Abstract Hafnium (Hf) oxide-based ferroelectric materials have emerged as a transformative platform for next-generation non-volatile memory and advanced computing technologies. This review comprehensively examines the development, challenges, and applications of HfO 2 ferroelectrics, emphasizing their CMOS compatibility, scalability, and robust polarization at nanoscale dimensions. Breakthroughs in doping strategies, stress engineering, and VO control have stabilized the metastable orthorhombic phase, enabling high-performance devices such as ferroelectric RAM (FeRAM), ferroelectric field-effect transistors (FeFETs), and ferroelectric tunnel junctions (FTJs). These devices offer ultrafast switching, low power consumption, and multi-level storage, driving innovations in neuromorphic computing, in-memory processing, and cryogenic systems; nonetheless, they face ongoing challenges in reliability, such as fatigue and imprint effects, and scalability at sub-5 nm technology nodes. Emerging frontiers, such as wurtzite-structured nitrides (e.g., AlScN) and antiferroelectric ZrO 2 -based systems, have garnered significant attention due to their exceptionally high remanent polarization and promising potential for enhanced endurance, respectively. Further addressing the reliability issues of these emerging ferroelectric materials and the challenges associated with large-scale integration processes through interdisciplinary efforts will unlock the full potential of ferroelectric technologies, positioning them as pivotal enablers of post-Moore computing architectures and sustainable AI-driven applications.

Edge devices deployed in unsupervised scenarios employ Physical Unclonable Functions (PUFs) for identity authentication and embedded XOR encoding for data encryption. However, on the one hand, the existing strong PUFs such as CMOS-based XOR Arbiter PUFs and NVM-based RRAM PUFs are vulnerable to various machine learning (ML) modeling attacks. On the other hand, the transmission of keys for embedded XOR encoding also faces the risk of being eavesdropped in unsecured channels. In response to these challenges, this paper proposes a high-security In-Memory Computing and Encrypting (IMCE) hardware architecture based on a FeFET macro, featuring both a PUF mode for identity authentication and an encrypted CIM mode with in-situ decryption. The PUF mode ensures a prediction accuracy close to 50% (equivalent to random guessing attack) under various ML models due to the proposed Hamming distance comparison used in challenge-response pairs (CRPs) generation. In addition, by utilizing the CRPs generated in PUF mode as encryption keys, the CIM mode of IMCE achieves robust security through public-key cryptography via CRPs-masked key transfer, preventing the leakage of keys and data. Therefore, by applying a novel CRPs generation scheme and reusing the generated CRPs for in-situ CIM decryption, the security of both PUF and encrypted CIM mode is enhanced concurrently. In addition, IMCE significantly reduces the power overhead thanks to the high energy efficiency of ferroelectric FETs (FeFETs), making it highly suitable for secure applications in edge computing devices.