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 amorphous Ga-Sn-O (α-GTO) thin-film device with double layers of different oxygen density. The double layers are deposited using radio frequency (RF) magnetron sputtering, whose gas for the lower layer contains less oxygen, whereas that for the upper layer contains more oxygen, and it is assumed that the former contains more oxygen vacancies, whereas the latter contains fewer vacancies. The characteristic is explained by drift of oxygen and is stable without forming operation because additional structures such as filament are unnecessary. The fabrication is easy because the double layers are successively deposited simply by changing the oxygen ratio in the chamber.
Artificial intelligences are indispensable in future societies, and neural networks are representative that mimic biological brains. However, the conventional ones are complicated software on high-spec hardware, the machine size is bulky, and power consumption is huge. Neuromorphic systems are practical solutions composed solely of optimized hardware. Therefore, we are investigating neuromorphic systems with amorphous met-al-oxide-semiconductor thin-film devices as synapse elements and proposing modified Hebbian learning done locally without extra control circuits. As a result, the conductance deterioration can be utilized as synaptic plasticity. It is expected that the neuromorphic systems are three-dimensional-integrated systems, the size can be compact, power can be low, and all functions of biological brains are realized. In this study, we have developed neuromorphic systems with crosspoint-type amorphous Ga-Sn-O thin-film devices as self-plastic synapse elements, and fundamental operations are confirmed. First, crosspoint-type devices are fabricated, and it is found that the electric current gradually decreases along the bias time. Next, a neuromorphic system is actually implemented using a field-programmable-gate-array chip and crosspoint-type devices, and it is confirmed that a function of letter recognition is obtained after learning process. Once the fundamental operations are confirmed, more advanced functions will be obtained by increasing the device and circuit scales.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.