A Monolithic Stochastic Computing Architecture for Energy and Area Efficient Arithmetic

As the energy and hardware investments necessary for conventional high-precision digital computing continues to explode in the emerging era of artificial intelligence, deep learning, and Big-data [1-4], a change in paradigm that can trade precision for energy and resource efficiency is being sought for many computing applications. Stochastic computing (SC) is an attractive alternative since unlike digital computers, which require many logic gates and a high transistor volume to perform basic arithmetic operations such as addition, subtraction, multiplication, sorting etc., SC can implement the same using simple logic gates [5, 6]. While it is possible to accelerate SC using traditional silicon complementary metal oxide semiconductor (CMOS) [7, 8] technology, the need for extensive hardware investment to generate stochastic bits (s-bit), the fundamental computing primitive for SC, makes it less attractive. Memristor [9-11] and spin-based devices [12-15] offer natural randomness but depend on hybrid designs involving CMOS peripherals for accelerating SC, which increases area and energy burden. Here we overcome the limitations of existing and emerging technologies and experimentally demonstrate a standalone SC architecture embedded in memory based on two-dimensional (2D) memtransistors. Our monolithic and non-von Neumann SC architecture consumes a miniscule amount of energy < 1 nano Joules for s-bit generation and to perform arithmetic operations and occupy small hardware footprint highlighting the benefits of SC.

Neuromorphic Chip Integrated with an LSI 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 an 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.

Engineering metal oxide semiconductor nanostructures for enhanced charge transfer: fundamentals and emerging SERS applications

Fundamentals of doping engineering strategies of metal oxide semiconductors and various charge transfer processes for emerging SERS applications are discussed.

Inherent characteristics of ultra-photosensitive Al/Cu–CeO2/p-Si metal oxide semiconductor diodes

An ultrahigh photosensitive diode was developed using a Cu-doped CeO2 thin film through spray pyrolysis processing, which has made a unique contribution in the field of optoelectronic device fabrication process.

Formaldehyde Gas Sensor with Extremely High Response Employing Cobalt-Doped SnO2 Ultrafine Nanoparticles

Formaldehyde is a common carcinogen in daily life and harmful to health. The detection of formaldehyde by a metal oxide semiconductor gas sensor is an important research direction. In this...