Analog ZTO resistive switching devices for neuromorphic applications
Carlos Silva a, Jonas Deuermeier a, Raquel Martins a, Maria Pereira a, Rodrigo Martins a, Asal Kiazadeh a
a I3N/CENIMAT, Department of Materials Science, NOVA School of Science and Technology and CEMOP/UNINOVA, NOVA University Lisbon, Campus de Caparica, 2829-516 Caparica, Portugal
Proceedings of Neuromorphic Materials, Devices, Circuits and Systems (NeuMatDeCaS)
VALÈNCIA, Spain, 2023 January 23rd - 25th
Organizers: Rohit Abraham John, Irem Boybat, Jason Eshraghian and Simone Fabiano
Poster, Carlos Silva, 063
Publication date: 9th January 2023
ePoster: 

Amorphous oxide semiconductors (AOS) have been recently studied for their use in neuromorphic systems, as they are compatible with cheap, easy and low temperature fabrication processes, making them prime candidates for applications that take flexibility and transparency into account (e.g. System-on-panel applications, wearables). In this work we present three different memristor structures, all including Zinc–Tin–Oxide (ZTO) as the active resistive switching material. By choosing ZTO instead of more heavily studied AOS´s, such as Indium-Gallium-Zinc-Oxide (IGZO) (specially in display technology), we avoid the use of rare elements, resulting in a more sustainable alternative.

While the first structure is fully fabricated at room temperature via PVD methods, the second involves both PVD and ALD processes reaching the maximum temperature of 150°C. The last structure contains an active ZTO layer deposited via solution process, with its contacts deposited by PVD methods.

The three different structures are compared in terms of their materials, fabrication processes, and electrical performance. Quasi-static I-V characteristics, pulse measurements analysis and synaptic performance are studied, having their neuromorphic capabilities assessed in various computational frameworks.

This work is funded by National Funds through the FCT – Fundação para a Ciência e a Tecnologia, I.P., under the scope of the doctoral grant 2021.07840.BD, and 2021.03386.CEECIND. This work also received funding from FEDER funds through the COMPETE 2020 Programme and National Funds through FCT – Portuguese Foundation for Science and Technology under the scope of the project UIDB/50025/2020-2023,  project SUPREME- IT  (Ref: EXPL/CTM-REF/0978/2021). This work also received funding from the European Community’s H2020 program under grant agreements 716510 (ERC-2016-StG TREND), 787410 (ERC-2019-AdG DIGISMART) and 952169 (SYNERGY, H2020-WIDESPREAD-2020-5, CSA).

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