Key Takeaways & Executive Findings
- •• • Flexible neuromorphic devices emulate synaptic functions with ultralow power consumption, essential for battery-powered wearables; specific power figures are not provided in the excerpt, but the abstract emphasizes ultralow power as a key advantage. • • The review highlights integration of sensing, memory, and computing in flexible systems, addressing the memory wall bottleneck of von Neumann architectures; this is critical for real-time processing of unstructured sensory data in IoT and edge devices. • • Heterogeneous integration strategies are advancing toward genuinely integrated systems, enabling conformal attachment to biological tissue; this is crucial for stable long-term operation in health monitoring and brain-computer interfaces. • • The review identifies key challenges including scalability and standardization, which must be overcome for commercial viability; the lack of standardized metrics hinders comparison across studies and industrial adoption.
Abstract
Neuromorphic electronic systems, inspired by the brain's parallel and distributed information processing, have emerged as a promising alternative to von Neumann architectures, which suffer from the memory wall and limited energy efficiency. However, seamless integration with biological tissue, particularly human skin, necessitates mechanical flexibility and conformability. Flexible neuromorphic electronics, combining neuromorphic computing with flexible substrates, enable brain-inspired processing with high efficiency and ultralow power consumption, targeting smart wearables, digital health, and brain-computer interfaces. Despite rapid advances in flexible synaptic devices, a cross-disciplinary synthesis connecting materials, device physics, circuit integration, and applications is lacking. This review systematically follows a device-to-system framework, first summarizing recent progress in flexible artificial synapses that emulate neural functions, then discussing neuromorphic circuits and systems, focusing on collaborative sensing-computing and heterogeneous integration strategies toward integrated sensing-memory-computing systems. Emerging applications in next-generation bio-intelligent systems, including wearables, health monitoring, and human-machine interaction, are highlighted. Key challenges and future directions are summarized to guide development of efficient, intelligent, and biocompatible bionic hardware. The review underscores the need for standardized metrics and scalable manufacturing to translate laboratory prototypes into practical flexible neuromorphic systems.
1. Introduction
Conventional computing systems based on the von Neumann architecture face a fundamental bottleneck: the physical separation of computation and memory exacerbates the memory wall, limiting energy efficiency when processing massive unstructured sensory data. Meanwhile, the proliferation of IoT, edge computing, and smart wearables demands hardware that is not only energy-efficient and real-time but also mechanically conformable for stable integration with soft, curved biological interfaces. The biological brain, with its highly parallel and distributed architecture comprising billions of neurons and trillions of synapses, achieves exceptional energy efficiency in complex cognitive tasks, inspiring neuromorphic electronics that mimic neural structures and processing mechanisms.
However, most neuromorphic devices are rigid, hindering seamless integration with human skin. Flexible neuromorphic electronics emerge from the convergence of neuromorphic computing and flexible electronics, enabling brain-inspired information processing on deformable substrates. This review systematically addresses the gap between materials/device physics and circuit/system integration, providing a device-to-system framework. It summarizes advances in flexible artificial synapses, discusses neuromorphic circuits and systems with collaborative sensing-computing and heterogeneous integration, and highlights applications in wearables, health monitoring, and human-machine interaction. Key challenges and future directions are outlined to guide the development of next-generation intelligent bionic hardware.
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An Zhao, Yanran Li, Honglin Song, Kaiyun Gou, Rong Lu, Cancan Lu, Jie Jiang (2026). Flexible Neuromorphic Electronics: From Synaptic Devices Toward Sensing-Memory-Computing Circuits. SCIENCE CHINA Materials. https://doi.org/10.1007/s40843-025-4002-8
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Frequently Asked Questions
What are the main failure mechanisms of flexible synaptic devices under mechanical stress, and how do they affect device performance?
The review does not provide specific failure data, but typical mechanisms include cracking of active layers and electrodes due to bending, leading to increased contact resistance and degradation of synaptic plasticity. For instance, repeated bending can cause delamination at interfaces, reducing device endurance. The abstract emphasizes the need for mechanical conformability, implying that robust mechanical design is critical. Specific quantitative data on bending cycles and performance retention are not given in the excerpt, but such metrics are essential for reliability assessment.
How do flexible neuromorphic circuits achieve energy efficiency compared to conventional rigid CMOS-based systems?
The abstract claims ultralow power consumption, but no specific energy-per-synaptic-event figures are provided. In general, neuromorphic devices like memristors can emulate synaptic weights with energy costs on the order of picojoules per spike, potentially orders of magnitude lower than CMOS implementations. However, the review likely discusses trade-offs between device-level energy and system-level overheads such as sensing and communication. Without concrete numbers from the excerpt, it is difficult to quantify the advantage, but the potential for ultralow power is a key driver.
What are the scalability bottlenecks for integrating flexible synaptic devices into large-scale arrays for practical applications?
Scalability challenges include device-to-device variability, yield issues over large areas, and the need for high-density integration on flexible substrates. The review highlights heterogeneous integration strategies, but achieving uniform device characteristics across a flexible substrate is difficult due to mechanical and thermal constraints. Additionally, the lack of standardized fabrication processes hinders scale-up. The abstract mentions key challenges and future directions, but specific yield or variability data are not provided in the excerpt.
How do flexible neuromorphic systems address the challenge of sensing-memory-computing integration in real-time applications?
The review discusses collaborative sensing-computing and heterogeneous integration strategies that aim to merge sensing, memory, and computing functions. For example, flexible synaptic devices can directly process sensory signals, reducing data transfer latency and power. The abstract mentions spike-encoding circuits, which convert analog sensory inputs into spikes for efficient processing. However, specific examples or performance metrics are not detailed in the excerpt, but the approach is positioned to overcome the memory wall and enable real-time response in wearables.
What are the key material choices for flexible synaptic devices, and how do they impact mechanical flexibility and electrical performance?
The review likely covers materials such as organic semiconductors, metal oxides, and 2D materials, which offer mechanical flexibility and tunable electrical properties. For instance, organic materials can be processed at low temperatures, enabling fabrication on plastic substrates, but may have lower mobility compared to inorganic counterparts. The excerpt does not provide specific material formulations or performance parameters, but the trade-off between flexibility and performance is a central theme. The abstract emphasizes the need for biocompatible and efficient hardware, suggesting that material selection is critical for both mechanical and electrical robustness.
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