Key Takeaways & Executive Findings
- •• • 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.
Abstract
The von Neumann architecture is increasingly constrained by energy consumption and data-transfer efficiency as artificial intelligence and data-intensive applications expand. Neuromorphic computing, inspired by the human brain's information-processing mechanisms, offers an alternative paradigm. Two-dimensional (2D) ferroelectric materials are promising candidates due to their intrinsic non-volatility, atomic-scale thickness, ultra-low power consumption, excellent fatigue endurance, and dangling-bond-free surfaces. This review examines recent advances in 2D ferroelectric materials and associated device architectures for neuromorphic applications. It first introduces ferroelectric mechanisms and representative 2D ferroelectrics, then surveys key device architectures including ferroelectric tunnel junctions, diodes, transistors, and photovoltaic devices. Their applications in in-memory computing and in-sensor neuromorphic systems are discussed, with emphasis on artificial neural networks, spiking neural networks, reservoir computing, and neuromorphic perception for efficient information processing and intelligent sensing. The unique properties of 2D ferroelectrics enable integrated sensing, memory, and computing functionalities, demonstrating potential for future neuromorphic and brain-inspired intelligent systems.
1. Introduction
Conventional von Neumann architectures separate memory and processing units, leading to energy-intensive data transfers and latency that impede artificial intelligence and big data applications. The rapid growth of autonomous driving, the Internet of Things, and edge computing exacerbates these limitations, as massive data volumes must be processed under strict power and thermal constraints. Neuromorphic computing, inspired by the brain's co-located memory and processing, promises orders-of-magnitude efficiency gains, but its hardware realization demands materials that combine non-volatility, scalability, and low-power operation.
Two-dimensional ferroelectrics have emerged as a compelling solution due to their atomic-scale thickness, intrinsic non-volatility, and dangling-bond-free surfaces, which enable ultra-low power consumption and excellent fatigue endurance. This review systematically examines recent advances in 2D ferroelectric materials and their device architectures—ferroelectric tunnel junctions, diodes, transistors, and photovoltaic devices—for in-memory and in-sensor neuromorphic computing. By integrating sensing, memory, and computing functionalities, these materials address the data-transfer bottleneck and open pathways for efficient artificial neural networks, spiking neural networks, reservoir computing, and neuromorphic perception, with direct implications for intelligent sensing and brain-inspired systems.
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ZHANG Qirui, CAO Guiming, PAN Er, YANG Fan, WANG Xuemei, CHEN Jiangang, WEN Zhixing, LIU Qing, LUO Xiao, LIU Fucai (2026). In-Memory and In-Sensor Neuromorphic Computing with 2D Ferroelectrics. SCIENCE CHINA Materials. https://doi.org/10.1007/s40843-026-4479-8
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Frequently Asked Questions
What are the primary failure mechanisms of 2D ferroelectric devices under prolonged electrical stress, and how do they impact neuromorphic computing reliability?
Prolonged electrical stress can lead to ferroelectric fatigue, imprint, and retention loss. While 2D ferroelectrics exhibit excellent fatigue endurance, domain pinning and defect migration at interfaces remain concerns. For neuromorphic computing, stable multi-bit conductance states are critical; degradation beyond 10^12 cycles or retention loss below 10 years at 85°C would compromise weight updates and inference accuracy. Mitigation strategies include interface engineering and encapsulation to reduce defect density.
How do the energy consumption and latency of 2D ferroelectric neuromorphic devices compare to conventional CMOS-based implementations?
2D ferroelectric devices can achieve ultra-low power consumption due to non-volatile operation and low leakage currents, potentially reducing energy per synaptic operation to femtojoule levels, compared to picojoule levels in CMOS. Latency is minimized by in-memory computing, eliminating data transfer delays. However, exact figures depend on device scaling and circuit architecture; experimental demonstrations show promising sub-nanosecond switching, but system-level benchmarks are still needed.
What are the scalability bottlenecks for integrating 2D ferroelectric materials into commercial neuromorphic chips?
Key bottlenecks include wafer-scale synthesis of uniform 2D ferroelectric films, precise thickness control at atomic scale, and compatibility with back-end-of-line (BEOL) CMOS processes. Transfer techniques may introduce defects and contamination. Additionally, variability in ferroelectric domains can lead to device-to-device performance mismatch, requiring advanced calibration. Current research focuses on large-area growth methods like MOCVD and ALD to address these issues.
Can 2D ferroelectric devices meet the endurance and retention requirements for automotive and industrial applications?
Automotive and industrial applications typically require endurance >10^12 cycles and retention >10 years at temperatures up to 125°C. While 2D ferroelectrics have shown endurance up to 10^12 cycles in lab settings, retention at high temperatures is limited by depolarization fields and thermal instability. Doping and strain engineering can enhance polarization stability, but comprehensive reliability testing under accelerated aging conditions is necessary before qualification.
What are the cost implications of adopting 2D ferroelectric materials compared to established ferroelectric technologies like HfO2-based devices?
2D ferroelectrics offer potential cost advantages through simplified device structures and lower processing temperatures, but current synthesis costs are high due to limited large-scale production. HfO2-based ferroelectrics are CMOS-compatible and already in production, giving them a cost edge. However, 2D materials enable new functionalities like in-sensor computing that could reduce overall system costs by eliminating separate sensors and processors. Economies of scale will determine long-term cost parity.
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