Perspective discusses fabrication challenges and potential applications of 1T1R structures in flexible electronics, suggesting future directions.
High Resolution Image Download MS PowerPoint Slide The increasing demand for energy-efficient and high-density hardware driven by artificial intelligence and the Internet of Things has exposed the limitations of conventional computing systems based on the von Neumann architecture. Consequently, neuromorphic computing has emerged as a promising paradigm, enabling in-memory data processing inspired by biological systems. The memristive array is one of the most promising hardware solutions for implementing various neuromorphic algorithms. Among the different unit cell configurations constituting a memristive array, the 1T1R architecture, combining a transistor and a memristor, has demonstrated significant potential due to its ability to provide controllable operation and reduced crosstalk. At the same time, printed electronics has gained attention as a low-cost and scalable fabrication approach, compatible with flexible substrates and low-temperature processing. However, the implementation of 1T1R structures using printing techniques introduces several challenges, including ink formulation, interfacial compatibility between printed layers, and multilayer alignment. This perspective provides an overview of 1T1R architectures with a particular focus on their fabrication through printing-based methods. The device structure, electrical performance, and integration strategies are discussed, along with the main challenges associated with printed techniques. The potential of 1T1R structures for applications in neuromorphic hardware, analog computing, and flexible IoT platforms is highlighted, outlining future directions toward scalable and low-power printed electronic systems.
No takes yet. Share an insight, caveat, or question.
Martins et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: