Highly Responsive Dual-Function Deep-Ultraviolet Neuromorphic Phototransistors Based on Silicon Carbide...

Two-dimensional (2D) semiconductors have been of great interest for phototransistors and neuromorphic devices in recent years because of their unique optical and electronic properties. However, the detectable spectral range and light absorption efficiency are limited for 2D-semiconductor-based phototransistors. Herein, we report a high-performance deep-ultraviolet (DUV) sensitive phototransistor by integrating molybdenum disulfide (MoS₂) with silicon carbide nanoparticles (SiC NPs) to form a van der Waals heterostructure (vdWH), which shows ultrahigh responsivity and detectivity, especially in the DUV spectral range. The SiC NPs/few-layer MoS₂ vdWH phototransistor shows a 20-fold enhancement in responsivity (from 9.4 × 10² to 1.9 × 10⁴ A/W) and 11-fold enhancement in detectivity (from 7.9 × 10¹² to 8.4 × 10¹³ cm × Hz^1/2/W) at 254 nm wavelength, compared to the phototransistor based on few-layer MoS₂ alone. Moreover, the SiC NPs/few-layer MoS₂ vdWH phototransistor also shows higher excitation postsynaptic current (EPSC) and longer retention time of postsynaptic current (PSC) compared to the phototransistor based on few-layer MoS₂ alone. This enables vdWH devices to successfully mimic various biological synaptic functions, including paired-pulse facilitation (PPF), spike-duration-dependent plasticity, spike-number-dependent plasticity, spike-frequency-dependent plasticity, the transition from short-term plasticity (STP) to long-term plasticity (LTP), and long-term depression (LTD) capabilities. The simulation of a deep neural network (DNN) shows that the image inference accuracy based on these SiC NPs/few-layer MoS₂ vdWH neuromorphic phototransistors reaches up to 98.99% even after considering the photoresponsivity variations. The high-performance dual-function neuromorphic optoelectronics based on SiC NPs/MoS₂ vdWH hold great promise for ultrasensitive DUV photodetection, neuromorphic DUV visual sensing, and in-sensor computing applications in a single device.