A Load-Adaptive Driving Method for a Quasi-Continuous-Wave Laser Diode.
Micromachines (Basel)
; 15(3)2024 Feb 29.
Article
in En
| MEDLINE
| ID: mdl-38542601
ABSTRACT
A quasi-continuous-wave (QCW) laser diode (LD) driver is commonly used to drive diode bars and stacks designed specifically for QCW operations in solid-state lasers. Such drivers are optimized to deliver peak current and voltage pulses to LDs while maintaining low average power levels. As a result, they are widely used in laser processing devices and laser instruments. Traditional high-energy QCW LD drivers primarily use capacitors as energy storage components and pulsed constant-current sources with op-amps and power metal-oxide-semiconductor field-effect transistors (MOSFETs) as their core circuits for generating repeated constant-current pulses. The drawback of this type of driver is that the driver's output voltage needs to be manually adjusted according to the operating voltage of the load before use to maximize driver efficiency while providing a sufficient current. Another drawback is its inability to automatically adjust the output voltage to maintain high efficiency when the load changes during the driver operation. Drastic changes in the load can cause the driver to fail to function properly in extreme cases. Based on the above traditional circuit structure, this study designed a stability compensation circuit and realized a QCW LD driver for driving a GS20 diode stack with a maximum repetition rate of 100 Hz, a constant current of approximately 300 A, a load voltage of approximately 10 V, and a pulse width of approximately 300 µs. In particular, a high-efficiency, load-adaptive driving method was used with the MOSFETs in the critical saturation region (i.e., between the linear and saturated regions), controlling its power loss effectively while achieving maximum output current of the driver. The experiments demonstrated that the driver efficiency could be maintained at more than 80% when the load current varied from 50 to 300 A.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Micromachines (Basel)
Year:
2024
Document type:
Article
Affiliation country:
China