Physics-based compact models for emerging non-volatile memories (NVMs) are often limited by the complex interactions of microscopic domains and defects that are difficult to capture analytically, resulting in reduced accuracy and simulation efficiency. To address this challenge, a machine learning (ML)-based approach is proposed using artificial neural networks (ANNs) trained entirely on device measurement data, enabling a direct translation of fabrication characteristics into SPICE-compatible circuit models. The resulting models achieve high accuracy (MSE: 0.724, adjusted R² : 0.998), significantly outperforming physics-based baselines with an 18× lower MSE for polarization and a two-order-of-magnitude precision improvement in FeFET current simulation, while accurately capturing the wake-up process. Furthermore, the model demonstrates robust out-of-distribution (OOD) extrapolation to unseen ferroelectric thicknesses and a 33.7% improvement in simulation speed. These results validate the ML-based approach as a highly efficient, SPICE-compatible solution for next-generation memory.

The thesis is a urban-research-based architectural design in Alamar, Havana, Cuba. Due to the special historical condition and cultural background of Cuba, the intervention into the urban context is generated from the analysis and anatomy of the specific community in Alamar. The design is a vocational school of urban farming, which is a sector representative not only in Alamar but also Cuba. The school is trying to connect the urban farming sector and the younger generation while being a helpdesk for the community. By serving both students and residents, the knowledge and spirits of Cuban people and urban farming sector is spread to the public and the yonger generation, stimulating the city to become more dynamic and better in the future.