Trim Time Reduction in Analog/RF ICs Based on Inter-Trim Correlation

Post-fabrication performance calibration, a.k.a. trimming, is an essential part of analog/RF IC manufacturing and testing. Its objective is to counteract the impact of process variations by individually fine-tuning the performance parameters of every fabricated chip so that they meet the design specifications and, thereby, to ensure both high yield and high performance. The prevalent trimming process currently employed in industry involves a search algorithm which consists of repeated digital trim-code selection and measurement in order to optimize the trimmed performance. With hundreds of trims commonly performed on contemporary analog/RF chips, this process becomes overly expensive. In this work, we discuss a machine learning-based approach that ameliorates this problem by leveraging inter-trim correlation. Specifically, our method relies on effectively trained regression models which use the measurements obtained through an intelligently selected and conventionally performed subset of trims, in order to accurately predict the optimal trim codes for the omitted trims. Thereby, as corroborated using data from an actual analog/RF IC currently in production, trim time can be drastically reduced without significantly affecting the accuracy of the selected trim codes.