Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST), Takayama, Ikoma, 630-0192, Japan.
Graduate School of Science and Technology, Ryukoku University, Seta, Otsu, 520-2194, Japan.
Sci Rep. 2022 Mar 30;12(1):5359. doi: 10.1038/s41598-022-09443-y.
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.
人工智能在未来社会中具有广阔的应用前景，神经网络是一种典型的技术，具有自组织、自学习、并行分布式计算和容错等优点，但它们的尺寸和功耗较大。神经形态系统是从硬件层面仿生的系统，具有与活体大脑相同的优点，特别是尺寸紧凑、功耗低、运行稳健，但一些知名的系统是非优化系统，因此仅部分获得了上述优势，例如，机器学习在其他地方进行处理，以下载固定参数。为了解决这些问题，我们从各个角度研究神经形态系统。在这项研究中，我们开发了一种集成大规模集成电路（LSI）和非晶金属氧化物半导体（AOS）薄膜突触器件的神经形态芯片。神经元元件是数字电路，在 LSI 中制造，而突触器件是模拟器件，由 AOS 薄膜制成，并直接集成在 LSI 上。这是世界上第一个集成不同功能半导体的神经元元件和突触器件的混合芯片，利用了局部自主学习，这成为可能，因为 AOS 薄膜可以在不进行热处理的情况下沉积，并且不会对底层造成损害，同时具有神经形态系统的所有优势。
