Accurately determining numerical values for key model parameters for any semiconductor devices is extremely important for analyzing the device characteristics and model-based device design optimization. However, their experimental determination can be very difficult since measurement results involve interaction of many parameters and isolating the influence of a single parameter is often not possible. One of the ways to solve this issue is deep learning. We achieve accurate determination of key laser diode model parameters such as internal loss, Auger coefficient, and free-carrier absorption coefficient of a fabricated ridge-waveguide 850 nm GaAs/AlGaAs laser diode(LD) applying the trained deep neural network (DNN). We use a LD TCAD simulator, PICS3D, for producing training and testing data. The accuracy of our approach is confirmed by comparing the simulation result with the actual measurement result for the LD L-I characteristics using extracted model parameters by DNN.
In this study, we present evaluation results of the 905nm pulse laser diode that has power of over 140W adopting 4stack epitaxy structure with 200um×15um emitter size for autonomous vehicle lidar and other lidar applications. The 4stack epitaxy structure was composed of AlGaAs/InGaAs composition and tunnel junction with GaAs and grown by MOCVD. As a results of the characteristic evaluation, 905nm pulse laser diode with 4stack epitaxy obtained an output of about 149.6W under the conditions of 1KHz cycle, 0.01% duty, and 40A input current. Also developed 905nm pulse laser diode achieved an operating voltage of 13V, a horizontal angle of 9.3°, a vertical angle of 29.1°, and peak wavelength of 905.2nm with TO-56 package respectively.
We present evaluation results of the 940nm 400mW transverse single-mode laser diodes (LDs) with real reflective index self-aligned (RISA) structure based on graded index separate confinement hetero structures (GRIN-SCH) for a three-dimensional (3D) depth sensor. The AlGaAs/InGaAs laser diodes that are adopted with RISA structure have many advantages over conventional complex refractive index guided lasers, what include low operating current, high temperature operation and stable fundamental transverse-mode operation up to high power levels.
Simultaneously, the RISA process is easy to control the waveguide channel width and does not require stable oxide mask for the regrowth of aluminum alloys, so it is possible to manufacture high output power and high reliability laser diodes.
At the optical power 400mW under the continuous-wave (CW) operation, Gaussian narrow far-field patterns (FFP) are measured with the full-width at half-maximum vertical divergence angle of 23°. A threshold current (Ith) of 33mA, slope efficiency (SE) of 0.81mW/mA and operating current (Iop) of 503mA are obtained at room temperature. Also, we could achieve catastrophic optical damage (COD) of 657mW and long-term reliability of 60°C with TO-56 package.
In this paper, we report the results of our investigation about 940nm AlGaAs/InGaAs single mode laser diodes adopting graded index separate confinement hetero structures (GRIN-SCH) and p, n-clad asymmetric structures with improved temperature and small divergence beams characteristics under the high output power operation for a 3D motion recognition sensors. The GRIN-SCH design provides good carrier confinement and prevents current leakage by adding a grading layer between clad and waveguide layers. In addition, the dopant concentration of the cladding layer is optimized to reduce resistance and internal loss. At the optical power 300mW, measured average values of threshold current (Ith), operating current (Iop), slop efficiency (SE), operating voltage (Vop), peak wavelength (λ) are 80mA, 352mA, 1.12mW/mA, 1.87V, 940nm respectively. Also, we could obtain catastrophic optical damage (COD) of 750mW and excellent long-term reliability characteristic 60°C with TO-56 package. From the experimental measurement results, the developed 940nm high power laser diode is suitable optical source for the sensor applications including 3D motion recognition sensors.
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