A New FPGA-Implemented Neural Network for Compensating Degradation of AMOLED Displays in Real Time for Long Operation With Temperature Considered

This is the authors' abstract. We don't add key points for this paper.

A new neural network (NN) model is established for compensating effectively in real time the luminance degradation of organic light emitting diodes (OLEDs) in a display operated for an extensive period. The compensation is achieved by three stages of models. First, a model was orchestrated to estimate well the temperature distribution of an OLED display. Second, a new, incremental NN was established based on collected data of degraded OLED luminance with ambient temperature recorded. Third, another algorithm in logic based on interpolation is designed to compensate effectively the degraded OLED luminance in real-time operation of the OLED displays in the shortest time possible. All the above-mentioned 3 algorithms are implemented into hardware via the technology of field programmable gate array (FPGA), with the platform of Xilinx Vivado 2020.1 for realizing the associated codes in Verilog. Based on experimental data, the compensation logics in the FPGA board led to the averaged displaying accuracies of 97.1%, 93.9%, and 95.1% for red, green, and blue OLEDs, respectively, with respect to target luminances over a long period of 1000 h, showing the best performance over all the other works reported in the past. The presented excellent performance attributes are mainly due to the consideration of temperature as one of the inputs to the built degradation NN model and the incremental nature of the model.