Scalable Fabrication of Neuromorphic Devices Using Inkjet Printing for the Deposition of Organic Mixed Ionic-Electronic Conductor

Abstract

Recent advancements in artificial intelligence (AI) have highlighted the critical need for energy-efficient hardware solutions, especially in edge-computing applications. However, traditional AI approaches are plagued by significant power consumption. In response, researchers have turned to biomimetic strategies, drawing inspiration from the ion-mediated operating principle of biological synapses, to develop organic neuromorphic devices as promising alternatives. Organic mixed ionic-electronic conductor (OMIEC) materials have emerged as particularly noteworthy in this field, due to their potential for enhancing neuromorphic computing capabilities. Together with device performance, it is crucial to select devices that allow fabrication via scalable techniques. This study investigates the fabrication of OMIEC-based neuromorphic devices using inkjet printing, providing a scalable and material-efficient approach. Employing a commercially available polymer mixed ionic-electronic conductor (BTEM-PPV) and a lithium salt, inkjet-printed devices exhibit performance comparable to those fabricated via traditional spin-coating methods. These two-terminal neuromorphic devices demonstrate functionality analogous to literature-known devices and demonstrate promising frequency-dependent short-term plasticity. Furthermore, comparative studies with previous light-emitting electrochemical cells (LECs) and neuromorphic OMIEC devices validate the efficacy of inkjet printing as a potential fabrication technique. The findings suggest that inkjet printing is suitable for large-scale production, offering reproducible and stable fabrication processes. By adopting the OMIEC material system, inkjet printing holds the potential for further enhancing device performance and functionality. Overall, this study underscores the viability of inkjet printing as a scalable fabrication method for OMIEC-based neuromorphic devices, paving the way for advancements in AI hardware.