On-chip generation of optical frequency combs using nonlinear ring resonators has enabled numerous applications of combs that were otherwise limited to mode-locked lasers. Nevertheless, on-chip frequency combs have relied predominantly on single-ring resonators. Here, we experimentally demonstrate the generation of a novel class of frequency combs, the topological frequency combs, in a two-dimensional lattice of hundreds of ring resonators that hosts fabrication-robust topological edge states with linear dispersion. By pumping these edge states, we demonstrate the generation of a nested frequency comb that shows oscillation of multiple edge state resonances across $\approx$40 longitudinal modes and is spatially confined at the lattice edge. Our results provide an opportunity to explore the interplay between topological physics and nonlinear frequency comb generation in a commercially available nanophotonic platform.

[Access this dataset on Dryad] https://doi.org/10.5061/dryad.xwdbrv1mw

Description of the data and file structure

112323_A1_C3R3_Drop_Group_Delay_larger_range.txt

Contains linear transmission and group delay data from Fig. 2 of the manuscript. Format is simple .txt file. Variables, which are columns in the text file, are labeled. In order, they are: Wavelength (nm), Frequency (GHz), Insertion Loss (dB), Group Delay (ps), Chromatic Dispersion (ps/nm).
231122_Run_3_Chip_A1_Col_3_Row_3_HighResOSA_Drop_bkTel_Theta_285__bkTel_swl_1547p35_OSA_N_1_centerwav_1500p0_span_400p0.npz

231122_Run_3_Chip_A1_Col_3_Row_3_HighResOSA_Drop_bkTel_Theta_283__bkTel_swl_1547p35_OSA_N_1_centerwav_1500p0_span_400p0.npz

231122_Run_3_Chip_A1_Col_3_Row_3_HighResOSA_Drop_bkTel_Theta_280__bkTel_swl_1547p35_OSA_N_1_centerwav_1500p0_span_400p0.npz

231122_Run_3_Chip_A1_Col_3_Row_3_HighResOSA_Drop_bkTel_Theta_278__bkTel_swl_1547p35_OSA_N_1_centerwav_1500p0_span_400p0.npz

231122_Run_3_Chip_A1_Col_3_Row_3_HighResOSA_Drop_bkTel_Theta_275__bkTel_swl_1547p35_OSA_N_1_centerwav_1500p0_span_400p0.npz

Contain Optical Spectrum Analyzer (OSA) spectra for five different pump powers as shown in Fig. 3 of the manuscript. (275, 278, 280, 283, 285 correspond to {70, 78, 86, 92, 100} W of on-chip peak power.) These can be processed via simple python function shown below. Variables stored are “intensity”, which is a numpy array of intensity values collected by the OSA, “times” which is a numpy array which stores timestamps for each datapoint in the “intensity” (dB) array, and “wl”, a numpy array storing wavelength (nm) values that correspond to each value in “intensity”.
240311_Run_3_A1_C3R3_Thru_Sample_Theta_24__FSV_BW_1kHz_AttnESA_0db_pts_31001_0_to_5p0GHz_wAPEX_bkTel_swl_1547p35_OSA_N_3_centerwav_1548p0_span_500p0.npz

240311_A1_C3R3_Thru_ApexAttn0p005_Theta_24_1547p35_Spectrum.dat

These contain data shown in Fig. 4. The .npz file contains the low resolution OSA spectrum stored as described above in #2. High resolution nested data from the ultra-high resolution heterodyne-based optical spectrum analyzer is stored in the .dat file. The .dat processing script below returns two variables: freq_in_GHz gives the x-axis of the collected spectrum in GHz, signal_in_vol gives the y-axis of the collected spectrum in voltage.
CCW_v1.png

CCW_Bulk_v1.png

CW_v1.png

These are the raw images taken that were then processed for Fig. 5 in the manuscript. CCW_v1 corresponds to panel A, CW_v1 to panel B, and CCW_Bulk_v1 to panel C.

Python Code For Processing

npz files can be processed with simple python function as follows.

import numpy as np
def load_sweep_file(filename): 
    npzfile = np.load(filename)
    intensity = npzfile['intensity']
    times = npzfile['times']
    wl = npzfile['wl']
    return intensity, times, wl

.dat file requires a more complex script, but can be processed with the following:

# Created by Apex Technologies
import os
def read_spectrum_binary_file(path, name_file):
    '''
    This function is to read spectrum binary file saved from OSA.
    The content of the spectrum binary stream is
       b185000.000000 7876 kh\x02Fk...
       b is a character of 'b' written into the binary file.
       185000.000000 is defaul optical frequency base.
       7876 is number of points.
       kh\x02Fk... stream contains the encoded frequency and signal voltage.
    Note: In this binary stream, the encoded optical frequency is in GHz,
          signal is in Voltage.
    '''

    #  Check if existing the path.
    if os.path.exists(path) == False:
        try:
            os.mkdir(path)
        except OSError:
            print("Creation of the directory %s failed" % path + '\n')
        else:
            print("Successfully created the directory %s " % path + '\n')
    else:
        print("the directory exits %s " % path + '\n')

    #  Open and reading the file to get the data.
    with open(path + '/' + name_file, 'rb') as binary_file:

        # Read the first character 'b' in the binary sream.
        ch = binary_file.read(1).decode('utf-8')

        if ch != 'b':
            path_file = path + '/' + name_file
            print("The file %s" % path_file + "is not binary file.")
            return

        # Read the default optical frequency base.
        optical_freq_base_GHz = binary_file.read(14).decode('utf-8')

        # Read the number of points.
        number_of_points = ''
        while True:
            ch = binary_file.read(1).decode('utf-8')
            number_of_points = number_of_points + ch
            if ch == str(' '):
                break

        number_of_points = int(number_of_points)
        # Read and unpack the double-precision float frequency in GHz.
        format_string = f'{number_of_points}f'
        freq_in_GHz = struct.unpack(
            format_string, binary_file.read(struct.calcsize(format_string)))

        # Read and unpack the double-precision float signal in Voltage.
        signal_in_vol = struct.unpack(
            format_string, binary_file.read(struct.calcsize(format_string)))

        # Convert tuple to list type.
        freq_in_GHz = np.array(freq_in_GHz)
        signal_in_vol = np.array(signal_in_vol)

        # Decode frequency vector to real one.
        freq_in_GHz += float(optical_freq_base_GHz)

        return freq_in_GHz, signal_in_vol

Observation of topological frequency combs

Data files

Abstract

Description of the data and file structure

Python Code For Processing

Observation of topological frequency combs

Data files

Abstract

README: Observation of Topological Frequency Combs

Description of the data and file structure

Python Code For Processing

Works referencing this dataset