Carbon nanotube-based bio-inspired neuron systems via cascaded thin-film transistor-driven light emitting diodes and optoelectronic synaptic transistors for neuromorphic computing

Abstract The development of bio-inspired neural systems has emerged as a transformative approach to overcome the limitations of von Neumann architecture, replicating the remarkable energy efficiency and unified sensory-processing capabilities of biological neurons. In this work, we present a monolithic neuromorphic platform utilizing cascaded single-walled carbon nanotube thin-film transistors (SWCNT TFTs) that integrate Mini-light-emitting diodes (Mini-LEDs) with optoelectronic synaptic transistors, achieving synergistic optoelectronic integration. The SWCNT TFTs exhibit dual functionality: (1) as highly stable active-matrix drivers (>1000 operational cycles) enabling precise Mini-LED grayscale modulation, and (2) as efficient optoelectronic synaptic devices. Fabricated at wafer-scale with micrometer feature sizes, these devices demonstrate exceptional performance metrics, including low operating voltages (±1 V), high on/off ratios (10⁶), near-ideal subthreshold swing (78 mV/dec), and precise Mini-LED current regulation (10⁻⁸~10⁻⁴ A) under 25 Hz pulsed gate operation. The optoelectronic synaptic devices based on organic-semiconductor heterojunction formed between poly(3,3'''-didodecyl quaterthiophene) (PQT-12) and semiconducting SWCNTs enable broadband photoresponses (365 nm-710 nm) through efficient charge transport, driven by TFT-controlled Mini-LED pulses. The implemented bio-inspired visual system successfully emulates fundamental synaptic functionalities, exhibiting excitatory postsynaptic currents (EPSC), short-term potentiation (STP) and long-term potentiation (LTP). Notably, we demonstrate system-level functionality through a five-layer convolutional neural network achieving 92.02% accuracy on MNIST classification, while the monolithic integration establishes a biomimetic closed-loop "electrical-optical-electrical" pathway that faithfully simulates complete biological synaptic operation. This pioneering cascade of electronic, photonic and optoelectronic components represents a significant advancement toward high-density, energy-efficient neuromorphic computing.