Carvalho, GuilhermePereira, MariaKiazadeh, AsalTavares, Vítor Grade2022-12-022022-12-022021-092072-666XPURE: 45670957PURE UUID: 213bde01-0cb0-4508-91e3-299affd2f628Scopus: 85115656626WOS: 000701117800001PubMed: 34577775PubMedCentral: PMC8468067ORCID: /0000-0002-8422-5762/work/116509186http://hdl.handle.net/10362/145970Funding: This research was funded by FEDER funds through the COMPETE 2020 Programme and National Funds through FCT—Portuguese Foundation for Science and Technology under project number DFA/BD/8335/2020. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.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 µm2 amorphous IGZO device. For the amorphous IGZO device, a normalized root-mean-squared error (NRMSE) of 5.66 × 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 × 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.133509698engA-IGZOArtificial neural network (ANN)Device modellingresistive switchingControl and Systems EngineeringMechanical EngineeringElectrical and Electronic Engineeringjournal article10.3390/mi12091132https://www.scopus.com/pages/publications/85115656626