We study coherent laser arrays operating in epitaxially grown semiconductor membrane quantum wells. The samples are deposited by transfer on substrates of oxidized silicon and we record the real and reciprocal space of the laser emission. The Laser arrays are in a lateral emission geometry and are waveguides lasers where the end mirrors are the end-facets of the cleaved membranes which usually form cavities in the order of 100 microns. We are able to create waveguide laser arrays with modal widths of approximately 5-10 microns separated by 10-20 microns. We use real and reciprocal space imaging to examine the emission characteristics of the lasing cavity. Remarkably, we discover that the mutual coherence is preserved whether the cavity operates on a single longitudinal mode or multiple modes. We will show how their emission and coherence can be controlled using a digital micromirror device to control the position and shape of the pump illumination studying threshold, coherence and frequency.
KEYWORDS: Artificial neural networks, Spectroscopy, Refractive index, Terahertz spectroscopy, Education and training, Data modeling, Ultrafast phenomena, Ultrafast lasers, Terahertz radiation, Signal to noise ratio
Terahertz Time-Domain Spectroscopy (THz-TDS) uses ultrafast lasers to emit and detect broadband, picosecond pulses with excellent signal-to-noise ratios and rapid data acquisition. Commercial spectrometers have become available and there is now great access to the technology. However, the data analysis remains complex and prone to errors due to multiple processing steps and variation in experimental setups, hindering its true breakout into industry. Machine learning, particularly the training of artificial neural networks with simulated data, has proven effective in various spectroscopic techniques, including refractive index extraction with THz-TDS. This approach allows controlled inclusion of analytical and experimental errors, enabling performant networks that are easier to characterize. We explore the use of deep neural networks for complex refractive index prediction that account for experimental and analytical errors, such as laser drift, compensating for imperfect experimental data and potentially superseding current extraction methods.
We present coherent laser arrays in a silicon photonics compatible waveguide geometry in optically pumped semiconductor membrane quantum well lasers (MQWLs) on oxidised silicon and silicon carbide substrates. Real and reciprocal space imaging is used to investigate the emission of the laser arrays and mutual coherence is seen to be maintained while operating on single and multiple longitudinal modes in each cavity. Further, we investigate writing laser cavity arrays through micro-structuring of the MQWL and also through the utilisation of a spatial light modulator (SLM) to define areas of gain in the MQWL by shaping the pump beam.
KEYWORDS: Terahertz spectroscopy, Spectroscopy, Data modeling, Artificial neural networks, Refractive index, Data acquisition, Signal to noise ratio, Nondestructive evaluation, Neural networks, Material characterization
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