Choe, GihunGihunChoeRavindran, Prasanna VenkatesanPrasanna VenkatesanRavindranHur, Jae H.Jae H.HurLederer, MaximilianMaximilianLedererReck, AndréAndréReckKhan, Asif IslamAsif IslamKhanYu, ShimengShimengYu2023-10-162023-10-162023https://publica.fraunhofer.de/handle/publica/45177010.1109/TED.2023.32447642-s2.0-85149386545A novel machine learning (ML)-assisted approach is proposed for investigating the variability of ferroelectric field-effect transistor (FeFET) to shorten the loop of technology pathfinding. To quantify the ferroelectric (FE) domain variation, the atomic intragranular misorientation of Si-doped HfO2 thin film is measured by transmission Kikuchi diffraction (TKD) and is transformed into a polarization map. With the metrology data, polarization variation (PV) of FE domains on the gate-stack is modeled in technology computer-aided design (TCAD) to assess the impact of PV on the FeFET performance and to obtain datasets for ML-assisted analysis. A neural network model is trained using the datasets (input: polarization maps; output: high/low threshold voltage, ON-state current, and subthreshold slope) for the 28-nm bulk FeFET analysis. Our trained network, if used for inference to obtain three-sigma statistics, shows >98% of accuracy of the device features and significantly faster simulation time than TCAD. In addition, we used the transfer learning technique to reduce the number of training datasets by 83% for the fully depleted silicon-on-insulator (FDSOI) FeFET by applying the pretrained model from the bulk FeFET.enFerroelectric (FE) transistormachine learning (ML)neural networktechnology-computer-aided-design (TCAD)transfer learning (TL)variationjournal article