Flexible neuromorphic for in-sensor computing with synaptic transistors

Abstract

The basic shortcomings of traditional von Neumann architectures have been revealed by the quick spread of edge devices and data-intensive sensing technologies, especially with regard to latency, energy consumption, and data transfer bottlenecks. By enabling data processing right at the moment of acquisition, in-sensor computing has emerged as a promising paradigm to address these issues. Simultaneously, flexible electronics have special possibilities for the development of wearable, lightweight, and conformable intelligent systems. In this regard, synaptic transistor-based flexible neuromorphic devices offer an appealing framework for highly effective and versatile biological signal processing emulation. The design, materials, and working mechanisms of synaptic transistors are the main topics of this review, which thoroughly examines current developments in flexible neuromorphic devices for in-sensor computing. We analyse a variety of material systems, including organic, inorganic, and newly developed low-dimensional materials, and we talk about important device physics that underlie synaptic functions, such as short- and long-term plasticity and spike-timing-dependent learning. Performance criteria like energy efficiency, mechanical robustness, and scalability are examined, as well as integration methodologies for integrating sensing and computing at the device and system levels. Lastly, we outline future paths toward fully autonomous, flexible neuromorphic sensory systems for next-generation wearable electronics, soft robotics, and bio-integrated applications. We also highlight current challenges, such as device variability, environmental stability, and large-area integration.