Trap densities in amorphous-$\mathrm{In}\mathrm{Ga}\mathrm{Zn}{\mathrm{O}}_4$ $\left(\alpha \text{-}\mathrm{IGZO}\right)$ are extracted directly from the capacitance-voltage characteristics of thin-film transistors at low frequencies. It is found that the trap densities are flat in the energy gap, and are $1.7\times {10}^{16}{\mathrm{cm}}^{-3}{\mathrm{eV}}^{-1}$ in the deep energy far from the conduction band edge $\left(E_c\right)$ , but become larger near $E_c$ . Moreover, postannealing reduces the trap density near $E_c$ , which is associated with the reduction of the hysteresis in the current-voltage characteristics. The annealed $\alpha \text{-}\mathrm{IGZO}$ does not have a Gaussian-type state and has fewer tail states than amorphous Si.

Amorphous In–Ga–Zn–O ($\alpha$ -IGZO) is expected for thin-film transistors (TFTs) in next-generation flat-panel displays but its intrinsic properties are not understood well and different mobility models have been applied to different films. This letter reports that a universal mobility model is obtained using a field-effect technique and capacitance-voltage method. Electrical characteristics of $\alpha$ -IGZO TFTs subjected to different annealing are reproduced using the mobility model and different trap densities. The present achievement will be a necessary basis to establish device and circuit simulators for $\alpha$ -IGZO-based electronic applications.

Artificial intelligence is a promising concept in modern and future societies. Presently, software programs are used but with a bulky computer size and large power consumption. Conversely, hardware systems named neuromorphic systems are suggested, with a compact computer size and low power consumption. An important factor is the number of processing elements that can be integrated. In the present study, three decisive technologies are proposed: (1) amorphous metal oxide semiconductor thin films, one of which, Ga–Sn–O (GTO) thin film, is used. GTO thin film does not contain rare metals and can be deposited by a simple process at room temperature. Here, oxygen-poor and oxygen-rich layers are stacked. GTO memristors are formed at cross points in a crossbar array; (2) analog memristor, in which, continuous and infinite information can be memorized in a single device. Here, the electrical conductance gradually changes when a voltage is applied to the GTO memristor. This is the effect of the drift and diffusion of the oxygen vacancies (Vo); and (3) autonomous local learning, i.e., extra control circuits are not required since a single device autonomously modifies its own electrical characteristic. Finally, a neuromorphic system is assembled using the abovementioned three technologies. The function of the letter recognition is confirmed, which can be regarded as an associative memory, a typical artificial intelligence application.

We have found a memristive characteristic of an α-GTO thin-film device. The α-GTO thin-film layer is deposited using radio-frequency (RF) magnetron sputtering at room temperature and sandwiched between the Al top and bottom electrodes. It is found that the hysteresis loop of the flowing current (I) and applied voltage (V) characteristic becomes larger and stable after the one hundredth cycle. The electrical resistances for the high-resistance state (HRS) and low-resistance state (LRS) are clearly different and relatively stable. Based on various analysis, it is suggested that the memristive characteristic is due to the chemical reaction between the SnO₂ and SnO blocked by AlO_x on the Al bottom electrode. It is marvelous that the memristive characteristic can be realized by such common materials, simple structures, and easy fabrication.

First, a p/i/n thin-film photodiode (TFPD) is evaluated, and it is found that the photoinduced current (I_photo) is relatively large. Next, a p/n TFPD is evaluated, and it is found that the I_photo is independent of the applied voltage (V_apply). However, it is difficult to simultaneously achieve a large and independent I_photo. Therefore, a p/i/n thin-film phototransistor (TFPT) is developed, and it is found that the I_photo can be both relatively large and independent of the V_apply by optimizing the gate voltage. These characteristics are obtained because the depletion layer is formed in the entire intrinsic region and the electric field is always high. It is expected that these characteristics are preferable for some types of photosensor application such as artificial retina.