The limits of Moore’s Law are insight. Moore’s Law is an index used by Gordon Moore, the co-founder of Intel Corporation, to predict the evolution of semiconductor technology, saying, “The number of transistors on a microchip doubles every 18 months.” Over the last 50 years, semiconductors have continued to miniaturize and become integrated as predicted, shaping computing progress. The recent rise of artificial intelligence (AI) may also depend considerably on the benefits of improving computer performance. In recent years, however, the miniaturization and integration of semiconductors have shown signs of approaching their physical and energy efficiency limits. Modern advanced semiconductor processors are manufactured with 5 nm process technology. Although integrated technology will continue advancing, exponential performance improvement is becoming hard to expect. Power consumption emerges as a serious issue. In fact, energy saving through the technological innovation of semiconductors has remained stagnant for about a decade. The Ministry of Economy, Trade and Industry projects that information-communication technology equipment will account for about 60% of the total power consumption by 2050. For AI to further evolve in this situation, there is a need to develop computers based on an entirely new concept. Particularly, the innovation of energy saving technologies achieving ultra-low power consumption and standby power reduction during operations is an urgent task.

This project aims to develop next-generation computers replacing conventional machines, that employ a new mechanism not using electron charges for transmitting and processing information. We will particularly focus on the “fluctuations” of living organisms and develop next-generation ultra-low power consumption computers based on the design guidelines for actively making use of noise, considered a “nuisance” in the past.

Existing electronic devices have used electric charge, a property of electrons and a source of electricity, to transmit and control information. Controlling the flow of electric charge (current), the “electronics” technology has supported the evolution of electronic devices. However, as Moore’s Law approaches its limits, attention is turning to another property of electrons, the intrinsic spin of the electron serving as the source of magnetism. Spin holds the potential to solve computer energy problems. Accordingly, research and development are actively promoted as the study of intrinsic spin, or “spintronics/magnonics,” worldwide.
In electronics (the study of electric current), the transmission and control of information involve the actual transportation of electrons. Heat generation is thus unavoidable combined with large energy loss. On the other hand, spin angular momentum transmission and control apply the phenomenon called spin wave. The angular momentum of electrons (a quantity representing the momentum of rotational motion) is transmitted as a wave, allowing information to be transmitted and controlled without transporting electrons. Consequently, zero heat is generated. Based on this property, spin is expected to be applied to next-generation ultra-low power consumption computers that can transmit and control information without heat loss. Our research team has already produced the world’s first high-temperature glass material for new spin wave elements. By maintaining and accelerating the advantage, we aim to solve the problem of power consumption of conventional computers, and develop highly reliable computers with innovative low power consumption.
Specifically, our research and development focus on spin wave elements using the magnetic garnet, known as a jewel, to develop brain-type computers (neuromorphic computing) mimicking the behavior of the human brain. We hope to apply the results of this project to quantum computing and reservoir computing by spin phase interference.

A characteristic approach of this project is the research and development of spin wave elements by applying stochastic resonance. Stochastic resonance is a phenomenon in which when an appropriate amount of noise is added to a weak signal that cannot be detected under normal circumstances, the response to the signal improves at a certain probability, and the signal is detected with high sensitivity. In fact, many creatures are known to make use of such stochastic resonance in their sensory organs and neurotransmission. For example, a fish experiment found that sharks of a certain species transmit a weak electric current as noise to find and prey on plankton farther away. Many living organisms, including human beings, naturally possess and skillfully utilize the phenomenon.
In conventional engineering, noise is considered bad and every effort is made to eliminate it. However, this project applies the reversed concept of making use of the nuisance (noise) as something useful. Our research team takes noises such as fluctuations and thermal fluctuations as energy sources in the environment and aims to design and develop electronic devices that make good use of them.
Specifically, we aim to develop a brain-type (neuromorphic) element that can function at room temperature, induce spin fluctuations on magnetic thin films made of garnet (magnetic optical material), and electrically detect spin angular momentum (spin wave) by spin-orbit interaction. These achievements will be applicable to reservoir computing by spin wave calculation, spiking neurons, etc.

Machine learning is based on a learning method called neural networks that mimic the functions of the human brain as its fundamental technology. Presently, the principal artificial neural network technology is realized on software. However, in existing machines, the memory for storing programs and the CPU executing them are separated due to their structure, and heat loss is enormous in large-scale and high-speed operations with the current integrated circuit technology, resulting in the need to disregard energy efficiency.
Brain-type elements operating at room temperature, the goal of this project, can establish functions equivalent to high-density and flexible interneuron connections without wiring by applying the spin wave phenomenon and can be mounted as a chip, making them suitable for integration. We hope that this project will lead to ultra-low power consumption and high-performance terminal devices indispensable for driving the future AI society.