P‐97: Applying a generative model to improve TFT measurement capacity performance

AMOLED is based on current driving, so the current characteristics of TFT IV(current per voltage) have much influence. Now, as the technology of AMOLED is expanded to HOP(hybrid of Oxide and Polysilicon TFT) and UPC(Under Panel Camera), polysilicon and oxide TFT are used in combination, and TFTs of various sizes must be used at the same time. So, first, it is attempted to reduce the IV measurement points and improve them with the known interpolation method. However, there is a limit to accurate prediction due to the non‐linear characteristics of TFT and noise measurement in the off region of TFT. In this paper, we applied an asymmetric neural network structure to overcome this limitation of reduction decoding. To do this, we developed an asymmetric autoencoder to decode or reconstruct TFT IV data from small sampled IV measurements (14~35%). Then, to overcome the error estimation of generating many errors in the surrounding interpolation because noise is included in the TFT off region, a function of partially removing noise only in the off region is introduced. In addition, to overcome the performance decrease problem due to the minimal amount of abnormal data, which should be accurately predicted in a situation close to abnormal, generative models were applied to overcome it. The IV information to be generated was encoded using a pretrained large language model to reduce the dimensionality and converted into text, which was then trained on the distillate GPT‐2 model and used as a generative model. In the general interpolation method, the performance decreases as the number of measurement samples are reduced; the method proposed in this paper maintains its performance even if the sampling is under 20%. This means that it is adequate to restore only a small portion of information using the latent space information that has learned the TFT IV saturation and linear mode characteristics. This is applied to mass production by increasing the measurement capability without additional equipment investment.