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.

Temperature-dependent electroluminescence of red high-In-content MQWs of dual-wavelength micro-LED.

Temperature-dependent electroluminescence (TDEL) measurements have been employed to investigate the carrier transport and recombination processes of InGaN red micro-LED based on dual-wavelength InGaN/GaN MQWs structure. EL peak energy and carrier transport of the red micro-LED both show temperature dependence, due to temperature-induced changes in defect activation. In addition, the current density at which the blue peak of the low-In-content appears in the EL spectrum varies with temperature. As the temperature increases, the blue peak of the low In component tends to appear at higher current densities, which may be attributed to the increase in thermally activated defects hindering the injection of holes into the low-In-content MQWs further away from p-GaN. Furthermore, the IQEs of the high-In-content MQWs are estimated from the TDEL method and then reveal the temperature-dependent efficiency droop. The IQE decreases as temperature increases, particularly above 50 K, where it drops sharply due to temperature-dependent nonradiative recombination. And the two different variation trends in IQE of MQWs with high and low In content reveal a competitive mechanism in carrier distribution, implying that more escaping holes from high-In-content MQWs will further reduce red emission efficiency but enhance carrier injection and blue emission in low-In-content MQWs.

Phenylazothiazoles as Visible-Light Photoswitches.

A novel class of photoswitches based on a phenylazothiazole scaffold that undergoes reversible isomerization under visible-light irradiation is reported. The photoswitch, which comprises a thiazole heteroaryl segment directly connected to a phenyl azo chromophore, has very different spectral characteristics, such as a redshifted absorption maximum wavelength and well-separated absorption bands of the trans and cis isomers, than conventional azobenzene and other heteroaryl azo compounds. Substituents at the ortho and para positions of the phenyl ring of the photoswitch resulted in a further shift to longer wavelengths up to 525 nm at the absorption maximum with a small thermal stability compensation. These photoswitches showed excellent photostationary distributions of the trans and cis isomers, thermal half-lives of up to 7.2 h, and excellent reductant stability. The X-ray crystal structure analysis revealed that the trans isomers exhibited a planar geometry and the cis isomers exhibited a T-shaped orthogonal geometry. Detailed ab initio calculations further demonstrated the plausible electronic transitions and isomerization energy barriers, which were consistent with the experimental observations. The fundamental design principles elucidated in this study will aid in the development of a wide variety of visible-light photoswitches for photopharmacological applications.

Improvements of the modulation bandwidth and data rate of green-emitting CsPbBr3 perovskite quantum dots for Gbps visible light communication.

CsPbBr3 perovskite quantum dots (PQDs) as promising color conversion materials have been widely used in display and visible light communication (VLC), but most CsPbBr3 PQDs for VLC are randomly selected without optimization. Thereby the exploration of fundamental experimental parameters of QDs is essential to better employ their performance advantages. Herein, we investigated the concentration and solvent effects on photoluminescence (PL) properties and communication performance of CsPbBr3 PQDs. The PL, time-resolved PL characterization and communication measurements of CsPbBr3 PQDs all exhibit concentration dependence, suggesting that there exists an optimum concentration to take advantages of performance merits. CsPbBr3 PQDs with a concentration of 0.5 mg/ml show the shortest carrier lifetime and achieve the highest -3 dB bandwidth (168.03 MHz) as well as the highest data rate (660 Mbps) comparing to other concentrations. And in terms of the optimal concentration, we further explored the approach to improve the communication performance, investigating the effect of polarity solvent on the communication performance of CsPbBr3 PQDs. Original 0.5 mg/ml CsPbBr3 PQDs (1 ml) with 55 µL ethanol added in obtain a higher -3 dB bandwidth of 363.68 MHz improved by ∼116.4% and a record data rate of 1.25 Gbps improved by ∼89.4% but weaker PL emission due to energy transfer. The energy transfer assisted improvement may open up a promising avenue to improve the communication performance of QDs, but more feasible energy transfer path needs to be explored to ensure the stability of QDs.

InGaN micro-LED array with integrated emission and detection functions for color detection application.

InGaN-based micro-LEDs can detect and emit optical signals simultaneously, owing to their overlapping emission and absorption spectra, enabling color detection. In this paper, we fabricated a green InGaN-based micro-LED array with integrated emission and detection functions. On the back side of the integrated device, when the 80 µm micro-LED emitted light, the 200 µm LED could receive reflected light to accomplish color detection. The spacing between the 80 µm and the 200 µm micro-LEDs was optimized to be 1 mm to reduce the effect of the direct light transmitted through the n-GaN layer without reflection. The integrated device shows good detection performance for different colors and skin colors, even in a dark environment. In addition, light can be emitted from the top side of the device. Utilization of light from both sides of the integrated device provides the possibility of its application in display, communication, and detection on the different sides.

Vertical stack integration of blue and yellow InGaN micro-LED arrays for display and wavelength division multiplexing visible light communication applications.

In this work, we demonstrated a convenient and reliable method to realize the vertical stack integration of the blue and yellow InGaN micro-LED arrays. The standard white and color-tunable micro-light sources can be achieved by adjusting the current densities injection of the micro-LEDs. The spectra cover violet, standard white, cyan, etc., showing an excellent color-tunable property. And the mixed standard white light can be separated into red-green-blue three primary colors through the color filters to realize full-color micro-LED display with a color gamut of 75% NTSC. Besides, the communication capability of the integrated micro-LED arrays as visible light communication (VLC) transmitters is demonstrated with a maximum total data rate of 2.35 Gbps in the wavelength division multiplexing (WDM) experimental set-up using orthogonal frequency division multiplexing modulation. In addition, a data rate of 250 Mbps is also realized with the standard white light using on-off keying (OOK) modulation. This integrated device shows great potential in full-color micro-LED display, color-tunable micro-light sources, and high-speed WDM VLC multifunctional applications.

High bandwidth series-biased green micro-LED array toward 6 Gbps visible light communication.

In this Letter, a record modulation bandwidth of 1.31 GHz was achieved by a 10 µm c-plane green micro light emitting diode (micro-LED) at a current density of 41.4 kA/cm2. Furthermore, by designing a series-biased 20 µm micro-LED with higher light output power, combined with an orthogonal frequency division multiplexing modulation scheme, a maximum data rate of 5.789 Gbps was achieved at a free-space transmission distance of 0.5 m. This work demonstrates the prospect of c-plane polar green micro-LED in ultrahigh-speed visible light communication, which is expected to realize a high-performance wireless system in the future.

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.

Impact of atmospheric particulate matter retention on physiological characters of five plant species under different pollution levels in Zhengzhou.

Atmospheric particulate matter (PM) pollution has become a major environmental risk, and green plants can mitigate air pollution by regulating their enzymatic activity, osmoregulatory substances, photosynthetic pigments, and other biochemical characteristics. The present investigation aims to evaluate the mitigation potential of five common evergreen tree species (Photinia serrulata, Ligustrum lucidum, Eriobotrya japonica, Euonymus japonicus, Pittosporum tobira) against air pollution and to assess the effect of dust retention on plant physiological functions exposed to three different pollution levels (road, campus, and park). The results found that the amount of dust retained per unit leaf area of the plants was proportional to the mass concentration of atmospheric particulate matter in the environment, and that dust accumulation was higher on the road and campus than in the park. There were significant differences in dust retention among the five tree species, with the highest leaf dust accumulation observed for E. japonica (5.45 g·m-2), and the lowest for P. tobira (1.53 g·m-2). In addition, the increase in PM adsorption by different plants was uneven with increasing pollution levels, with significant decreases in chlorophyll content, photosynthetic and transpiration rate. From a physiological perspective, P. tobira exhibited greater potential to respond to PM pollution. Biochemical indicators suggested that PM pollution caused changes in plant protective enzyme activities, with a decrease in superoxide dismutase (SOD) and peroxidase (POD) activities, as well as promoting membrane lipid peroxidation, and appropriate stress also enables plants to counteract oxidative damage. In particular, PM exposure also induced stomatal constriction. Overall, PM retention was significantly associated with physiological and photosynthetic traits. In conclusion, our study contributes to the understanding of the effects of PM on plant physiology. Furthermore, it also provides insights into the selection of plants that are tolerant to PM pollution.

Numerical and Experiment Analysis of Sapphire Sandwich-Structure Fabry-Perot Pressure Sensor through Fast Fourier Transform and Mean Square Error Demodulation Algorithm.

Pressure sensors prepared from sapphire exhibit excellent characteristics, including high-temperature resistance, high hardness, and resistance to electromagnetic interference. A Fast Fourier Transform and Mean Square Error (FFT-MSE) demodulation algorithm was employed to demodulate a sapphire sandwich-structure Fabry-Perot (F-P) pressure sensor. Through simulation analysis, the experimental results indicated that the demodulation error of the air cavity length in the range of 206 µm to 216 µm was less than 0.0008%. Compared to single demodulation methods and combined demodulation methods based on FFT or Minimum Mean Square Error (MMSE), the method proposed in this work reduced the demodulation error by more than three times and increased accuracy by more than six times. The algorithm was utilized to demodulate the sapphire sandwich-structure F-P pressure sensor, and the test results indicated that the fitting error of the sensor was less than 0.025% within the pressure range of 0 MPa to 10 MPa. The repeatability error was less than 0.066%, the zero-point deviation was 1.26%, and the maximum stability deviation was 0.0063% per 30 min. The algorithm effectively demodulated the actual cavity length variation in the sapphire sandwich-structure F-P pressure sensor, providing a solution for the performance evaluation of the sapphire sandwich-structure F-P pressure sensor.

Accelerating reinforcement learning with case-based model-assisted experience augmentation for process control.

In the context of intelligent manufacturing in the process industry, traditional model-based optimization control methods cannot adapt to the situation of drastic changes in working conditions or operating modes. Reinforcement learning (RL) directly achieves the control objective by interacting with the environment, and has significant advantages in the presence of uncertainty since it does not require an explicit model of the operating plant. However, most RL algorithms fail to retain transfer learning capabilities in the presence of mode variation, which becomes a practical obstacle to industrial process control applications. To address these issues, we design a framework that uses local data augmentation to improve the training efficiency and transfer learning (adaptability) performance. Therefore, this paper proposes a novel RL control algorithm, CBR-MA-DDPG, organically integrating case-based reasoning (CBR), model-assisted (MA) experience augmentation, and deep deterministic policy gradient (DDPG). When the operating mode changes, CBR-MA-DDPG can quickly adapt to the varying environment and achieve the desired control performance within several training episodes. Experimental analyses on a continuous stirred tank reactor (CSTR) and an organic Rankine cycle (ORC) demonstrate the superiority of the proposed method in terms of both adaptability and control performance/robustness. The results show that the control performance of the CBR-MA-DDPG agent outperforms the conventional PI and MPC control schemes, and that it has higher training efficiency than the state-of-the-art DDPG, TD3, and PPO algorithms in transfer learning scenarios with mode shift situations.

Event-Triggered Constrained Optimal Control for Organic Rankine Cycle Systems via Safe Reinforcement Learning.

The organic Rankine cycle (ORC) is an effective application for converting low-grade heat sources into power and is crucial for environmentally friendly production and energy recovery. However, the inherent complexity of the mechanism, its strong and unidentified nonlinearity, and the presence of control constraints severely impair the design of its optimal controller. To solve these issues, this study provides a novel event-triggered (ET) constrained optimal control approach for the ORC systems based on a safe reinforcement learning technique to find the optimal control law. Instead of employing the usual non-quadratic integral form to solve the control-limited optimal control problems, a constraint handling strategy based on a relaxed weighted barrier function (BF) technique is proposed. By adding the BF terms to the original value function, a modified value iteration algorithm is developed to make the control input solutions that tend to violate the constraints be pushed back and maintained in their safe sets. In addition, the ET mechanism proposed in this article is critically required for the ORC systems, and it can significantly reduce the computational load. The combination of these two techniques allows the ORC systems to achieve set-point tracking control and satisfy the control restrictions. The proposed approach is conducted based on a heuristic dynamic programming framework with three neural networks (NNs) involved. The safety and convergence of the proposed approach and the stability of the closed-loop system are analyzed. Simulation results and comparisons are presented to demonstrate its effectiveness.