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

Advanced Electronic Materials, vol. 10

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][2][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 biologicallevel energy efficiency and adaptability, making it ideal for nextgeneration AI hardware.[6][7][8][9]

Authors 3

1. Giovanni Ligorio

   Humboldt-Universität zu Berlin

   Affiliation as printed

   Center for the Science of Materials Berlin Humboldt‐Universität zu Berlin Zum Großen Windkanal 2 12489 Berlin Germany

   Institut für Physik, Institut für Chemie Humboldt‐Universität zu Berlin Zum Großen Windkanal 2 12489 Berlin Germany

2. Francesca Santoro Aachen Faculty of Electrical Engineering and Information Technology

   RWTH Aachen University · Forschungszentrum Jülich · Italian Institute of Technology

   Affiliation as printed

   Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany

   Institute of Biological Information Processing—Bioelectronics (IBI‐3) Forschungszentrum Jülich Germany

   Tissue Electronics Istituto Italiano di Tecnologia Naples Italy

3. Hans Kleemann

   Technische Universität Dresden · Dresden Integrated Center for Applied Physics and Photonic Materials

   Affiliation as printed

   IAPP Dresden, Institute for Applied Physics Technische Universität Dresden Nöthnitzer Str. 61 01187 Dresden Germany