Publication date: 21st July 2025
The traditional silicon-based computing systems are consuming excessive energy to process the huge amount of data generated daily. Neuromorphic computing is seen as a new, promising alternative as it can potentially reproduce the human brain’s efficiency.[1] Mixed Ionic-Electronic Conducting materials (MIECs) are ideal candidates to fabricate neuromorphic electronic devices as they can mimic the synapses in neurons. However, their performance and reliability need to be further improved to compete with the state-of-the-art silicon-based technology.
In this presentation, we will discuss diverse strategies that have been recently explored in our research group to improve the energy efficiency and performance of MIECs-based electronic devices, i.e., memristors and organic electrochemical memtransistors (OECmTs). Analyzing different examples, we will see how controlling the nature and environment of the migrating cations is a key strategy to tune synaptic plasticity and power consumption in these devices. In addition, we will introduce the use of a novel n-type organic MIEC to fabricate OECmTs, the so-called PBFDO or n-PBDF, which presents outstanding electrical conductivity and stability.[2] n-PBDF OECmTs exhibit non-volatile memory and long-term synaptic functions when H+ are used as migrating ions.[3] The effectiveness of the discussed strategies will be validated through simulations with deep neural network (DNN) models for handwritten digit recognition.[4] This talk aims to demonstrate the importance of understanding the synaptic mechanism to design strategies that can boost performance in neuromorphic devices.
The authors acknowledge the funding provided by the project NEUROVISIONM (Generalitat Valenciana, code: MFA/2022/055).
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