A 3.2-inch flexible color display, with a resolution of 50 ppi and, composed of bottom-emission multiphoton organic light-emitting diodes (OLEDs) and inkjet-printed organic thin-film transistors (OTFTs) with a bottom-gate/bottom-contact structure on a color filter, was developed. The device could successfully display color videos while being bent, and achieved a maximum luminance of 125 cd/m 2 with white light emission. The gate dielectrics of the OTFTs used on the backplane were bilayers of cardo polymer and Parylene, and the material used for the organic semiconductors was dithieno[2,3-d;2',3'-d']benzo[1,2-b;4,5-b']dithiophene blended with polystyrene in tetralin solvent, which was coated using inkjet printing to sufficiently fill the banks composed of a fluorine-based polymer. OTFTs with a channel length of 5 μm were created using the above process, and the structure achieved a high mobility of 1.2 cm2 /(V · s), making it suitable for flexible color OLED displays. The mobility was about three times as high as that obtained using solution shearing methods.

An active matrix backplane based on solution-processed organic thin-film transistors (OTFTs) has been developed for flexible displays having multiphoton organic light-emitting diodes (OLEDs). The OTFT device has a bottom-gate/bottom-contact type structure consisting of a polyethylene naphthalate-based flexible substrate covered with patterned gate electrodes and a gate dielectric of UV-cured cardo-polymer. Teflon AF was used for a hydrophobic bank structure that defines active regions of the OTFTs, and the organic semiconductor was coated by solution shearing. The OTFT backplane fabricated here had 384 × 128 pixels at a resolution of 300 dpi; images and video were successfully displayed on the resulting flexible multiphoton-emission OLED display.

Seung-Il Cho;, Takumi Negishi, Minami Tsuchiya, Muneki Yasuda and Michio Yokoyama. IJFIS 2018;18:229-36. https://doi.org/

A deep Neural Network (DNN) is used to estimate the sleep onset and awaking time of a subject with physiological data during sleep, in order to control the sleeping environment based on sleep phase. The results of the estimation from 40 minutes before the actual sleeping time show approximately 2.8 minutes mean error. Regarding awaking time, the results of the estimation from 120 minutes before show approximately 9.9 minutes mean error. Furthermore, the results of estimation in case of the range from 60 to 20 minutes before the actual awakening time show approximately 7.5 minutes mean error. The proposed DNN estimation is found to be effective for control of a comfortable awaking environment.

Minami Tsuchiya, Seung-Il Cho, Atsushi Tanaka, Muneki Yasuda, Tomochika Harada, and Michio Yokoyama. IJFIS 2020;20:87-95. https://doi.org/10.5391/IJFIS.2020.20.2.87