A thermally optimizing method of thin film resistor trimming with machine learning

This paper proposes a novel trimming method of thin-film re-sistors dedicated to the power modules. Thin-film resistors are utilized for applications including snubber circuits which avoids unwanted ringing appearing in the output voltage of power transistors. Our previous works have revealed that the applicable voltage of a NiCr thin-film resistor is thermally limited, and then the indispensable trimming process affects the degree of temperature rise in use. In this paper, we first formulate the rela-tionship between the target resistance value and the trim dimensions using machine learning. With the obtained equation, we propose a new trimming method, which enables the trimmed pattern to reduce the variability of the maximum temperature rise. The experimental results show that the pro-posed trimming method can suppress the estimated range of the maximum temperature from 619 K to 179 K.