IP Library › Granted Patent US 12,554,176
Granted Patent B2
US 12,554,176 · App. 18/014,871 · Granted Feb 17, 2026

Nonlinear optical system and method for optical information processing

Inventors: Bennet Fischer (Montreal, CA); Piotr Roztocki (Longueuil, CA); Mario Chemnitz (Jena, DE); Cristina Rimoldi (Turin, IT); Benjamin Maclellan (Stirling, CA); Luis Romero Cortes (Aljaraque, ES); Michael Kues (Hannover, DE); Jose Azana (Montreal, CA); Yoann Jestin (Montreal, CA); Roberto Morandotti (Montreal, CA)
Assignees: INSTITUT NATIONAL DE LA RECHERCHE SCIENTIFIQUE; GOTTFRIED WILHELM LEIBNIZ UNIVERSITAT HANNOVER
G02F1/353G06E1/02
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,554,176
App. No.
18/014,871
Granted
Feb 17, 2026
Kind
B2
Abstract

An optical information processing system comprising a nonlinear element selected in relation to input optical pulses to initiate nonlinear optical frequency conversion and a detection unit, the nonlinear element receiving encoded information input in form of pulsed light, pulsed light from the nonlinear element being read-out by the detection unit for spectro-temporal feature extraction, and the readout being used to train the system on a specific target to obtain a task-specific output or re-directed to the nonlinear element to obtain an input-dependent output, yielding processed information comprising selective positions in an output of the system. A method for training an optical system comprises, for each individual optical input information, reading specific optical amplitude or phase features from specific output bins of the system in time or frequency, weighting and evaluating the specific features towards optimizing user-defined fitness function to identify, classify, or fit the input information.

Claims (20)

1 . An optical information processing system, comprising a nonlinear element selected in relation to input optical pulses to initiate nonlinear optical frequency conversion and a detection unit; the nonlinear element receiving encoded information input in form of pulsed light, pulsed light from the nonlinear element being read-out by the detection unit for spectro-temporal feature extraction, the readout being one of: i) used to train the system on a specific target to obtain a task-specific output and ii) re-directed to the nonlinear element to obtain an input-dependent output; yielding processed information comprising selective positions in an output of the system.

2 . The system of claim 1 , comprising a tunable spectral routing element before the nonlinear element, for a random change of settings of the system.

3 . The system of claim 1 , comprising a tunable spectral routing processing the input optical pulses element before the nonlinear element, and a control unit for an adaptive change of settings of the system based on the readout through feedback-control.

4 . The system of claim 1 , wherein the input optical pulses have a time duration in a range between 1 fs and and 10000 fs and a spectral bandwidth below 100 nm.

5 . The system of claim 1 , wherein the input optical signals are ones of: sensor signals, image signals, optical ranging signals, optical tomography signals, telecom signals, and information carrying optical pulse series.

6 . The system of claim 1 , comprising an encoding unit, said encoding unit encoding information on the input optical signal.

7 . The system of claim 1 , wherein the nonlinear element is selected in relation to input optical pulses to initiate nonlinear optical effects by one or a cascade of ones of: four-wave mixing, soliton fission, dispersive wave generation, modulation instabilities, cross-phase modulation and self-phase modulation.

8 . The system of claim 1 , wherein the nonlinear element comprises at least one of: highly nonlinear fibers, dispersion-shifted fibers; doped fibers, oft-glass fibers, liquid-core fibers, hollow-core fibers, photonic crystal fibers and chip-integrated nonlinear waveguides.

9 . The system of claim 1 , wherein the detection unit comprises a tunable-spectral temporal detector.

10 . The system of claim 1 , the detection unit comprises a tunable-spectral temporal detector, wherein said detector is interfaced to a computer for on-line read-out and further processing.

11 . The system of claim 1 , comprising a tunable spectral routing element before the nonlinear element, wherein the routing unit is one of: a tunable spectral routing element, a tunable temporal routing unit and a spectro-temporal routing unit.

12 . The system of claim 1 , comprising a tunable spectro-temporal routing element before the nonlinear element, wherein the routing unit is optically connected to the detection unit and electrically interfaced to a computer for feedback-control.

13 . The system of claim 1 , comprising a tunable temporal splitter before the nonlinear element for one of: information encoding and input pulse processing, and the tunable temporal splitter is interfaced to a computer for feed-back control according to the readout.

14 . The system of claim 1 , comprising one of: a temporal, spectral, and spectro-temporal phase and/or amplitude filter unit before the nonlinear element for information encoding or input signal processing, and the filter unit is interfaced to a computer for feed-back control according to the readout.

15 . The system of claim 1 , interfaced to a computer for feed-back control according to the readout.

16 . The system of claim 1 , interfaced to a computer for feed-back control according to the readout, by one of machine-learning and optimization.

17 . The system of claim 1 , comprising a nonlinear element, a tunable spectral, temporal, or spectro-temporal unit, and a feedback circuit from the nonlinear element output that controls the tunable spectral, temporal, or spectro-temporal unit.

18 . An optical information processing method, comprising processing encoded information input in form of pulsed light in a nonlinear element, and reading out for spectro-temporal feature extraction, comprising training to a user-defined task by at least one of: i) recording an output and using machine-learning to retrieve an input-specific response, in offline-training configuration; ii) using a tunable temporal pulse splitter between the input and the nonlinear element, evaluating an output and feeding back to the tunable temporal splitter for improving amplitude or phase features distinguishability, in offline-training; iii) using a tunable spectral and/or temporal filter after the nonlinear element to extract the amplitude or phase features in online output training.

19 . The system of claim 1 , wherein the nonlinear element is selected in relation to the input optical pulses to initiate a cascade of one of: four-wave mixing, soliton fission, dispersive wave generation, modulation instabilities, cross-phase modulation, and self-phase modulation.

20 . An optical information processing method, comprising processing information input in form of pulsed light in a nonlinear element, and reading out for spectro-temporal feature extraction, the method comprising training to a user-defined task by at least one of: i) recording an output and using machine-learning to retrieve an input-specific response; ii) using a tunable spectral and/or temporal filter or temporal pulse splitter between the input and the nonlinear element, evaluating an output and feeding back to the tunable temporal splitter for improving feature amplitude or phase features distinguishability; iii) using a tunable spectral and/or temporal filter after the nonlinear element to extract the amplitude or phase features.

Assignments (10)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 9, 2023
From: CHEMNITZ, MARIO
To: INSTITUT NATIONAL DE LA RECHERCHE SCIENTIFIQUE
Reel/Frame 062315/0814 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 9, 2023
From: RIMOLDI, CRISTINA
To: INSTITUT NATIONAL DE LA RECHERCHE SCIENTIFIQUE
Reel/Frame 062315/0857 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 9, 2023
From: MACLELLAN, BENJAMIN
To: INSTITUT NATIONAL DE LA RECHERCHE SCIENTIFIQUE
Reel/Frame 062315/0874 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 9, 2023
From: CORTES, LUIS ROMERO
To: INSTITUT NATIONAL DE LA RECHERCHE SCIENTIFIQUE
Reel/Frame 062315/0885 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 9, 2023
From: KUES, MICHAEL
To: GOTTFRIED WILHELM LEIBNIZ UNIVERSITAT HANNOVER
Reel/Frame 062315/0908 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 9, 2023
From: AZANA, JOSE
To: INSTITUT NATIONAL DE LA RECHERCHE SCIENTIFIQUE
Reel/Frame 062315/0934 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 9, 2023
From: JESTIN, YOANN
To: INSTITUT NATIONAL DE LA RECHERCHE SCIENTIFIQUE
Reel/Frame 062315/0961 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 9, 2023
From: MORANDOTTI, ROBERTO
To: INSTITUT NATIONAL DE LA RECHERCHE SCIENTIFIQUE
Reel/Frame 062315/0975 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 9, 2023
From: FISCHER, BENNET
To: INSTITUT NATIONAL DE LA RECHERCHE SCIENTIFIQUE
Reel/Frame 062316/0005 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 9, 2023
From: ROZTOCKI, PIOTR
To: INSTITUT NATIONAL DE LA RECHERCHE SCIENTIFIQUE
Reel/Frame 062316/0034 →
Continuity (2)
Provisional Application 63051435 · Jul 14, 2020
Related Publication 20230296960A1 · Sep 21, 2023
References Cited (21)
US 6724783B2 · Jalali et al. · 2004 [cited by applicant]
US 7139478B2 · Eggleton · 2006 [cited by examiner]
US 7352504B2 · Hirooka · 2008 [cited by examiner]
US 10268232B2 · Harris et al. · 2019 [cited by applicant]
US 10908026B2 · Maia Da Silva · 2021 [cited by examiner]
US 20170351293A1 · Carolan et al. · 2017 [cited by applicant]
US 20190226989A1 · Karpf · 2019 [cited by examiner]
US 20200209709A1 · Suchowski · 2020 [cited by examiner]
US 20220253685A1 · Ozcan · 2022 [cited by examiner]
WO WO2019200289 · 2019 [cited by applicant]
WO WO2021050550 · 2021 [cited by applicant]
International Search Report issued on Oct. 26, 2021 in corresponding PCT Application No. PCT/CA2021/050972. [cited by applicant]
Chang et al., Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification, Scientific Reports (2018) 8:12324. [cited by applicant]
Fang et al., Design of optical neural networks with component imprecisions, Optics Express 14009, vol. 27 No. 10, May 13, 2019. [cited by applicant]
Goda et al., Serial time-encoded amplified imaging for real-time observation of fast dynamic phenomena, Nature, vol. 458, Apr. 30, 2019. [cited by applicant]
Lin et al., All-optical machine learning using diffractive deep neural networks, Science 361, 1004-1008, Sep. 7, 2018. [cited by applicant]
Shen et al., Deep learning with coherent nanophotonic circuits, Nature Phononics, vol. 11, Jun. 12, 2017. [cited by applicant]
Wetzel et al., Customizing supercontinuum generation via on-chip adaptive temporal pulse-splitting, Nature Communication, 2018. [cited by applicant]
Willner et al., All-Optical Signal Processing, Journal of Lightwave Technology, vol. 32, No. 4, Feb. 15, 2014. [cited by applicant]
Zhang et al., Low-Depth Optical Neural Networks, Physics.Optics, May 18, 2019. [cited by applicant]
Zhou et al., Self-learning photonic signal processor with an optical neural network chip, (2019). [cited by applicant]