Printed Zinc Tin Oxide Memristors for Reservoir Computing

Abstract

In this work, fully patterned zinc tin oxide (ZTO) memristors are introduced using inkjet printing. By targeting a scalable, solution-based fabrication approach, highly stable devices with excellent reproducibility and minimal variability are achieved, using ZTO as the active layer, silver (Ag) as the top electrode, and molybdenum as the bottom electrode. The use of sustainable materials like ZTO enhances scalability and environmental compatibility, paving the way for next-generation, low-power neuromorphic computing. The devices successfully fulfill the fundamental criteria for in materia implementation of physical reservoir computing (PRC), including nonlinearity and fading memory property. The devices are successfully trained for classification tasks with MNIST handwritten dataset, achieving 89.4% accuracy and 86.5% by processing 4-bit and 5-bit input temporal sequences. The integration of printed memristors into hardware-based PRC architecture simplifies training complexity, making them particularly advantageous for energy-efficient, wearable AI systems.