Kocak, MuratMuratKocakMitard, JeromeJeromeMitardPerini, LorenzoLorenzoPeriniNaskali, Ahmet TeomanAhmet TeomanNaskaliDavis, JesseJesseDavis2026-09-032026-09-0320260952-1976https://imec-publications.be/handle/20.500.12860/60211Current approaches for performing electrical characterization are too slow to meet the demands of modern semiconductors. This problem is exacerbated as the complexity of the chip designs increases. We present a machine learning based methodology for performing the characterization more efficiently by avoiding redundant drain current - gate voltage (𝐼𝑑−𝑉𝑔) measurements. The proposed methodology combines physics informed Gaussian Process Regression (PI-GPR) and Active Sampling (AS) techniques to dynamically determine a limited but representative subset of 𝐼𝑑−𝑉𝑔 points using prior knowledge. The value of the proposed method was verified through a variety of device designs, maskset, and process variations of the complementary field effect transistor architecture; a total of 1000 devices were tested. We show that the proposed PI-GPR is significantly faster than standard (cold start) GPR in all tested cases and speeds up the conventional characterization process by six times while maintaining threshold voltage accuracy within a maximum absolute error of 25 millivolts, and subthreshold slope within a maximum absolute error of 25 millivolts per decade.engJournal article10.1016/j.engappai.2026.115498WOS:001808756500001INFORMED NEURAL-NETWORKSOPTIMIZATION