Neuromorphic Computing with Memcapacitors: Advancements, Challenges, and Future Directions

Abstract

Modern applications demand immense data processing and computational power, yet conventional architectures, constrained by the Von Neumann bottleneck and data presentation, struggle to meet these requirements. This has driven the rise of neuromorphic computing, which mimics the biological nervous system through spike-encoded data and threshold-based computations for high energy efficiency. However, traditional hardware (CMOS transistors) designed for continuous computations fails to harness this potential fully, necessitating specialized neuromorphic hardware alternatives. Memristors have emerged as key components for neuromorphic hardware but suffer from high static power consumption, sneak-path currents, and reliance on selector devices. In contrast, memcapacitors provide a more efficient alternative, leveraging high resistance and charge-domain computations to overcome these limitations. This review presents a comprehensive analysis of memcapacitors for neuromorphic applications, covering capacitive switching mechanisms and materials, key hardware considerations, and recent advancements. It explores their role in artificial synapses, physical reservoir computing, and crossbar-based accelerators, highlighting their potential for scalable and low-power neuromorphic systems. Finally, key challenges and future research directions are discussed, particularly in materials engineering, device fabrication, and large-scale system integration, positioning memcapacitors as promising candidates for next-generation neuromorphic computing.