• • 2D ferroelectrics exhibit intrinsic non-volatility and atomic-scale thickness, enabling ultra-low power consumption and high fatigue endurance; these properties directly address the von Neumann bottleneck's energy and data-transfer inefficiencies, critical for edge AI and IoT devices where battery life and thermal budgets are constrained.
• • Ferroelectric tunnel junctions, diodes, transistors, and photovoltaic devices are identified as key architectures; their integration into in-memory and in-sensor computing allows for artificial neural networks, spiking neural networks, and reservoir computing, reducing latency and energy overhead associated with frequent memory-processor data shuttling.
• • The dangling-bond-free surfaces of 2D ferroelectrics facilitate heterogeneous integration with other materials, potentially enabling monolithic 3D integration of sensing, memory, and computing units; this is essential for scaling neuromorphic hardware beyond current CMOS limitations.
• • Neuromorphic perception using 2D ferroelectrics offers efficient information processing and intelligent sensing, with potential applications in autonomous driving and real-time edge analytics; however, industrial adoption requires demonstration of wafer-scale uniformity, endurance beyond 10^12 cycles, and retention exceeding 10 years at elevated temperatures.