Organic neuromorphic devices: Past, present, and future challenges

Organic neuromorphic devices: Past, present, and future challenges

The main goal of the field of neuromorphic computing is to build machines that emulate aspects of the brain in its ability to perform complex tasks in parallel and with great energy efficiency. Thanks to new computing architectures, these machines could revolutionize high-performance computing and find applications to perform local, low-energy computing for sensors and robots. The use of organic and soft materials in neuromorphic computing is appealing in many respects, for instance, because it allows better integration with living matter to seamlessly meld sensing with signal processing, and ultimately, stimulation in a closed-feedback loop. Indeed, not only can the mechanical properties of organic materials match those of tissue, but also, the working mechanisms of these devices involving ions, in addition to electrons, are compatible with human physiology. Another advantage of organic materials is the potential to introduce novel fabrication techniques relying on additive manufacturing amenable to one-of-a-kind form factors. This field is still nascent, therefore many concepts are still being proposed, without a clear winner. Furthermore, the field of application of organic neuromorphics, where bioinspiration and biointegration are extremely appealing, calls for a co-design approach from materials to systems.

神经形态计算领域的主要目标是制造能够模拟大脑某些方面的机器，使其具备并行执行复杂任务的能力以及极高的能源效率。借助新的计算架构，这些机器可能会给高性能计算带来革命性变化，并在传感器和机器人的局部低能耗计算方面找到应用。在神经形态计算中使用有机和软质材料在很多方面都具有吸引力，例如，它能够更好地与生物物质整合，将传感与信号处理无缝融合，并最终在闭环反馈中实现刺激。事实上，有机材料不仅机械性能可与组织相匹配，而且这些涉及离子以及电子的设备的工作机制也与人的生理机能相兼容。有机材料的另一个优势在于有可能引入依赖于增材制造的新型制造技术，以适应独特的外形规格。这个领域仍处于起步阶段，因此很多概念仍在被提出，尚无明显的优胜者。此外，有机神经形态学的应用领域（其中生物启发和生物整合极具吸引力）需要从材料到系统的协同设计方法。