Abstract
Scientific Reports 2023, 13, 3352
Using machine learning with optical profilometry for GaN wafer screening
Gallagher JC, Mastro MA, Ebrish MA, Jacobs AG, Gunning BP, Kaplar RJ, Hobart KD, Anderson TJ
To improve the manufacturing process of GaN wafers, inexpensive wafer screening techniques are required to both provide feedback to the manufacturing process and prevent fabrication on low quality or defective wafers, thus reducing costs resulting from wasted processing effort. Many of the wafer scale characterization techniques—including optical profilometry—produce difficult to interpret results, while models using classical programming techniques require laborious translation of the human-generated data interpretation methodology. Alternatively, machine learning techniques are effective at producing such models if sufficient data is available. For this research project, we fabricated over 6000 vertical PiN GaN diodes across 10 wafers. Using low resolution wafer scale optical profilometry data taken before fabrication, we successfully trained four different machine learning models.