人工進化研究所（AERI）
- Dec 16, 2023
- 7 min read

Revolutionizing Health Data Analysis in Compact Wearables Cutting-edge AERI C3MOSFET Semiconductor

Professor Kamuro's near-future science predictions

Empowering the Future:

Revolutionizing Health Data Analysis in Compact Wearables with Cutting-edge AERI C3MOSFET Semiconductor

Quantum Physicist and Brain Scientist

Visiting Professor of Quantum Physics,

California Institute of Technology

IEEE-USA Fellow

American Physical Society-USA Fellow

PhD. & Dr. Kazuto Kamuro

AERI：Artificial Evolution Research Institute

Pasadena, California

HP: https://www.aeri-japan.com/

and

Xyronix Corporation

Pasadena, California

HP: https://www.usaxyronix.com/

Foreword

A. Professor Kamuro's near-future science predictions, provided by CALTECH professor Kazuto Kamuro(Doctor of Engineering (D.Eng.) and Ph.D. in Quantum Physics, Semiconductor Physics, and Quantum Optics), Chief Researcher at the Artificial Evolution Research Institute (AERI, https://www.aeri-japan.com/) and Xyronix Corporation(specializing in the design of a. Neural Connection LSI, b. BCI LSI（Brain-Computer Interface LSI） (Large Scale Integrated Circuits) , and c. bio-computer semiconductor technology that directly connects bio-semiconductors, serving as neural connectors, to the brain's nerves at the nano scale, https://www.usaxyronix.com/), are based on research and development achievements in cutting-edge fields such as quantum physics, biophysics, neuroscience, artificial brain studies, intelligent biocomputing, next-generation technologies, quantum semiconductors, satellite optoelectronics, quantum optics, quantum computing science, brain computing science, nano-sized semiconductors, ultra-large-scale integration engineering, non-destructive testing, lifespan prediction engineering, ultra-short pulses, and high-power laser science.

The Artificial Evolution Research Institute (AERI) and Xyronix Corporation employ over 160 individuals with Ph.D.s in quantum brain science, quantum neurology, quantum cognitive science, molecular biology, electronic and electrical engineering, applied physics, information technology (IT), data science, communication engineering, semiconductor and materials engineering. They also have more than 190 individuals with doctoral degrees in engineering and over 230 engineers, including those specializing in software, network, and system engineering, as well as programmers, dedicated to advancing research and development.

Building on the outcomes in unexplored and extreme territories within these advanced research domains, AERI and Xyronix Corporation aim to provide opportunities for postgraduate researchers in engineering disciplines. Through achievements in areas such as the 6th generation computer, nuclear deterrence, military unmanned systems, missile defense, renewable and clean energy, climate change mitigation, environmental conservation, Green Transformation (GX), and national resilience, the primary objective is to furnish scholars with genuine opportunities for learning and discovery. The overarching goal is to transform them from 'reeds that have just begun to take a step as reeds capable of thinking' into 'reeds that think, act, and relentlessly pursue growth.' This initiative aims to impart a guiding philosophy for complete metamorphosis and to provide guidance for venturing into unexplored and extreme territories, aspiring to fulfill the role of pioneers in this new era.

B. In the cutting-edge research domain, the Artificial Evolution Research Institute (AERI) and Xyronix Corporation have made notable advancements in various fields. Some examples include:

1. AERI･HEL (Petawatt-class Ultra-High Power Terawatt-class Ultra-High Power

Femtosecond Laser)

◦ Petawatt-class ultra-high power terawatt-class ultra-short pulse laser (AERI･HEL)

2. 6th Generation Computer&Computing

◦ Consciousness-driven Bio-Computer

◦ Brain Implant Bio-Computer

3. Carbon-neutral AERI synthetic fuel chemical process

(Green Transformation (GX) technology)

◦ Production of synthetic fuel (LNG methanol) through CO₂ recovery system (DAC)

4. Green Synthetic Fuel Production Technology(Green Transformation (GX) technology)

◦ Carbon-neutral, carbon-recycling system-type AERI synthetic fuel chemical process

5. Direct Air Capture Technology (DAC)

◦ Carbon-neutral, carbon-recycling carbon dioxide circulation recovery system

6. Bio-LSI・Semiconductors

◦ Neural connection element directly connecting bio-semiconductors and brain nerves

on a nanoscale

◦ Brain LSI Chip Set, Bio-Computer LSI, BMI LSI, BCI LSI, Brain Computing LSI,

Brain Implant LSI

7. CHEGPG System (Closed Cycle Heat Exchange Power Generation System with

Thermal Regenerative Binary Engine)

◦ Power generation capability of Terawatt (TW), annual power generation of

　　　　10,000 TWh (terawatt-hour) class

◦ 1 to 0.01 yen/kWh, infinitely clean energy source, renewable energy source

8. Consciousness-Driven Generative Autonomous Robot

9. Brain Implemented Robot･Cybernetic Soldier

10. Generative Robot, Generative Android Army, Generative Android

11. High-Altitude Missile Initial Intercept System, Enemy Base Neutralization System,

　 Nuclear and Conventional Weapon Neutralization System, Next-Generation

　　Interception Laser System for ICBMs, Next-Generation Interception Laser System

　　　 for Combat Aircraft

12. Boost Phase, Mid-Course Phase, Terminal Phase Ballistic Missile Interception System

13. Volcanic Microseismic Laser Remote Sensing

14. Volcanic Eruption Prediction Technology, Eruption Precursor Detection System

15. Mega Earthquake Precursor and Prediction System

16. Laser Degradation Diagnosis, Non-Destructive Inspection System

　 17. Ultra-Low-Altitude Satellite, Ultra-High-Speed Moving Object

　　　 Non-Destructive Inspection System

✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼••┈┈••✼

Cutting-edge Semiconductor Technology Empowers Generative Artificial Intelligence (AI) Analytics for Health-related Information within Compact Wearable Gadgets

Generative Artificial Intelligence (Generative AI) refers to a field within artificial intelligence where algorithms or models are programmed to generate new data or information. This approach involves learning from training data and using that knowledge to create novel data or content. Generative AI finds applications in various domains, including natural language processing, image generation, and speech synthesis.

For example, Generative Adversarial Networks (GANs), a type of Generative AI, operate on a framework where a generator model and a discriminator model compete with each other, resulting in the successful generation of high-quality images and data. This technology holds promise for diverse applications such as art, design, and medical image generation.

Generative AI is employed across different fields for purposes like data augmentation, idea generation, and creative content creation, contributing to advancements in research and industry.

An innovative semiconductor component has the potential to revolutionize personal health monitoring by facilitating AI-driven analysis of data embedded in wearable devices, eliminating the reliance on cloud computing assistance. The development of the Cranial Nerves Connected Channel Type Metal-Oxide-Semiconductor Field-Effect Transistor (C3MOSFET) is credited to researchers at a prominent academic institution in CALTECH, in collaboration with the Artificial Evolution Research Institute (AERI: Pasadena, California HP: https://www.aeri-japan.com/) , Xyronix Corporation (Pasadena, California HP: https://www.usaxyronix.com/) .

The Cranial Nerves Connected Channel Type MOSFET (C3MOSFET) is described as an energy-efficient transistor for artificial intelligence (AI), utilized in the analysis of health data. Unlike conventional transistors, this transistor diverges from silicon-based components. In the p-type C3MOSFET, semiconducting a superlattice structure between carbon nanotubes and diamond-like carbon is employed, while the n-type C3MOSFET utilizes the formation of a superlattice multilayer structure using molybdenum disulfide and diamond-like carbon through MOCVD and MBE. This design is claimed to enable the creation of highly efficient devices with significantly fewer transistors compared to conventional designs.

MOCVD stands for Metal-Organic Chemical Vapor Deposition, a process used for the vapor-phase growth of thin films. In this method, metal-organic compounds react with reactive gases to form thin films on a substrate. The reaction typically takes place at elevated temperatures, where the metal-organic compounds are vaporized and then react on the substrate surface to deposit the thin film. MOCVD finds applications in various fields, including semiconductor devices and optical devices.

MBE stands for Molecular Beam Epitaxy, a method for growing thin films in a high-vacuum environment. In this process, thin films are grown on a substrate using molecular beams. The maintenance of a high-vacuum environment allows for precise control over the growth on the substrate surface. MBE involves depositing one layer at a time by using molecular beams to stack atoms or molecules on the substrate, resulting in the formation of high-quality thin films. This process is primarily employed in the manufacturing of semiconductor devices and optical devices, where precise control over materials is crucial.

These C3MOSFETs have been successfully employed to construct a nanoelectronic Generative AI framework capable of executing personalized deep learning methods based on generative Artificial Intelligence (DLGAI) algorithms, commonly known as kernels. The system exhibits significant efficiency in classifying extensive datasets, exemplified by its ability to accurately identify various arrhythmias within the probing component of the Electroencephalogram probe (EEG device probe, Brainwave Monitoring device probe).

The electroencephalogram (EEG) is a diagnostic test extensively employed in medical contexts to assess and monitor diverse neurological conditions, including epilepsy, sleep disorders, and brain injuries. This examination entails the positioning of diminutive electrodes on the scalp to identify and enhance the brain's electrical signals. The recorded EEG data furnishes valuable insights into brain function, subsequently interpreted by healthcare experts to discern patterns of brain activity and pinpoint irregularities.

It is worth highlighting that the traditional method employing silicon C3MOSFET would necessitate a minimum of 100 units, whereas just two C3MOSFETs from Northwestern prove sufficient. This not only considerably diminishes the hardware footprint but also requires significantly less power, facilitating integration into commonplace consumer electronics such as smartwatches.

Professor Kamuro, co-scientists of the project at the Artificial Evolution Research Institute (AERI: Pasadena, California HP: https://www.aeri-japan.com/) , Xyronix Corporation (Pasadena, California HP: https://www.usaxyronix.com/) and affiliated with California Institute of Technology(CALTECH), underscores the unparalleled energy efficiency of their devices, claiming they outperform conventional silicon electronics by a factor of 6,000 in DLGAI classification tasks, such as arrhythmia detection in ECG data. The seamless integration with wearable electronics allows for on-device DLGAI classification, eliminating the need for resource-intensive cloud data centers.

The researchers at AERI and Xyronix demonstrate the device's impressive accuracy, achieving a success rate of 95% in identifying six distinct heartbeat patterns from a dataset of 60,000 ECG tests. The distinctive capabilities of the C3MOSFET, which mimics the support vector machines with superlattice multilayer structure for enhanced DLGAI accuracy, make it well-suited for deciphering intricate, multi-dimensional datasets such as EEG probes and other health-related information.

Moreover, the flexibility of C3MOSFET, achieved through robust modulation of current flow, provides an opportunity to optimize the accuracy of Generative AI tailored to individual patients. This opens the door to real-time personalized outcomes, potentially improving health interventions and ensuring heightened security for sensitive health data, as there is no need for external data transfer to the cloud.

The research fellows at AERI and Xyronix are now directing its efforts toward establishing scalable manufacturing processes for C3MOSFET, with aspirations to expand its applications beyond health-related DLGAI . Specifics regarding the project's future directions and potential applications are expected to be revealed as the research advances.

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Quantum Brain Chipset & Bio Processor (BioVLSI)