Ferroelectric Hf_0.5Zr_0.5O₂ with Enhanced Intermediate Polarization: A Platform for Neuromorphic and Logic-in-Memory Computing

Ferroelectric materials, known for their nonvolatile and reversible polarization states, are emerging as promising candidates for innovative computing paradigms such as neuromorphic computing and logic-in-memory (LiM) architectures. Their polarization dynamics in response to external stimuli closely emulates biological synapses, a feature crucial for learning and adaptation in neural networks. Achieving multiple intermediate states between fully polarized states is critical for energy-efficient computation. However, the exploitation of switching properties for weight modulation in ferroelectric materials remain underexplored. In this study, we demonstrate improved intermediate and cumulative polarization levels in Hf_0.5Zr_0.5O₂ (HZO) through phase engineering. Consequently, HZO-based synaptic ferroelectric field-effect transistors (FeFETs) achieve a wide range of synaptic weights (up to 8 bits) with remarkable linearity, resulting in high classification accuracies of 98% for MNIST and 88% for Fashion-MNIST in neuromorphic computing tasks. Additionally, we present reconfigurable in-memory NOR and NAND logic functions along with 3-bit logic state generation using a multigate FeFET, demonstrating the potential for LiM operations. This work underscores the successful cointegration of neuromorphic and LiM computing functionalities within a unified platform, addressing key challenges in developing efficient and versatile computing architectures. Our findings highlight the potential of HZO to enable next-generation computing systems that seamlessly integrate learning and logic capabilities.