Maskless photolithography based on ultraviolet micro-LEDs and direct writing method for improving pattern resolution.

Ultraviolet micro-LEDs show great potential as a light source for maskless photolithography. However, there are few reports on micro-LED based maskless photolithography systems, and the studies on the effects of system parameters on exposure patterns are still lacking. Hence, we developed a maskless photolithography system that employs micro-LEDs with peak wavelength 375 nm to produce micrometer-sized exposure patterns in photoresists. We also systematically explored the effects of exposure time and current density of micro-LED on static direct writing patterns, as well as the effects of stage velocity and current pulse width on dynamic direct writing patterns. Furthermore, reducing the size of micro-LED pixels enables obtaining high-resolution exposure patterns, but this approach will bring technical challenges and high costs. Therefore, this paper proposes an oblique direct writing method that, instead of reducing the micro-LED pixel size, improves the pattern resolution by changing the tilt angle of the sample. The experimental results show that the linewidths of the exposed lines decreased by 4.0% and 15.2%, respectively, as the sample tilt angle increased from 0° to 15° and 30°, which confirms the feasibility of the proposed method to improve the pattern resolution. This method is also expected to correct the exposure pattern error caused by optical distortion of the lens in the photolithography system. The system and method reported can be applied in various fields such as PCBs, photovoltaics, solar cells, and MEMS.

Ultrafast machine vision with artificial neural network devices based on a GaN-based micro-LED array.

GaN-based micro-LED is an emerging display and communication device, which can work as well as a photodetector, enabling possible applications in machine vision. In this work, we measured the characteristics of micro-LED based photodetector experimentally and proposed a feasible simulation of a novel artificial neural network (ANN) device for the first time based on a micro-LED based photodetector array, providing ultrafast imaging (∼133 million bins per second) and a high image recognition rate. The array itself constitutes a neural network, in which the synaptic weights are tunable by the bias voltage. It has the potentials to be integrated with novel machine vision and reconfigurable computing applications, acting as a role of acceleration and similar functionality expansion. Also, the multi-functionality of micro-LED broadens its application potentials of combining ANN with display and communication.