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Updated: May 23, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
Electrosynthesis of molecular memory elements
Pradeep Sachan1, Anwesha Mahapatra1, Lalith Adithya Sai Channapragada2
1Department of Chemistry, Indian Institute of Technology Kanpur Uttar Pradesh-208016 India pcmondal@iitk.ac.in.
Abstract:
The increasing pace of computing beyond Moore's law scaling and the von Neumann bottleneck necessitates a universal memory solution that offers high speed, low-power consumption, scalability, and non-volatility, such as resistive switching memristors. However, inconsistencies in the homogeneity and uniformity of surface coverage for switching materials on various electrode substrates, especially those prepared via non-covalent methods, result in reduced interfacial stability, thus yielding poor device reproducibility. Electrosynthesis, a reliable and versatile technique for creating covalently bound molecular films on electrode surfaces, enables controlled deposition of large-area, high-quality molecular thin films with nanoscale thicknesses, making it an ideal platform for scalable nanoelectronics. This study explores the electrochemical grafting of two distinct ruthenium complexes: structurally symmetrical [Ru(tpy-ph-NH2)2](2PF6)] (1) and asymmetrical [Ru(tpy-ph-NH2)(naptpy)](2PF6)] (2), for the fabrication of large-area, two-terminal molecular junctions intended for resistive switching memory applications. A comparative analysis reveals that 2 exhibits relatively superior memory performance to 1, attributed to its donor-acceptor configuration playing a crucial role. Stable vertical molecular junctions with the configuration ITO/Ru complex24nm/Al were fabricated, and electrical measurements were carried out to understand the enhanced switching characteristics. The redox-active molecular devices demonstrate non-volatile resistive switching behavior within a ±3.0 V operation window, featuring a large I ON/I OFF ratio (∼103), a high power consumption ratio (SET/RESET = 25.5 mJ/75000 mJ), and switching time (SET/RESET = 56/24 ms). Synapse-like potentiation and convolutional neural network simulation were performed, highlighting the potential of these devices for in-memory data processing applications.

