Ferroelectric HfZrO 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 HfZrO (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.