Yoeri van de Burgt

Organic materials offer a transformative opportunity for smart edge applications due to their compatibility with large-area manufacturing, mechanical flexibility, scalability at low cost, and potentially reduced environmental impact. While their use in display technologies is now well-established, their integration into other applications domains is still in its early stages. Significant progress has been made in the development of materials for organic neuromorphic devices, enabling explorations of applications in neuroscience, soft robotics and healthcare. Additional promising directions include photonics, optoelectronics and memories. The goal of this roadmap is to consider how the properties of these materials can be leveraged to implement alternative computing methodologies to enable a new generation of organic technologies.

Nano-enabled living materials and living electronics represent the next frontier in integrating biology with advanced nanotechnology, offering unprecedented opportunities to design systems with programmable, adaptive, and multifunctional capabilities. By combining living cells or tissues with engineered nanostructures, living materials and electronics create platforms for bi-directional communication, sensing, and actuation. These advancements hold immense potential for applications in healthcare, energy systems, and environmental sustainability. This roadmap provides a comprehensive vision for advancing this transformative field, addressing scientific challenges, technological pathways, and long-term goals for deploying these hybrid systems at scale.

Organic mixed ionic‐electronic conductors (OMIECs) have the unique ability to transport both ionic and electronic charges, making them the ideal choice for interaction between biology (ionic) and human technology (electronic). Applied first to sense biological environments, OMIECs are now used to develop brain‐inspired circuits capable of seamlessly “communicating” with biology. However, patterning these organic materials in complex circuits is a challenge as traditional microfabrication techniques are not fully compatible. Current research is still heavily relying on a technique called peel‐off, which involves the physical removal of a sacrificial layer, making it cumbersome to develop highly integrated circuits. To circumvent these fabrication limitations, a photo‐patternable PEDOT:PSS is developed by blending it with a photo‐sensitive interpenetrating network, allowing features down to 2 µm to be patterned using photolithography on a wide range of substrates. The process fully retains the ability of the OMIEC to transport ions and electrical charges; moreover, organic electrochemical transistors fabricated using this technique outperform the widely used chemically‐cross‐linked GOPS PEDOT:PSS, while having a batch‐to‐batch variability below 5%. Our photo‐patterning process opens the door to monolithic fabrication of miniaturized and highly integrated circuits, allowing to push the limits of interaction between biology and man‐made technology.

The rapid growth of artificial intelligence (AI) is increasingly constrained by fundamental hardware bottlenecks in computation throughput and energy efficiency. Bioinspired computing (BIC) offers a promising alternative by emulating the intrinsic advantages of biological systems, such as parallelism, adaptability, and robustness. Progress in BIC hardware demands interdisciplinary convergence that bridges materials science and device physics with neuroscience, computer science, mathematics, and information science. Therefore, the development of this cross-disciplinary field urgently requires a comprehensive roadmap that analyzes systematically and in-depth the frontier issues and the latest progress. In this roadmap, we categorize the critical challenges into three components: hardware foundations, architectures, and prototype realizations. We highlight how biological features inspire the design of BIC hardware through device physics and discuss their performance metrics and engineering challenges. We then describe how diverse signaling rules and structural organizations in BIC architectures support specific computational prototypes, including electronic and photonic BIC chips, and present a technological roadmap that outlines opportunities to expand the functional scope of BIC hardware through coordinated advances in devices, architectures, and system demonstrations. This ongoing convergence of interdisciplinary knowledge can help accelerate the shift toward high-efficiency AI hardware.

Analog non‐volatile memory devices, such as electrochemical random‐access memory (ECRAM), have emerged as a promising platform for in‐memory computing, facilitating efficient data processing. In this research, a pioneering approach is presented by introducing an all‐organic and fully integrated crossbar array comprising 3 × 3 ECRAM devices, notable for its facile fabrication employing photolithography techniques and exclusive utilization of organic materials for the devices and resistors. The crossbar array demonstrates remarkable capabilities, enabling inference and in situ parallel training, leading to high accuracy when classifying linearly separable 2D and 3D tasks. Notably, the biocompatible nature of the materials employed in the array offers promising prospects for the development of smart and adaptable bioelectronics that can directly interface with the biological environment.

Neural network training can be slow and energy-expensive due to the frequent transfer of weight data between digital memory and processing units. Neuromorphic systems can accelerate neural networks by performing multiply-accumulate operations in parallel using nonvolatile analog memory. However, executing the widely used backpropagation training algorithm in multilayer neural networks requires information—and therefore storage—of the partial derivatives of the weight values preventing suitable and scalable implementation in hardware. Here, we propose a hardware implementation of the backpropagation algorithm that progressively updates each layer using in situ stochastic gradient descent, avoiding this storage requirement. We experimentally demonstrate the in situ error calculation and the proposed progressive backpropagation method in a multilayer hardware-implemented neural network. We confirm identical learning characteristics and classification performance compared to conventional backpropagation in software. We show that our approach can be scaled to large and deep neural networks, enabling highly efficient training of advanced artificial intelligence computing systems.

Biological systems interact directly with the environment and learn by receiving multimodal feedback via sensory stimuli that shape the formation of internal neuronal representations. Drawing inspiration from biological concepts such as exploration and sensory processing that eventually lead to behavioral conditioning, we present a robotic system handling objects through multimodal learning. A small-scale organic neuromorphic circuit locally integrates and adaptively processes multimodal sensory stimuli, enabling the robot to interact intelligently with its surroundings. The real-time handling of sensory stimuli via low-voltage organic neuromorphic devices with synaptic functionality forms multimodal associative connections that lead to behavioral conditioning, and thus the robot learns to avoid potentially dangerous objects. This work demonstrates that adaptive neuro-inspired circuitry with multifunctional organic materials, can accommodate locally efficient bio-inspired learning for advancing intelligent robotics.

Xiaodong Chen, Cheol Seong Hwang, Yoeri van de Burgt and Francesca Santoro present a themed collection in Materials Horizons and Nanoscale Horizons gathering the latest developments in memristive materials, device fabrication, characterization and circuit design for neuromorphic systems.

Organic electrochemical transistors (OECTs) are of great interest in low‐power bioelectronics and neuromorphic computing, as they utilize organic mixed ionic‐electronic conductors (OMIECs) to transduce ionic signals into electrical signals. However, the poor environmental stability of OMIEC materials significantly restricts the practical application of OECTs. Therefore, the non‐fused planar naphthalenediimide (NDI)‐dialkoxybithiazole (2Tz) copolymers are fine‐tuned through varying ethylene glycol (EG) side chain lengths from tri(ethylene glycol) to hexa(ethylene glycol) (namely P‐XO, X = 3–6) to achieve OECTs with high‐stability and low threshold voltage. As a result, the NDI‐2Tz copolymers exhibit ambipolarity, rapid response (5. This work sheds light on the design of exceptional n‐type/ambipolar materials for OECTs. It demonstrates the potential of incorporating these ambipolar polymers into water‐operational integrated circuits for long‐term biosensing systems and energy‐efficient brain‐inspired computing.

Accurate glucose prediction is vital for diabetes management. Artificial intelligence and artificial neural networks (ANNs) are showing promising results for reliable glucose predictions, offering timely warnings for glucose fluctuations. The translation of these software‐based ANNs into dedicated computing hardware opens a route toward automated insulin delivery systems ultimately enhancing the quality of life for diabetic patients. ANNs are transforming this field, potentially leading to implantable smart prediction devices and ultimately to a fully artificial pancreas. However, this transition presents several challenges, including the need for specialized, compact, lightweight, and low‐power hardware. Organic polymer‐based electronics are a promising solution as they have the ability to implement the behavior of neural networks, operate at low voltage, and possess key attributes like flexibility, stretchability, and biocompatibility. Here, the study focuses on implementing software‐based neural networks for glucose prediction into hardware systems. How to minimize network requirements, downscale the architecture, and integrate the neural network with electrochemical neuromorphic organic devices, meeting the strict demands of smart implants for in‐body computation of glucose prediction is investigated.