Kompozit Tabanlı Alan Etkili Transistör Tasarımı ve Dokunsal Algılama Uygulaması

Abstract

Studies on the design and production of flexible field-effect transistors (FET) using nanocomposite structures were carried out within the scope of this thesis. The design, on which field effects occur on it, is similar to thin-film transistors (TFT) in topology and had been used as a functionally flexible tactile sensor. In today's technologies, in direct proportion to the increasing interest in wearable systems, the tendency towards flexible structures is increasing rapidly. Transistors, which are accepted as the basic unit of electronic devices, have their share of this development. The tendency to create field effect transistors using metal oxide structures has increased in this process. While the electrical values change with the pressure, which is a mechanical input, from the sensor outputs, the channel width on the TFT dielectric changes, and accordingly touch-current relation is observed. The mixing ratios and conductivity information of the composites were prepared before the experiments were carried out in the simulation environment. Material selection and comparison have been made for the use of field effect transistors, which will be created within the scope of the thesis, with low cost and sustainable sources. In addition, homemade systems were preferred for fast and widespread production, and it was desired to increase accessibility to production. Many other industrial production steps are explained and their benefits are mentioned. In addition, parameter studies were carried out to optimize the channel width and the number of FETs per unit volume. In order to compare the gate electrodes of these FETs, graphene and copper electrodes were preferred. Characterization of graphene electrodes for post-production validation was achieved by Raman spectroscopy. With the created graphene electrodes, a two times better capacitive effect was achieved. The resulting semiconductor layers are expressed as P or N-type by the hot-point probe method. The distribution of additive particles was interpreted with the help of a scanning electron microscope and atomic force microscope. Then electrical characterizations were carried out. Machine learning algorithms were used to obtain meaningful data from the outputs of flexible sensing units. In the design produced with a bottom gate and bottom contact, accuracy values of over 95% have been reached in the classification of the data obtained as a result of the contact of triangle, star, square, and circle patterns on the sensor matrix.