Ferroelectric materials have recently shown great promise in low-energy non-volatile memory and neuromorphic computing applications. This dissertation aims to develop a comprehensive framework for the performance analysis of ferroelectric-based memory systems, encompassing from device modeling to system-level analysis. Initially, a computationally efficient phase-field physics-based compact model for ferroelectric capacitors is developed. The model self-consistently solves the time-dependent Landau-Ginzburg (TDGL) and Poisson's equations to capture polarization dynamics. Analytical equations for the time-dependent kinetic coefficient and voltage-dependent gradient energy coefficient are derived, which are crucial for accurately modeling the transient characteristics of ferroelectric capacitors. This framework is then extended for ferroelectric, antiferroelectric, and dielectric mixed phase capacitors based on Kittel's two sublattice theory. It allows the model to capture endurance effects due to phase evolution during cycling and the effect of depolarization field due to the presence of dielectric phases. The developed models are calibrated with experimental results for low switching voltage ferroelectric materials for circuit level analysis. A comprehensive analysis is conducted on ferroelectric random access memory (FERAM) circuit arrays, examining the impact of various design parameters. The performance of these memory arrays is then compared to other competing memory technologies, particularly magnetic memories, in terms of read/write latency and energy consumption. Finally, the dissertation discusses a framework for system level analysis under real workloads, exploring the potential of using FERAM as main memory.