Memory for Artificial Intelligence Patent Landscape Analysis Report 2023: IBM and Samsung have a Leading Intellectual Property (IP) Position

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Jan 31, 2023, 06:20 ET

DUBLIN, Jan. 31, 2023 /PRNewswire/ -- The "Memory for Artificial Intelligence Patent Landscape Analysis 2023" report has been added to ResearchAndMarkets.com's offering.

Patent landscape analysis is a powerful tool for understanding the competitive and technological environment. It makes it possible to identify new players in emerging industries long before they enter the market while providing a better understanding of their expertise and know-how of a specific technology.

In recent years, neuromorphic computing has emerged as a promising technology in the post-Moore's law era. Neuromorphic computing systems are highly connected and parallel and consume relatively low power and processes in memory.

Artificial neurons and synapses that mimic biological ones are needed to implement such a system on hardware. Both must be power-efficient, scalable, and capable of implementing relevant learning rules to facilitate large-scale neuromorphic functions.

To this end, numerous efforts have been made over the last few years to create artificial neurons and synapses using emerging memories, including magnetoresistive random-access memory (MRAM), phase-change memory (PCM), resistive random-access memory (ReRAM or memristors), conductive bridging random-access memory (CB-RAM), ferroelectric random-access memory (FeRAM), ferroelectric field-effect transistor (FeFET), synaptic transistors, and others.

In this context, the publisher is releasing a new report that aims to provide a comprehensive view of the patent landscape related to memory technologies for artificial intelligence/machine learning (AI/ML) applications, from materials and devices to the systems and methods that use them, categorized into memory technologies (ReRAM, PCM, MRAM, FRAM/FeFET, Flash, DRAM, SRAM, etc.).

Overall, patenting activity (patent filings) reflects the level of R&D investment made by a country or player in a specific technology while providing clues as to the technology readiness level reached by the main IP players. What's more, the technology coverage and the geographical coverage of the patent portfolios are closely related to the business strategy of IP players.

A diversified ecosystem of semiconductor companies

A mix of IC and memory players, universities, and R&D centers are competing for innovation at all stages of the R&D ladder. Industrial companies' patenting activity took off in 2015 after R&D efforts were focused on research and fundamental physics knowledge. American and Korean industrials and Chinese and Korean universities mainly hold patents.

Top patent assignees are well-established semiconductor companies, and IBM and Samsung have a leading IP position. New players such as Applied Materials, TSMC, GlobalFoundries, TetraMem, and ICLeague are entering the game, and their intellectual property (IP) may become important in the coming years.

Patent segmentation

The patents have been categorized according to the memory technologies developed for neuromorphic computation: resistance-based emerging memory technology (ReRAM, PCM, MRAM), polarization-based emerging memory technology (FRAM/FeFET), traditional memory technologies (Flash, DRAM, SRAM), other memory technologies (synaptic ionic transistor, devices based on 2D or 1D materials, hybrid materials, perovskites, nanoparticles, quantum physics, organic materials, skyrmions, etc.), and not specific (type of memory technology not specified).

All emerging memories are currently under investigation, with an upward trend for protecting FRAM in recent years. RRAM is the technology with the most inventions and the most significant number of enforceable patents worldwide.

IP profiles: focus on the top IP players' patent portfolios

The IP report includes the IP profile of eight key IP players: IBM, Samsung, Applied Materials, TDK, SK hynix, Macronix, HP, and TetraMem.

Each player's patent portfolio related to memory technologies for AI applications is analyzed to provide an overview of its strengths, potential for reinforcement, level of IP activity, main IP collaborations, recent patenting activity, and inventions that stand out.

Useful Excel patent database

This report includes an extensive Excel database with the 1,300+ patent families (inventions) analyzed in this study. This useful patent database allows for multicriteria searches and includes patent publication numbers, hyperlinks to an updated online database (original documents, legal status, etc.), priority date, title, abstract, patent assignees, patent's current legal status, and nine segments (RRAM, PCM, MRAM, FeRAM/FeFET, Flash, DRAM, SRAM, other, not specific).

Key Features:

PDF>100 slides
Excel database containing all patents analyzed in the report (>1,300 patent families), including patent segmentations and hyperlinks to an updated online database.
Describing the global patenting trends, including time evolution of patent publications, countries of patent filings, etc.
Identifying the main patent assignees and the IP newcomers in the different segments.
Determining the status of their patenting activity (active / inactive) and their IP dynamics (ramping up, slowing down, steady).
Identifying the IP collaborations (patent co-filings) and IP transfers (changes of patent ownership).
Patents categorized into 9 segments according to technologies developed for neuromorphic computation: ReRAM, PCM, MRAM, FRAM/FeFET, Flash, DRAM, SRAM, etc.
IP profile of 8 key IP players: patent portfolio overview (IP dynamics, segmentation, legal status, geographic coverage, etc.), IP collaborations, key patents, and recent patenting activity.

In this report, the publisher aims to answer the following questions:

Is there a specific memory technology more suitable than others for artificial intelligence/machine learning (AI/ML)?
Who are the main patent owners and patent applicants in the field?
What are the technical challenges targeted in patents, and what is protected (device, system, method, etc.)?
Are there memory properties developed more specifically for intelligence/machine learning (AI/ML)?

Key Topics Covered:

INTRODUCTION

Questions answered by the patent landscape
Scope of the report
Reading guide
Main patent assignees mentioned in this report

EXECUTIVE SUMMARY

PATENT LANDSCAPE OVERVIEW

Time evolution of patent publications
IP players location
Industrial IP players location
Ranking of main patent assignees
Current legal status of the patents
Focus on industrials
Typology of industrials patent assignees
Current legal status of patents held by industrials
Timeline of industrial IP players
Geographical coverage of alive patents held by industrial players
IP leadership of industrial patent assignees
Main IP collaborations of industrial patent assignees
Samsung / IBM IP collaboration
SK Hynix IP collaborations
Industrial companies: summary
Focus on research organizations
Research organization location
IP leadership of research organization patent assignees
Timeline of academic IP players
Main IP collaborations of research organizations
Geographical distribution of alive patents held by research organizations
Research organizations: summary

PATENT SEGMENTATION

Memory technologies for AI/ML
Patent segmentation
Time evolution of patent publications for each
memory technology
Main patent assignees by memory technologies for
AI/ML
Other memory technologies
IP leadership of patent assignees for emerging NVM
Key patent families
Methodology
Selection of key patents
Other notable patent families

IP PROFILES OF KEY PLAYERS

IBM
Samsung
Applied Materials
TDK
SK Hynix
Macronix International
Hewlett Packard
TetraMem
Companies with RRAM core business, but being absent of this report since none of their memoryrelated patents explicitly mention AI/ML application

CONCLUSION

ANNEX

Methodology for patent search, selection and analysis
Methodology to identify key patents and key IP players
Terminology

A selection of companies mentioned in this report includes

IBM
Samsung
HP
TDK
Micron
SK Hynix
Macronix
Intel
TetraMem
GlobalFoundries
Fujitsu
Thales
Panasonic
Sony
Toshiba/Kioxia
Applied Materials
Winbond Electronics
Honeywell
Monolithic 3D
Qualcomm
TSMC
Gyrfalcon
I & F
ICLeague
Merck
NTT
Olympus
Rohm
HRL Labs
Semron
Synopsys
Texas Instruments
Western Digital
Crossbar
Weeebit
Nantero
Huazhong University of Science and Technology
Peking University
Nanjing University of Posts & Telecommunications (NJUPT)
Hanyang University
Seoul National University (SNU)
Pohang University of Science and Technology (POSTECH)
Institute of Microelectronics
Chinese Academy of Sciences (IMECAS)
Korea Advanced Institute of Science and Technology
Ningbo Institute of Materials Technology & Engineering (NIMTE)
Tsinghua University
CNRS - French National Research Center
Fudan University
Nanjing University
University of Electronic Science and Technology of China (UESTC)
Hebei University
Beihang University
Fuzhou University
University of Nankai (NKU)
Shandong University of Science and Technology
Sungkyunkwan University
Kookmin University
California Institute of Technology (Caltech)
Harvard University
CEA - French Alternative Energies and Atomic Energy Commission

For more information about this report visit https://www.researchandmarkets.com/r/jz6f2v-for?w=5

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