Analysis of a Memcapacitor‐Based Online Learning Neural Network Accelerator Framework - Ankur Singh; Dowon Kim; Byung‐Geun Lee (Advanced Intelligent Systems, 2025)
Data‐intensive computing tasks, such as training neural networks, are fundamental to artificial intelligence applications but often demand substantial energy resources. This study presents a novel complementary metal‐oxide‐semiconductor (CMOS)‐based memcapacitor framework designed to address these challenges by enabling efficient and robust neuromorphic computing. Utilizing memcapacitor devices, a crossbar array that performs parallel vector‐matrix multiplication operations, validated through cadence simulations and implemented in python for scalable accelerator design, is developed. The framework demonstrates outstanding performance across classification tasks, achieving 98.4% accuracy in digit recognition and 85.9% in object recognition. A key aspect of this research is its focus on real‐world fabrication nonidealities, including up to 30% device parameter variations, ensuring robustness and reliability under practical deployment conditions. The results emphasize the effectiveness of capacitance‐based systems in handling classification tasks while demonstrating resilience to fabrication‐induced variations. This work establishes a foundation for scalable, energy‐efficient, and robust memcapacitor‐based neural networks, advancing the potential for intelligent systems in artificial intelligence‐driven applications and paving the way for future innovations in neuromorphic computing.