Abstract
Changes in thin-film transistor (TFT) characteristics can cause nonuniform brightness in organic light-emitting diode (OLED) displays, and external compensation circuits cannot address such changes if they occur rapidly. Our system acquires a short segment of the source voltage curve of the TFT driving each pixel, during the vertical blanking interval. It then estimates the threshold voltage ${V}_{\text {th}}$ and the mobility-related parameter ${k}$ of that TFT, using neural networks and recurrent conditional (RC) learning to perform expectation–maximization (EM). Finally, it corrects the pixel brightness accordingly. The average errors in the estimated ${V}_{\text {th}}$ and ${k}$ are, respectively, 7.3 mV and ${3.7} \times {10}^{-{11}}\,\,\text {F}/\text {V}\text {s}$ , and we demonstrate the visual effect of compensating with these estimates.
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