

Fake news

Autor
Herbert Laroca Mendes Pinto

Instituição

2021

Jogos para Literacia Criativa: Uma framework para cultivar a literacia criativa através de jogos

Autor
José Manuel da Conceição Raimundo

Instituição
UP-FEUP

2021

Prediction of Privacy Preferences with User Profiles: A Federated Learning Approach

Autor
André Xavier Ribeiro de Almeida Brandão

Instituição
UP-FCUP

2021

Towards a Digital-Twin Based, Multi-sided Market of Data-enabled Product-Services Systems

Autor
Henrique Diogo Cardoso da Silva

Instituição
UP-FEUP