Ultra-wideband arbitrary optical waveform single-frame measurement method based on neural network

Abstract

The invention discloses an ultra-wideband arbitrary optical waveform single-frame measurement method based on a neural network, and belongs to the technical field of ultrafast laser. The method comprises the specific steps of generating optical waveforms to be measured, performing linear spectrum slicing processing, performing multi-channel photoelectric detection and data acquisition, constructing a neural network model and a data set, training the neural network model, and performing amplitude and phase reconstruction measurement on any optical waveform to be measured. Frequency spectrum slicing and multi-channel parallel processing of the optical waveform to be detected are achieved through linear frequency spectrum slicing processing, the system complexity can be effectively reduced, and the requirement for photoelectric detection and data acquisition analog bandwidth is reduced; the rapid reconstruction measurement of the amplitude and phase of the optical waveform to be measured is realized through the calculation of the neural network model, the single-frame rapid measurement of any optical waveform is realized, and the measurement sensitivity and flexibility are remarkably improved; the ultra-wideband spectrum slice parallel output can be realized based on a common optical filter, and the method has the advantages of simplicity, high efficiency, good stability and the like.

Description

Technical Field

[0001] The invention belongs to the technical field of ultrafast lasers and relates to a single-frame measurement method for ultra-wideband arbitrary optical waveforms based on a neural network. Background Art

[0002] The measurement of the amplitude and phase of ultra-wideband arbitrary optical waveforms plays an important role in the fields of ultrafast optics, quantum information processing, spectral measurement, microwave photons, optical communications, etc. The analog bandwidth of existing electronic devices such as photodetectors and oscilloscopes can only reach tens of GHz, which is not enough to measure picosecond and femtosecond ultrafast laser pulses, and it is also difficult to directly obtain the amplitude and phase information of ultra-wideband optical waveforms at the same time. Currently, commonly used optical waveform measurement methods such as frequency-resolved optical gating (FROG) can achieve the measurement of ultrashort pulses based on nonlinear effects and phase recovery algorithms.

[0003] Chinese invention patent CN112595425B discloses an ultrashort laser pulse measurement method and measurement system, which solves the technical problem that the existing ultrashort laser pulse measurement method has high requirements for nonlinear crystal processing, but the system structure is complex and still needs to use phase extraction algorithms such as iterative calculations to achieve pulse phase reconstruction. Chinese invention patent CN114216575B proposes a FROG ultrashort pulse reconstruction system and method based on BP neural network, which uses neural networks instead of traditional algorithms to calculate amplitude phase data. However, the above methods still realize pulse measurement based on nonlinear effects, the system structure is complex and the pulse to be measured needs to be amplified, which limits the actual application scenarios.

[0004] In summary, it is of great significance to study a single-frame measurement method for ultra-wideband arbitrary optical waveforms that does not require nonlinear effects and has a simple structure and method. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention proposes a single-frame measurement method for ultra-wideband arbitrary optical waveforms based on neural networks, which realizes rapid reconstruction and measurement of the amplitude and phase of arbitrary optical waveforms through linear spectrum slicing processing, multi-channel photoelectric detection and deep learning.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] The single-frame measurement method of ultra-wideband arbitrary optical waveform based on neural network includes the following steps:

[0008] The first step is to generate the optical waveform to be measured.

[0009] The laser pulse is generated by a femtosecond pulse laser, and the amplitude modulation and phase modulation of the laser pulse spectrum are realized by a programmable optical waveform shaper to obtain an optical waveform to be measured. The optical waveform to be measured refers to a time domain optical waveform. The amplitude modulation and phase modulation are intended to make the optical waveform to be measured have arbitrary amplitude and phase in the time domain. In the optical waveform to be measured, the amplitude modulation signal and the phase modulation signal can be a randomly generated Gaussian white noise signal or other randomly generated arbitrary waveform signals.

[0010] The second step is linear spectrum slicing processing.

[0011] The optical waveform to be measured is processed by linear spectrum slicing through a 1×N splitter and N optical filters with different central wavelengths, or through a 1×N multiplexer, and then N-channel spectrum slicing signals are output in parallel.

[0012] In order to achieve gapless spectrum slicing, the optical filter or multiplexer should have different central wavelengths in different channels, and the passband frequency coverage ranges of channels with adjacent central wavelengths overlap each other.

[0013] The number N of channels of the optical filter or multiplexer is determined by the spectrum bandwidth corresponding to the optical waveform to be measured and the working bandwidth of the optical filter or multiplexer.

[0014] The third step is multi-channel photoelectric detection and data acquisition.

[0015] The N-channel spectrum slice signals output in parallel in the second step are subjected to N-channel photoelectric detection and data acquisition to obtain time domain intensity waveform data of the N-channel spectrum slice signals.

[0016] Alternatively, the spectrum slice signals of each channel output in parallel in the second step are sequentially introduced with time delays and synthesized into a single output. Through single-channel photoelectric detection and data acquisition, serial detection and acquisition of multi-channel spectrum slice signals are realized to obtain time domain intensity waveform data, which can effectively reduce the number of required photoelectric detection channels.

[0017] The fourth step is to build a neural network model and data set.

[0018] The neural network model includes a fully connected neural network model or a convolutional neural network model. For a specific optical waveform to be measured generated in the first step, its amplitude and phase are used as output labels, and the time domain intensity waveform data obtained in the third step is used as input to the neural network model. The output of the neural network model is the predicted optical waveform amplitude and phase, which is used as the reconstruction result.

[0019] By randomly generating a large number of Gaussian white noise signals or arbitrary waveforms in the first step, a large number of optical waveforms to be tested are generated, and a data set is constructed for subsequent neural network model training.

[0020] The fifth step is to train the neural network model.

[0021] The neural network model constructed in step 4 is trained using the data set constructed in step 4. The neural network model is trained using the back propagation algorithm and optimizer to reduce the error between the reconstruction result and the label and adjust the neural network model parameters. Through multiple iterations of training, the neural network model can effectively learn the mapping relationship between the time domain intensity waveform data and the optical waveform to be measured, and finally obtain accurate optical waveform amplitude and phase reconstruction results, and obtain a trained neural network model.

[0022] Step 6: Reconstruct and measure the amplitude and phase of any optical waveform to be measured.

[0023] For any optical waveform to be measured, the second and third steps are used to process and obtain the time domain intensity waveform data, which is then input into the neural network model trained in the fifth step, and the predicted optical waveform amplitude and phase are output, thereby finally realizing the reconstruction measurement of the amplitude and phase of any optical waveform to be measured.

[0024] The beneficial effects of the present invention are:

[0025] (1) The present invention realizes spectrum slicing and multi-channel parallel processing of the optical waveform to be measured through linear spectrum slicing processing, avoids the use of nonlinear effects, can effectively reduce system complexity, and effectively reduce the demand for photoelectric detection and data acquisition analog bandwidth.

[0026] (2) The present invention realizes rapid reconstruction measurement of the amplitude and phase of the optical waveform to be measured through neural network model calculation, avoiding the use of complex coherent receiving systems and phase recovery algorithms. It can realize rapid single-frame measurement of arbitrary optical waveforms and significantly improve measurement sensitivity and flexibility.

[0027] (3) The present invention can realize the parallel output of ultra-wideband spectrum slices based on commonly used optical filters, and does not require other prior information such as filters and photodetector spectrum responses. Compared with traditional methods, the present invention has the advantages of simple implementation method, high efficiency and good stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart for implementing the ultra-wideband arbitrary optical waveform single-frame measurement method provided by the present invention.

[0029] Figure 2 This is a simulation result of reconstructing a measured optical waveform in Embodiment 1 of the present invention.

[0030] Figure 3 This is a reconstructed measurement simulation result of another optical waveform to be measured in embodiment 2 of the present invention. DETAILED DESCRIPTION

[0031] In order to make the method problems solved by the present invention, the method solutions adopted and the method effects achieved more clearly, the present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0032] like Figure 1 As shown, the neural network-based ultra-wideband arbitrary optical waveform single-frame measurement method provided by the present invention comprises the following steps:

[0033] The first step is to generate the optical waveform to be measured.

[0034] In this embodiment, a femtosecond pulse laser is used to generate a laser pulse with a 3-dB bandwidth of 400 GHz, which is input into a programmable optical waveform shaper for generating an optical waveform to be measured. A MATLAB Gaussian white noise function generator is used to randomly generate an amplitude modulation signal and a phase modulation signal, and the amplitude modulation and phase modulation of the laser pulse spectrum are realized through the programmable optical waveform shaper, and an optical waveform to be measured with random amplitude and phase is output.

[0035] The second step is linear spectrum slicing processing.

[0036] In this embodiment, the optical waveform to be measured passes through a 1×3 splitter and three optical filters with different center wavelengths, and then outputs three-channel spectrum slice signals in parallel. To achieve gapless spectrum slicing, the 3-dB bandwidth of the optical filters is 150 GHz, and the center frequency intervals of the filters in adjacent channels differ by 125 GHz. By slicing and processing the broadband spectrum of the optical waveform to be measured in parallel, the loss of spectrum information due to the analog bandwidth limitation of the photodetector and data acquisition can be avoided, thereby significantly reducing the demand for the photodetector and data acquisition bandwidth.

[0037] The third step is multi-channel photoelectric detection and data acquisition.

[0038] In this embodiment, the 3-channel spectrum slice signals output in parallel in the second step are subjected to 3-channel photoelectric detection and data acquisition to obtain the time domain intensity waveform data of the 3-channel spectrum slice signals. The analog bandwidth used for photoelectric detection and data acquisition is 30 GHz.

[0039] The fourth step is to build a neural network model and data set.

[0040] A fully connected neural network model is constructed, which includes an input layer, three fully connected layers and an output layer. Each fully connected layer contains 256 neurons, and each neuron uses a linear rectification function as an activation function.

[0041] The amplitude and phase of the optical waveform to be measured generated in the first step are used as output labels, and the time domain intensity waveform data obtained in the third step is used as the input of the neural network model. The output of the neural network model is the predicted optical waveform amplitude and phase, which is used as the reconstruction result.

[0042] By randomly generating 5000 time-domain optical waveforms to be tested in the first step, the time-domain intensity waveform data of the corresponding 3-channel spectrum slice signals are obtained through the second and third steps to construct the data set.

[0043] The fifth step is to train the neural network model.

[0044] The neural network model constructed in step 4 is trained using the data set constructed in step 4. The model is trained using the back propagation algorithm and the Adam optimizer to reduce the mean square error between the reconstruction result and the label and adjust the model parameters. Through multiple iterations of training, the mean square error is reduced to an acceptable range, and finally accurate optical waveform amplitude and phase reconstruction results are obtained, and a trained neural network model is obtained.

[0045] Step 6: Reconstruct and measure the amplitude and phase of any optical waveform to be measured.

[0046] This embodiment generates any optical waveform to be measured through the first step method, obtains time domain intensity waveform data through the second and third steps, inputs it into the neural network model trained in the fifth step, and outputs the predicted optical waveform amplitude and phase, thereby finally realizing the reconstruction measurement of the amplitude and phase of any optical waveform to be measured.

[0047] Example 1

[0048] In this embodiment, a simulation result of an optical waveform to be measured is generated by the first step method. Figure 2 (dashed line), the reconstructed measurement simulation results for the optical waveform to be measured are as follows Figure 2 (solid line).

[0049] Example 2

[0050] In this embodiment, another optical waveform simulation result to be measured is generated by the first step method. Figure 3 (dashed line), the reconstructed measurement simulation results for the optical waveform to be measured are as follows Figure 3 (solid line).

[0051] It can be seen that the present invention can well complete the measurement of arbitrary optical waveforms, and can achieve accurate reconstruction of the amplitude and phase of optical waveforms with bandwidths of hundreds of GHz based on photoelectric detection with bandwidths of tens of GHz.

[0052] The above-described embodiments merely express the implementation methods of the present invention, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A single-frame measurement method for ultra-wideband arbitrary optical waveform based on neural network, characterized in that: The ultra-wideband arbitrary optical waveform single-frame measurement method comprises the following steps: The first step is to generate the optical waveform to be measured; A laser pulse is generated by a femtosecond pulse laser, and the amplitude modulation and phase modulation of the laser pulse spectrum are realized by a programmable optical waveform shaper to obtain an optical waveform to be measured; the amplitude modulation signal and the phase modulation signal obtained by the amplitude modulation and the phase modulation are randomly generated Gaussian white noise signals or other randomly generated arbitrary waveform signals; The second step is linear spectrum slicing processing; The optical waveform to be measured is processed by a 1×N splitter and N optical filters with different central wavelengths, or by a 1×N multiplexer for linear spectrum slicing, and then N-channel spectrum slicing signals are output in parallel; The third step is multi-channel photoelectric detection and data acquisition; The N-channel spectrum slice signals output in parallel in the second step are subjected to N-channel photoelectric detection and data acquisition to obtain time domain intensity waveform data of the N-channel spectrum slice signals; Or, the spectrum slice signals of each channel output in parallel in the second step are sequentially introduced with time delay and synthesized into a single output, and the serial detection and collection of the multi-channel spectrum slice signals are realized through single-channel photoelectric detection and data collection to obtain time domain intensity waveform data; The fourth step is to build a neural network model and data set; The neural network model includes a fully connected neural network model or a convolutional neural network model; for a specific optical waveform to be measured generated in the first step, its amplitude and phase are used as output labels, and the time domain intensity waveform data obtained in the third step is used as input of the neural network model; the output of the neural network model is the predicted optical waveform amplitude and phase, which is used as the reconstruction result; The optical waveform to be measured is generated by randomly generating a Gaussian white noise signal or an arbitrary waveform signal in the first step, and a data set is constructed for subsequent neural network model training; Step 5: Train the neural network model; Using the data set constructed in the fourth step, the neural network model constructed in the fourth step is trained to obtain a trained neural network model; Step 6: reconstruct and measure the amplitude and phase of any optical waveform to be measured; For any optical waveform to be measured, the second and third steps are used to process and obtain the time domain intensity waveform data, which is then input into the neural network model trained in the fifth step, and the predicted optical waveform amplitude and phase are output, thereby finally realizing the reconstruction measurement of the amplitude and phase of any optical waveform to be measured.

2. The neural network-based ultra-wideband arbitrary optical waveform single-frame measurement method according to claim 1, characterized in that: In the first step, the amplitude modulation and phase modulation are intended to make the optical waveform to be measured have arbitrary amplitude and phase in the time domain.

3. The neural network-based ultra-wideband arbitrary optical waveform single-frame measurement method according to claim 1, characterized in that: In the second step, the optical filter or multiplexer should have different central wavelengths in different channels, and the passband frequency coverage ranges of channels with adjacent central wavelengths overlap each other to achieve gapless spectrum slicing.

4. The neural network-based ultra-wideband arbitrary optical waveform single-frame measurement method according to claim 1, characterized in that: In the second step, the number of channels N of the optical filter or multiplexer is determined by the spectrum bandwidth corresponding to the optical waveform to be measured and the working bandwidth of the optical filter or multiplexer.

5. The neural network-based ultra-wideband arbitrary optical waveform single-frame measurement method according to claim 1, characterized in that: In the fifth step, the neural network model is trained using a back-propagation algorithm and an optimizer to reduce the error between the reconstruction result and the label and adjust the neural network model parameters; through multiple iterative training, accurate optical waveform amplitude and phase reconstruction results are obtained, and finally a trained neural network model is obtained.

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