FinFET DTCO / ML Patent Snapshot 2026
The FinFET DTCO / ML patent space is small and highly concentrated, with 19 patent families split among a handful of academic and industrial filers whose top five account for the majority of output among the largest filers. Annual volume peaked in 2019 and has eased considerably since, signaling a field past its initial surge rather than one in broad commercial expansion.
A concentrated field led by three equally ranked filers
Xidian University, IBM, and Applied Materials each hold 4 patent families, placing them jointly at the top of the applicant ranking. GITAM Deemed University follows with 2 patent families, and the remaining filers each hold 1.
The top five filers account for 68% of the combined output of the ranked applicants visible in this query, a high concentration ratio for a corpus of this size. The tier gap between the joint leaders and the rest of the field is steep: no challenger has more than half the leaders’ output.
| # | Applicant | Patent families | Share |
|---|---|---|---|
| 1 | Xidian University | 4 | |
| 2 | IBM (International Business Machines Corporation) | 4 | |
| 3 | Applied Materials Inc. | 4 | |
| 4 | GITAM Deemed to be University | 2 | |
| 5 | Dr. Md Waseem Akram | 1 | |
| 6 | Prof. Dinesh Prasad | 1 |
| # | Applicant | Patent families | Share |
|---|---|---|---|
| 7 | Intel Corporation | 1 | |
| 8 | Synopsys Inc. | 1 | |
| 9 | Southeast University | 1 | |
| 10 | DHANALAKSHMI SRINIVASAN COLLEGE OF ENGINEERING & T… | 1 | |
| 11 | Dr. Umayia Mushtaq | 1 | |
| 12 | National Center of Technology Innovation for EDA | 1 |
The co-presence of a research university (Xidian), a foundational semiconductor equipment supplier (Applied Materials), and a systems incumbent (IBM) at the same count signals that neither academia nor industry has yet pulled decisively ahead in defining the IP evidence snapshot for ML-assisted FinFET DTCO.
Filing years 2024–2026 are likely under-counted owing to standard patent publication lag; the apparent modest activity in those years should not be read as a further drop in interest. Longer-window growth, applicant concentration, and technology-route coverage are therefore more reliable signals than the latest-year bar alone.
A 2019 filing spike followed by sustained low-volume activity
The annual trend chart and technology composition map together show a field that burst into activity around 2019 and has since settled into low but persistent output, anchored by AI-computing and semiconductor-device classes in roughly equal measure.
Annual filing trend
Filings jumped to 6 in 2019, fell to 3 in 2020, dropped to zero in 2021, then recovered modestly to 3 in 2022 before tapering to 1–2 per year. The 2024–2026 bars are subject to publication lag and likely understate true activity.
↗ Hover for values · click a bar to ask EurekaTechnology composition
G06N (AI-model computing) and H01L (semiconductor devices) each appear in 11 records, reflecting the field’s dual identity as both a device-physics and an AI-methods domain. G06F (digital data processing) is the third pillar with 8 records, consistent with EDA and simulation tool patents. Memory-related classes H10B and G11C are present at lower counts, suggesting early but limited coverage of ML-assisted memory cell optimization.
↗ Hover for values · click a bar to ask EurekaHighly cited patent families surfaced by the query
Citation-heavy patent families returned by the query. Use this section as citation context, not as a curated list of the most topic-specific patents.
FinFET 2T2R RRAM
A first fin field effect transistor (FinFET) has an internal source/drain (S/D) with a facetted face that is connected to a dielectric side of a first RRAM. A second FinFET and RRAM structure are also disclosed. In some embodiments, an electrode contact side of each RRAM is connected in common to form a 2T2R device. The locations of one or more electrode… (excerpt from the patent abstract)
| # | Patent | Citations |
|---|---|---|
| 1 | FinFET 2T2R RRAM | 8 |
| 2 | Reconfigurable finfet-based artificial neuron and … | 5 |
| 3 | 基于神经网络预测FinFET器件的单粒子效应及特征参数的方法 | 4 |
| 4 | 基于神经网络的FinFET器件性能预测及结构优化方法 | 4 |
| 5 | FinFET 2T2R RRAM | 4 |
| 6 | Finfet resistive switching device having interstit… | 3 |
| 7 | 基于深度学习的FINFET器件直流特性预测方法 | 2 |
| 8 | 一种预测鳍式场效应管器件随机掺杂波动效应的方法 | 1 |
Ranked by total forward citations. Citation counts favour older and broadly cited patent families, and broad or adjacent patents may appear when they match the search scope. Treat this section as citation context, not as a curated list of the most topic-specific patents. Some patent titles may be shown in their original, non-English language where an accurate translation could not be guaranteed.
Assignee snapshot from the current evidence set
The applicants below are visible in this query result. Because the evidence set is relatively small, read this section as a directional snapshot rather than a full competitive ranking.
Xidian University
Xidian University holds 4 patent families concentrated in G06F 30 (EDA / design simulation), G06F 119, and G06N 3 (neural-network computing), reflecting a research focus on ML-based FinFET device characterization and performance prediction. Momentum data marks the institution as a new entrant in the recent filing window, suggesting its portfolio is recent and therefore likely still within examination.
families: 4Applied Materials
Applied Materials holds 4 patent families with emphasis on G06N 3 (AI models), H01L 27 (semiconductor devices), and H10B 51 (memory device manufacture), indicating an applied ML-in-process-equipment angle that bridges device physics and manufacturing. This cross-class coverage distinguishes Applied Materials from the more simulation-oriented academic filers.
families: 4Frequently asked questions
The corpus contains 19 patent families in scope. This is a small, specialized field; the limited corpus size means individual filers can materially shift concentration ratios with a handful of new filings.
Xidian University, IBM, and Applied Materials are jointly ranked first, each holding 4 patent families. GITAM Deemed University follows with 2 families. All remaining filers hold 1 family each.
The United States leads with 7 records by filing office, followed by China with 5 and India with 4. Europe (EPO) has 2 records and WIPO has 1.
The field is assessed as being in Decline. Annual filings peaked at 6 in 2019 and have eased back considerably since, with a recent-window growth rate of -33%. Filing years 2024–2026 are likely under-counted due to publication lag.
G06N (AI-model computing) and H01L (semiconductor devices) each appear in 11 records, reflecting the dual AI-methods and device-physics character of the field. G06F (digital data processing) is third with 8 records, consistent with EDA and simulation patents.
One co-applicant relationship is documented: the National Center of Technology Innovation for EDA and Southeast University filed jointly. No cross-border or industry-to-industry co-filing pairs are in evidence in this corpus.
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Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
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