Silicon Photonics AI (Computing/Inverse Design) Patent Snapshot
The silicon photonics AI and inverse-design field is still expanding on a multi-year basis, with Aurora Operations holding a clear lead among a small set of early movers. The corpus remains compact, signalling an early-growth window where targeted filings can still establish foundational positions.
Aurora Operations leads a nascent, highly concentrated field
Aurora Operations Inc ranks first with 7 patent records, followed by Intel Corp with 4 and a cluster of applicants — Neye Systems Inc, the Regents of the University of California, and several individual inventors — each holding 3 records. The top-five filers account for 51% of the ranked applicants visible in this query’ combined total, a high concentration level for a field of this size.
The tier gap between Aurora Operations and the second-ranked Intel is already visible at this early stage, yet the overall applicant count remains small, meaning a single focused filing campaign by a new entrant can shift rankings materially.
| # | Applicant | Patent records | Share |
|---|---|---|---|
| 1 | Aurora Operations Inc | 7 | |
| 2 | Intel Corporation | 4 | |
| 3 | Neye Systems Inc | 3 | |
| 4 | Van Driel Henry M | 3 | |
| 5 | Wehrspohn Ralf Boris | 3 | |
| 6 | Leonard Stephen W | 3 | |
| 7 | Regents of the University of California | 3 | |
| 8 | NEC Laboratories America Inc | 2 | |
| 9 | IBM Corporation | 2 |
| # | Applicant | Patent records | Share |
|---|---|---|---|
| 10 | Massachusetts Institute of Technology | 2 | |
| 11 | Colorado State University Research Foundation | 1 | |
| 12 | Chandigarh University | 1 | |
| 13 | JIS College of Engineering | 1 | |
| 14 | Anurag Engineering College | 1 | |
| 15 | Hewlett Packard Enterprise Development LP | 1 | |
| 16 | Stanford University | 1 | |
| 17 | Huawei Technologies Canada Co Ltd | 1 |
Aurora Operations’ lead is concentrated in optical elements and radar/positioning branches, suggesting a LIDAR-oriented application of silicon photonics AI rather than a pure inverse-design software play. Intel’s presence spans optical control, modulation, and optical computing, positioning it as the nearest broad-scope challenger.
The most recent 18–24 months of filings are understated due to standard patent publication lag; current activity is likely higher than reported counts suggest. Longer-window growth, applicant concentration, and technology-route coverage are therefore more reliable signals than the latest-year bar alone.
A 2022 filing surge shaped the field; optical elements dominate the technology mix
Filing activity rose sharply through 2022 and has eased from that peak year, while the three-year recent window remains well above the prior window — consistent with a Growth-stage field. The technology composition is anchored by optical elements and systems, with AI computing and radar/positioning as secondary branches.
Annual filing trend
Activity was minimal before 2019, spiked to 10 records in 2022, and has moderated since. The 2024 and 2025 figures are understated by publication lag and should not be read as a real decline. The net 260% growth in the recent three-year window versus the prior three-year window confirms the field is still expanding on a multi-year basis.
↗ Hover for values · click a bar to ask EurekaTechnology composition
G02B (optical elements and systems) is visible in with 23 records, reflecting foundational waveguide and photonic-circuit work. G01S (radar, sonar, and positioning) and G06N (AI-model computing) are tied at 8 records each, showing that LIDAR sensing and neural-network inference are the two leading application axes. G02F (optical control and modulation) and G06E (optical computing) represent smaller but technically adjacent branches.
↗ 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.
Efficient Analog Backpropagation Training Architec…
An all-analog optical neural network includes multiple all-analog optical neural network layers; a laser and splitter configured to distribute light signals from the laser equally across all of the multiple all-analog optical neural network layers; integrated MZI switches configured to switch the all-analog optical neural network to a hybrid backpropagation… (excerpt from the patent abstract)
| # | Patent | Citations |
|---|---|---|
| 1 | Methods, systems, and apparatus for programmable q… | 107 |
| 2 | Methods, systems, and apparatus for programmable q… | 86 |
| 3 | Heterogeneously integrated silicon photonics neura… | 46 |
| 4 | Method of varying optical properties of photonic c… | 37 |
| 5 | Method of varying optical properties of photonic c… | 23 |
| 6 | Efficient photonic circuits for liquid-cooled high… | 7 |
| 7 | Silicon photonics device for LIDAR sensor and meth… | 4 |
| 8 | Efficient photonic circuits for liquid-cooled high… | 4 |
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.
Aurora Operations Inc
Aurora Operations holds 7 patent records, the largest position in the landscape. Its filings are concentrated in G02B (optical elements and systems), G01S radar and positioning sub-classes — specifically LIDAR-relevant classes G01S 7 and G01S 17 — indicating that silicon photonics AI is being applied primarily to autonomous-vehicle sensing. The applicant entered recently with a new-entrant trajectory, suggesting a rapid, focused filing campaign.
patent records: 7Intel Corp
Intel holds 4 patent records and covers a broader technology range than the leader: G02B optical elements, G02F optical control and modulation, and G06E optical computing. This spread positions Intel as the primary challenger in the computing and inverse-design sub-segments specifically. Intel also entered recently with a new-entrant trajectory, signalling active programme development rather than legacy portfolio maintenance.
patent records: 4Frequently asked questions
The current evidence covers 25 patent families in scope. This is a compact corpus consistent with an early Growth-stage field, where the foundational filing race is still active.
Aurora Operations Inc leads with 7 patent records, focused on optical elements and LIDAR/positioning applications of silicon photonics. Intel Corp is the nearest broad-scope challenger at 4 records.
The field is classified as Growth stage. Recent three-year filings are 260% above the prior three-year window. Annual volume peaked in 2022 and has eased since, with the most recent years further understated by publication lag.
The United States leads with 15 records, followed by WIPO/PCT at 5 and Europe (EPO) at 4. India has 3 records, Canada 2, and Singapore 1. Coverage outside the US remains limited, indicating potential gaps for international filers.
Yes. The only documented co-filing collaboration in the evidence is between Neye Systems Inc and the Regents of the University of California, with 3 co-filed records focused on optical elements and systems.
G06E (optical computing) and B82Y (nanotechnology applications) each carry very low record counts relative to the visible G02B class. G06E is particularly notable given its direct relevance to AI-driven photonic computing and inverse design.
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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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