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

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, Greece, led by Professor Paschalis Gkoupidenis, shifted its focus to the development and fabrication of neuromorphic devices, highlighting the role of organic materials and showcasing progress in material science. In 2023, the third edition, held in Bad Schandau, Germany, under Dr. Hans Kleemann, emphasized theoretical modeling and simulation of neuromorphic devices. Sponsored by Advanced Electronic Materials, this special collection presents key works from the 2023 NOD workshop. The most recent edition of the workshop, held in 2024 in Paris, was organized by Professor Laurie Calvet. It expanded discussions to advanced applications and systems integration, continuing to emphasize organic neuromorphic hardware, biosensors, and computational models, reflecting the growing impact of NODs in neuromorphic computing. A strong theoretical foundation is essential for advancing neuromorphic computing, addressing challenges like ionic behavior, memory stability, and material variability. These studies explore scalability and efficiency, providing critical insights into neuromorphic systems. Barbosa et al. (article number 202400481) investigate the ionic trapping mechanism in Electrochemical Neuromorphic Organic Devices (ENODes). They propose a 2D drift-diffusion model that describes the progressive filling and emptying of trapping sites as a key factor for achieving non-volatility. The authors validate their model experimentally, demonstrating its ability to predict memory stability across various device geometries. This work addresses a fundamental challenge in neuromorphic computing: achieving reliable, energy-efficient memory retention in complex environments. Dreamer et al. (article number 202400373) present a novel approach to modeling Organic Electrochemical Transistors (OECTs). Their work highlights the impact of non-monolithic capacitance on device performance and proposes a method to accurately estimate parasitic resistance. By bridging theoretical predictions with experimental validation, the authors provide a reliable tool for optimizing OECTs in biosensing and neuromorphic applications. This study emphasizes the importance of refining device-level models to enhance system-level performance. Calvet et al. (article number 202400515) employ machine learning to tackle variability in Organic Thin Film Transistors (OTFTs). Using multilayer perceptron (MLP) regression models, they simulate OTFT behavior with high accuracy, addressing one of the most persistent challenges in organic electronics. This work demonstrates the power of AI-driven modeling in improving device predictability and optimizing circuit design for neuromorphic systems. By applying advanced computational techniques, the study expands the theoretical toolbox available for organic neuromorphic devices. Collectively, these works address critical aspects of neuromorphic computing by elucidating device behavior, offering solutions to challenges like memory stability and device variability. By bridging theoretical models with practical applications, they pave the way for more efficient and scalable neuromorphic systems. The integration of neuromorphic devices into biosensing opens new possibilities for healthcare, environmental monitoring, and bioelectronics. A key future application of NOD devices is the development of tailored technologies to interface with biological systems. These efforts connect biosensing detection with neuromorphic organic devices (NODs), demonstrating their ability to enable precise sensing and signal processing. The following studies showcase significant advancements in this field. Vyshniakova et al. (article number 202400509) introduce a hybrid ZnO-organic neuromorphic sensor for ammonia detection. By integrating a ZnO thin-film sensor with organic neuromorphic circuits, the device achieves high sensitivity and signal resilience in noisy environments. This innovation demonstrates the potential of hybrid systems for environmental monitoring, addressing challenges in signal processing and stability. Rondelli et al. (article number 202400467) present a single-electrode organic neuromorphic device designed for dopamine sensing. The device emulates synaptic plasticity and offers real-time mapping of neurotransmitter levels in vivo. This study underscores the importance of bio-compatible neuromorphic sensors for advanced brain-machine interfaces, bridging bioelectronics with neuromorphic technology. Rühl et al. (article number 202400748) focus on MoS₂ Electrolyte-Gated Field-Effect Transistors (EGFETs) for biosensing. MoS₂, a Transition Metal Dichalcogenide (TMDC), can be exfoliated into single atomic layers, enabling highly modifiable interfaces for sensing applications. Since conduction occurs only on the atomically thin layer, these devices hold significant potential for biosensing. However, their impact depends on addressing stability challenges in aqueous environments. This work advances performance optimization through protective designs and material improvements, positioning MoS₂ EGFETs as reliable tools for biosensing in challenging conditions. These contributions emphasize the role of neuromorphic devices in bridging biological and electronic systems. By enhancing sensitivity, stability, and biocompatibility, they demonstrate the potential for transformative applications in healthcare and environmental monitoring. Energy efficiency and scalable fabrication are critical for advancing neuromorphic computing to meet the demands of next-generation AI systems. Addressing these challenges requires innovations in device architecture, materials, and manufacturing processes, as highlighted by the following studies. Fedorova et al. (article number 202400258) provide an in-depth review of Spiking Neural Networks (SNNs), emphasizing their energy efficiency compared to traditional Artificial Neural Networks (ANNs). They detail specific mechanisms by which SNNs leverage event-driven computation to drastically reduce energy consumption. The study highlights the role of memristive hardware in emulating synaptic plasticity and achieving real-time adaptive learning. Additionally, it discusses the potential for integrating biologically inspired feedback mechanisms into neuromorphic architectures, offering a pathway to sustainable AI systems. This review underscores the need to develop energy-efficient designs to meet the growing demands of AI while reducing environmental impact. Gärisch et al. (article number 202400479) explore inkjet printing as a scalable fabrication method for neuromorphic devices using Organic Mixed Ionic-Electronic Conductors (OMIECs). This approach demonstrates the feasibility of scalable fabrication while reducing material waste and achieving performance comparable to traditional spin-coated devices. The study also highlights two-terminal devices, such as synaptic diodes, as innovative alternatives to three-terminal devices, broadening the possibilities for neuromorphic system design. Inkjet printing thus offers a pathway to large-scale, sustainable, and cost-effective production of neuromorphic devices. In conclusion, the works presented here represent only a selection of the outstanding contributions from the NOD Workshop 2023. These studies underscore the inherently multidisciplinary nature of the field, where material science, device engineering, and theoretical modeling converge to push the boundaries of innovation. This dynamic interplay offers an excellent playground for the development and fabrication of novel technologies, paving the way for transformative advancements in neuromorphic devices and their applications. We invite readers to join us at the next NOD Workshop, taking place from October 9-11, 2025 in Berlin, Germany. This event will continue the vibrant discussions and groundbreaking research that define the NOD workshop series.