Unidirectional mode selection in bistable quantum cascade ring lasers

Ideal ring resonators are characterized by travelling-wave counterpropagating modes, but in practice travelling waves can only be realized under unidirectional operation, which has proved elusive. Here, we have designed and fabricated a monolithic quantum cascade ring laser coupled to an active waveguide that allows for robust, deterministic and controllable unidirectional operation. Spontaneous emission injection through the active waveguide enables dynamical switching between the clockwise and counterclockwise states of the ring laser with as little as 1.6% modulation of the electrical input. We show that this behavior stems from a perturbation in the bistable dynamics of the ring laser. In addition to switching and bistability, our novel coupler design for quantum cascade ring lasers offers an efficient mechanism for outcoupling and light detection.Comment: 13 pages, 6 figures, submitted to journa

Quantum Cascade Surface-Emitting Photonic Crystal Laser

We combine photonic and electronic band structure engineering to create a surface-emitting quantum cascade microcavity laser. A high-index contrast two-dimensional photonic crystal is used to form a micro-resonator that simultaneously provides feedback for laser action and diffracts light vertically from the surface of the semiconductor surface. A top metallic contact allows electrical current injection and provides vertical optical confinement through a bound surface plasmon wave. The miniaturization and tailorable emission properties of this design are potentially important for sensing applications, while electrical pumping can allow new studies of photonic crystal and surface plasmon structures in nonlinear and near-field optics

Quantum cascade photonic-crystal microlasers

We describe the realization of Quantum Cascade photonic-crystal microlasers. Photonic and electronic bandstructure engineering are combined to create a novel Quantum Cascade microcavity laser source. A high-index contrast two-dimensional photonic crystal forms a micro-resonator that provides feedback for laser action and diffracts light vertically from the surface of the semiconductor chip. A top metallic contact is used to form both a conductive path for current injection as well as to provide vertical optical confinement to the active region through a bound surface plasmon state at the metal-semiconductor interface. The device is miniaturized compared to standard Quantum Cascade technology, and the emission properties can in principle be engineered by design of the photonic crystal lattice. The combination of size reduction, vertical emission, and lithographic tailorability of the emission properties enabled by the use of a high-index contrast photonic crystal resonant cavity makes possible a number of active sensing applications in the mid- and far-infrared. In addition, the use of electrical pumping in these devices opens up another dimension of control for fundamental studies of photonic crystal and surface plasmon structures in linear, non-linear, and near-field optics

Quantum cascade photonic-crystal microlasers

We describe the realization of Quantum Cascade photonic-crystal microlasers. Photonic and electronic bandstructure engineering are combined to create a novel Quantum Cascade microcavity laser source. A high-index contrast two-dimensional photonic crystal forms a micro-resonator that provides feedback for laser action and diffracts light vertically from the surface of the semiconductor chip. A top metallic contact is used to form both a conductive path for current injection as well as to provide vertical optical confinement to the active region through a bound surface plasmon state at the metal-semiconductor interface. The device is miniaturized compared to standard Quantum Cascade technology, and the emission properties can in principle be engineered by design of the photonic crystal lattice. The combination of size reduction, vertical emission, and lithographic tailorability of the emission properties enabled by the use of a high-index contrast photonic crystal resonant cavity makes possible a number of active sensing applications in the mid- and far-infrared. In addition, the use of electrical pumping in these devices opens up another dimension of control for fundamental studies of photonic crystal and surface plasmon structures in linear, non-linear, and near-field optics

Evanescent Coupling of a Center-Cleaved Mid-Infrared Quantum Cascade Laser to a Suspended Silicon-on-Insulator Waveguide

Abstract: Two-dimensional finite-element analysis of the evanescent coupling of a center-cleaved quantum cascade laser to a suspended silicon waveguide in Silicon-on-insulator shows peak efficiency at a separation of 1.0 µm for a wavelength of 4 µm

QCL Dataset, 10 Layer Structure, Tolerance [-5, +20] Å, Electric Field [10,10,150] kV/cm

A dataset of 2400 quantum cascade structures at 15 electric field iterations, for a total of 36000 unique designs. The structures are generated by randomly altering a starting 10-layer design of alternating Al0.48In0.52As barrier material and In0.53Ga0.47As well material, with layer thickness sequence of 9/57/11/54/12/45/25/34/14/33 Angstroms (starting with well material). The random tolerance range is from -5 to +20 Angstroms in 5 Angstrom increments. The laser transition Figure of Merit, among other quantities of interest, is identified for each design using a method found in: A. C. Hernandez, M. Lyu and C. F. Gmachl, "Generating Quantum Cascade Laser Datasets for Applications in Machine Learning," 2022 IEEE Photonics Society Summer Topicals Meeting Series (SUM), 2022, pp. 1-2, doi: 10.1109/SUM53465.2022.9858281QCL-layer_10-4rep-rand-m5d5p20A-efield_10-10-150-v22-dataset.cs

Quantum cascade laser transition code

The software ErwinJr2 is publicly available at: https://github.com/ErwinJr2/ErwinJr2 Details on using the laser transition code to build QCL datasets to be used in machine learning can be found in: A. C. Hernandez and C. F. Gmachl, “Application of Machine Learning to Quantum Cascade Laser Design,” in 2023 57th Annual Conference on Information Sciences and Systems, CISS 2023, 2023. doi: 10.1109/CISS56502.2023.10089756. More details on building QCL datasets can be found: A. C. Hernandez, M. Lyu and C. F. Gmachl, "Generating Quantum Cascade Laser Datasets for Applications in Machine Learning," 2022 IEEE Photonics Society Summer Topicals Meeting Series (SUM), 2022, pp. 1-2, doi: 10.1109/SUM53465.2022.9858281 An example of a QCL dataset is found: Andres Correa, H., Gmachl, C. F., & Lyu, M. (2023). QCL Dataset, 10 Layer Structure, Tolerance [-2, +3] A, Electric Field [0,10,150] kV/cm [Data set]. Princeton University. https://doi.org/10.34770/R7NR-EE50A code to identify the laser transition for a quantum cascade laser design based on the figure of merit. Variables such as the number of layers, and layer thicknesses, as well the applied electric field, materials composition, number of period repetitions, and layer tolerance ranges to generate random designs are specified. A folder containing a .csv file with all electronic state-pair transitions collected, a .png file of the bandstructure and the laser transition chosen (in red), for all electric field iterations, and a summary .csv file of all these laser transitions for a structure at each electric field is generated by the code. To use, first install ErwinJr2 on your computer. Then locate the "ErwinJr2" folder and copy these 6 files into that directory, overwriting the previous five files (Material.py, QCLayers.py, QCPlotter.py, QuantumTab.py, rFittings.py). Lastly, run the "acej-qcl-layer_10-lwrandom-v23.py" script using Python. The "summary-fomstar-3lu-eVmiddle-19.csv" file is generated after running the laser transition code, with all of the data collected for one structure at many electric fields. Running the script various times will generate random structures with the same electric field range. Joining these "summary" .csv files makes a QCL dataset.acej-qcl-layer_10-lwrandom-v23.py, Material.py, QCLayers.py, QCPlotter.py, QuantumTab.py, rFittings.p

MLP neural network trained on the QCL [-5, +20] Å dataset

This item contains two files. A multi-layer perceptron (MLP) neural network is built using the MATLAB Deep Network Designer (.m file). It imports a quantum cascade laser (QCL) dataset and splits it into 70% training, 15% validation, and 15% testing subsets. The network consists of an input layer, three hidden layers (each having a normalization and activation layer), and a regression output layer. All of the layers are fully connected, and the root-mean-square error (RMSE) is used to evaluate the accuracy of the network. An algorithm is trained on the [-5, +20] QCL dataset using 50 neurons, ReLU activation function, solver Adam, 0.001 learning rate, over 50 epochs, and is saved to be used in the prediction of figure of merit values for QCL designs (.mat file).adam_relu_hidden_3_neuron_50_epoch_50_net.mat, mlp_reg_train.m, README.tx

QCL-layer_10-4rep-rand-m2A_3A-efield_0-10-150-v22-dataset

The method for building QC datasets and identifying the laser transition for a design is referenced in [1] A. C. Hernandez, M. Lyu and C. F. Gmachl, "Generating Quantum Cascade Laser Datasets for Applications in Machine Learning," 2022 IEEE Photonics Society Summer Topicals Meeting Series (SUM), 2022, pp. 1-2, doi: 10.1109/SUM53465.2022.9858281.This dataset contains 1800 quantum cascade (QC) structures generated by randomly modifying an initial 10-layer design in the tolerance range of -2 to +3 Angstroms at an applied electric field range of 0 to 150 kV/cm (in 10 kV/cm increments). One structure at one electric field is one design, thus there are 27000 unique designs, represented as a row in the dataset. The layer thicknesses (in angstroms) and the electric field are inputs which get evaluated using a Schrödinger solver, ErwinJr2, to identify the laser transition Figure of Merit (fom*), among other reported outputs.Schmidt DataX Fund at Princeton University, National Science Foundation under Grant No. DGE-2039656, and the Center for Statistics and Machine Learning at Princeton University through the support of MicrosoftQCL-layer_10-4rep-rand-m2A_3A-efield_0-10-150-v22-dataset.csv, README.tx

MLP neural network trained on the QCL [-2, +3] Å dataset

Details on building machine learning algorithms for QCL design can be found in [1] and the [-2, +3] Å QCL dataset can be found in [2]. References: [1] A. C. Hernandez and C. F. Gmachl, “Application of Machine Learning to Quantum Cascade Laser Design,” in 2023 57th Annual Conference on Information Sciences and Systems, CISS 2023, 2023. doi: 10.1109/CISS56502.2023.10089756. [2] A. Correa Hernandez, C. F. Gmachl, and M. Lyu, “QCL Dataset, 10 Layer Structure, Tolerance [-2, +3] A, Electric Field [0,10,150] kV/cm.” Princeton University, Princeton, 2023. doi: https://doi.org/10.34770/r7nr-ee50.This item contains two files. A multi-layer perceptron (MLP) neural network is built using the MATLAB Deep Network Designer (.m file). It imports a quantum cascade laser (QCL) dataset and splits it into 70% training, 15% validation, and 15% testing subsets. The network consists of an input layer, three hidden layers (each having a normalization and activation layer), and a regression output layer. All of the layers are fully connected, and the root-mean-square error (RMSE) is used to evaluate the accuracy of the network. An algorithm is trained on the [-2, +3] QCL dataset using 50 neurons, ReLU activation function, solver Adam, 0.001 learning rate, over 150 epochs, and is saved to be used in the prediction of figure of merit values for QCL designs (.mat file).mlp_reg_train.m, adam_relu_hidden_3_neuron_50_epoch_150_net.ma