Details
Name
Guilherme Luís LeitãoCluster
Networked Intelligent SystemsRole
Research AssistantSince
01st March 2019
Nationality
PortugalCentre
Telecommunications and MultimediaContacts
+351222094000
guilherme.l.leitao@inesctec.pt
2021
Authors
Carvalho, G; Pereira, M; Kiazadeh, A; Tavares, VG;
Publication
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.
2020
Authors
Carvalho, G; Ferreira, JC; Tavares, VG;
Publication
2020 XXXV CONFERENCE ON DESIGN OF CIRCUITS AND INTEGRATED SYSTEMS (DCIS)
Abstract
The access to the final selection minute is only available to applicants.
Please check the confirmation e-mail of your application to obtain the access code.