In this study, we present a high-efficiency InGaN red micro-LED fabricated by the incorporation of superlattice structure, atomic layer deposition passivation, and a distributed Bragg reflector, exhibiting maximum external quantum efficiency of 5.02% with a low efficiency droop corresponding to an injection current density of 112 A / cm 2 . The fast carrier dynamics in the InGaN is characterized by using time-resolved photoluminescence, which is correlated to a high modulation bandwidth of 271 MHz achieved by a 6 × 25-μm-sized micro-LED array with a data transmission rate of 350 Mbit/s at a high injection current density of 2000 A / cm 2 . It holds great promise for full-color micro-displays as well as high-speed visible light communication applications based on monolithic InGaN micro-LED technologies.

We propose and implement a high-bandwidth white-light visible light communication (VLC) system accomplishing data rate of 2.805 Gbit/s utilizing a semipolar blue micro-LED. The system uses an InGaN/GaN semipolar (20-21) blue micro-LED to excite yellow phosphor film for high-speed VLC. The packaged 30 μm 2 × 4 blue micro-LED array has an electrical-to-optical (EO) bandwidth of 1042.5 MHz and a peak wavelength of 447 nm. The EO bandwidth of the white-light VLC system is 849 MHz. Bit error rate (BER) of 2.709 × 10⁻³ meeting the pre-forward error correction (FEC) threshold is accomplished by employing a bit and power loaded orthogonal frequency division multiplexing (OFDM) signal. The proposed white-light VLC system employs simple and inexpensive yellow phosphor film for white-light conversion, complex color conversion material is not needed. Besides, no optical blue filter is employed in the white-light VLC system. The fabrication of the InGaN/GaN semipolar (20-21) blue micro-LED is discussed, and its characteristics are also evaluated.

New systems and technologies, such as Internet-of-Things (IOT) may require high reliability and high accuracy indoor positioning and tracking of persons and devices in indoor areas. Among different visible-light-positioning (VLP) schemes, received-signal-strength (RSS) scheme is relatively easy to implement. RSS VLP scheme can provide high accuracy positioning if the optical channels between the Txs and Rxs, as well as the received optical powers of different LEDs are accurately known. Unfortunately, these conditions are not easy to achieve in practice. Due to the limited field-of-view (FOV) of the LED lamps, light deficient regions will happen. This light deficient region could be large and significantly affect the positioning accuracy when performing 3-dimentional (3-D) VLP since at these light deficient regions, very weak or even no optical signal is received. In this work, we put forward and demonstrate the RSS VLP system utilizing data pre-processing and convolutional neural network (CNN) to mitigate light deficient regions in VLP system. Traditional ANN model and linear regression (LR) model are also compared with the CNN model, and the results illustrate that the proposed scheme outperforms the other schemes by not only improving the positioning accuracy, but also the error distribution uniformity.

A record 4.343 Gbit/s green color micro-light-emitting-diode (μ-LED) based visible-light-communication (VLC) is demonstrated. We designed and fabricated the InGaN/GaN μ-LED array with modulation bandwidth >1.1 GHz. The micro-LED was grown on semipolar (20-21) orientation, which could offer higher modulation bandwidth at a lower current density.

We propose and demonstrate a green semipolar (20-21) micro-light emitting diode (LED) acting as a high speed visible light communication (VLC) photodiode (PD). The micro-LED PD has the optical-to-electrical (OE) response of 228 MHz. A record data rate of 540 Mbit/s in on-off-keying (OOK) format with free-space transmission distance of 1.1 m was achieved, fulfilling the pre-forward error correction (FEC) limit. Many transmitters (Txs) and receivers (Rxs) is required to support the high density pico/femto-cells in future wireless networks, as well as the Internet-of-Things (IOT) networks. The proposed work could allow the realization of a low-cost, small-footprint and a high level of integration of VLC Txs and Rxs on the same platform.

Machine-learning (ML) can be employed to enhance the positioning accuracy of visible-light-positioning (VLP) system. To diminish the training time and complexity, the whole area is usually divided into several positioning unit cells. Most literatures only focus on the positioning performance within an unit cell, and assume the unit cell can be repeatedly duplicated to cover the whole area. In this work, we propose and demonstrate a positioning unit cell model duplication scheme, named as spatial sequence adaptation (SSA) process. We also propose and demonstrate a residual concatenation neural network (RCNN) and transfer learning (TL) to refine the model of the target positioning unit cell. A practical test-bed with vertical distance of 2.8 m consisting of two unit cells with dimensions of about 1.55 m × 2 m per cell is constructed. The client side is an autonomous mobile robot (AMR) for acquiring continuous training and testing data. Our experimental results reveal that high precision positioning in the duplicated unit cell duplication can be achieved.