Advancing Organic Neuromorphic Devices: Progress in Modeling, Fabrication, and Biosensing

Abstract

The rapid rise of artificial intelligence (AI) is revolutionizing industries such as healthcare, robotics, and automation, creating a demand for more efficient, adaptive computing systems. In recognition of AI's profound impact, the 2024 Nobel Prize in Physics was awarded to John J. Hopfield and Geoffrey E. Hinton for their pioneering work in machine learning and artificial neural networks.[1-3] This underscores the growing recognition within the scientific community of AI's transformative role in society. However, the energy-intensive nature of deep learning models and the computational demands of modern AI continue to outpace the capabilities of traditional silicon-based hardware, posing significant sustainability challenges as AI scales.[4, 5] To address these growing challenges, there is an increasing need for hardware that can meet the demands of next-generation AI. The concept of “more-than-more” calls for breakthroughs beyond traditional improvements, such as new materials and innovative architectures. Neuromorphic computing, inspired by the brain, offers a promising direction, providing biological-level energy efficiency and adaptability, making it ideal for next-generation AI hardware.[6-9] While organic materials may not yet offer a complete solution for all AI hardware needs, they hold significant promise in addressing critical challenges in neuromorphic computing. Their flexibility, biocompatibility, and low power consumption make them ideal for applications in wearable systems, bioelectronics, and bio-interfacing technologies—areas where traditional silicon-based devices face limitations. These properties enable the development of Neuromorphic Organic Devices (NODs) that mimic synaptic functions like plasticity, learning, and memory storage. The intrinsic flexibility of organic materials, due to Van der Waals bonding, allows for time-dependent interactions, which are crucial for neuromorphic applications. Moreover, chemical modification can optimize properties, such as ionic conduction in materials like mixed ionic-electronic conductors, further enhancing their dynamic behavior.[10, 11] Low-cost fabrication techniques, such as inkjet printing, also make organic materials a scalable solution for producing integrated neuromorphic devices. Despite the promise of organic materials, they face challenges such as lower carrier mobility and stability issues compared to traditional silicon-based devices. These limitations can hinder their use in high-speed, high-performance neuromorphic devices, and long-term stability remains a critical concern, especially in environments with exposure to oxygen, moisture, and light. Despite these challenges, ongoing research is exploring ways to improve their electrical performance, durability, and scalability, making them a promising area of development for the future of neuromorphic computing. In recent years, the growing interest in organic materials for neuromorphic devices has gained significant momentum, attracting attention from the academic community across diverse fields, ranging from theoretical research to applied device development and material science. As these materials show promise in providing scalable solutions for neuromorphic computing, there is a pressing need to address both the challenges and opportunities they present. This growing demand for deeper exploration has highlighted the need for dedicated forums to discuss how organic neuromorphic devices can advance and the obstacles that remain in their development. To unite the growing community of researchers and experts in the field of NODs, Professor Fabio Biscarini and collaborators launched the first NOD Workshop in 2019 in Ferrara, Italy. The inaugural event featured strong contributions from both device applications and biosensing, particularly in interfacing with biological systems, establishing the workshop's interdisciplinary foundation. Building on this success, the second edition, held in 2022 in Chania, G