Neuromorphic System Using Memcapacitors and Autonomous Local Learning.

Artificial intelligence is used for various applications and is promising as an indispensable infrastructure in future societies. Neural networks are representative technologies that imitate human brains and exhibit various advantages. However, the size is bulky, the power is huge, and some advantages are not demonstrated because they are executed on Neumann-type computers. Neuromorphic systems are biomimetic systems from the hardware level to implement neuron and synapse elements, and the size is compact, the power is low, and the operation is robust. However, because the conventional ones are not composed of fully optimized hardware, the power is not yet minimal, and extra control circuits must be used. In this article, we developed a neuromorphic system using memcapacitors and autonomous local learning. By using memcapacitors, the power can be minimal, and by using autonomous local learning, the control circuits to handle the synapse elements can be deleted. First, the memcapacitors are completed in a cross-bar array, where the ferroelectric layers are sandwiched between the horizontal and perpendicular electrodes. The polarization and capacitance exhibit hysteresis due to the dielectric polarization. Next, autonomous local learning is introduced as follows. During the training phase, associative patterns to be memorized are directly sent, relatively high voltages are applied, and dielectric polarizations are induced. During the operation phase, relatively low voltages are applied, and input signals are weighted with the capacitances of the memcapacitors, summed, and transferred as the output signals. Finally, the experimental system is set up, and the experimental results are acquired. The memorized patterns during the training phase, distorted patterns as the input signals during the operation phase, and retrieved patterns as the output signals in the operation phase are shown. Researchers found that the retrieved patterns are completely the same as the memorized patterns. This means that the neuromorphic system works as an associative memory.

Bisection Neural Network Toward Reconfigurable Hardware Implementation.

A hardware-friendly bisection neural network (BNN) topology is proposed in this work for approximately implementing massive pieces of complex functions in arbitrary on-chip configurations. Instead of the conventional reconfigurable fully connected neural network (FC-NN) circuit topology, the proposed hardware-friendly topology performs NN behaviors in a bisection structure, in which each neuron includes two constant synapse connections for both inputs and outputs. Compared with the FC-NN one, the reconfiguration of the BNN circuit topology eliminates the remarkable amount of dummy synapse connections in hardware. As the main target application, this work aims at building a general-purpose BNN circuit topology that offers a great amount of NN regressions. To achieve this target, we prove that the NN behaviors of the FC-NN circuit topologies can be migrated to the BNN circuit topologies equivalently. We introduce two approaches including the refining training algorithm and the inverted-pyramidal strategy to further reduce the number of neurons and synapses. Finally, we conduct the inaccuracy tolerance analysis to suggest the guideline for ultra-efficient hardware implementations. Compared with the state-of-the-art FC-NN circuit topology-based TrueNorth baseline, the proposed design can achieve 17.8-22.2 × hardware reduction and less than 1% inaccuracy.

Neuromorphic chip integrated with a large-scale integration circuit and amorphous-metal-oxide semiconductor thin-film synapse devices.

Artificial intelligences are promising in future societies, and neural networks are typical technologies with the advantages such as self-organization, self-learning, parallel distributed computing, and fault tolerance, but their size and power consumption are large. Neuromorphic systems are biomimetic systems from the hardware level, with the same advantages as living brains, especially compact size, low power, and robust operation, but some well-known ones are non-optimized systems, so the above benefits are only partially gained, for example, machine learning is processed elsewhere to download fixed parameters. To solve these problems, we are researching neuromorphic systems from various viewpoints. In this study, a neuromorphic chip integrated with a large-scale integration circuit (LSI) and amorphous-metal-oxide semiconductor (AOS) thin-film synapse devices has been developed. The neuron elements are digital circuit, which are made in an LSI, and the synapse devices are analog devices, which are made of the AOS thin film and directly integrated on the LSI. This is the world's first hybrid chip where neuron elements and synapse devices of different functional semiconductors are integrated, and local autonomous learning is utilized, which becomes possible because the AOS thin film can be deposited without heat treatment and there is no damage to the underneath layer, and has all advantages of neuromorphic systems.

Amorphous metal oxide semiconductor thin film, analog memristor, and autonomous local learning for neuromorphic systems.

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.

Memristive characteristic of an amorphous Ga-Sn-O thin-film device.

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 AlOx on the Al bottom electrode. It is marvelous that the memristive characteristic can be realized by such common materials, simple structures, and easy fabrication.

Studies on the mechanism of printing film-coated tablets containing titanium dioxide in the film by using UV laser irradiation.

AIM: The purpose of this article is to study a detailed mechanism of printing when film-coated tablets were irradiated by UV laser at a wavelength of 355 nm. METHODS: Hydroxypropylmethylcellulose (HPMC) film containing titanium dioxide (TiO(2)) and the film not containing TiO(2) and TiO(2) powder were lirradiated by the UV laser and estimated by the morphological observation by zoom stereo microscope, thermogravimetric analysis (TGA), total color difference (dE), X-ray powder diffraction (XRD), and dispersive Raman microscopy. RESULTS: In the case of the film containing TiO(2), the film showed a visible change in its color from white to gray by the UV laser irradiation. By zoom stereo microscope, it was found that the entire UV laser-irradiated area was not grayed uniformly, but many black particles, whose diameter was about 2 microm, were observed on the film. When TiO(2) powder was irradiated by the UV laser, a visible change in its color from white to gray was observed similar to the case of the film containing TiO(2). There were many black particles locally in the UV laser-treated TiO(2) powder by the morphological observation, and these black particles, agglomerates of the grayed oxygen-defected TiO(2), were associated with the visible change of the TiO(2). CONCLUSION: It was found that the film-coated tablets were printed utilizing the formation of the black particles by the agglomeration of the grayed oxygen-defected TiO(2) by the UV laser irradiation.