Chengkuo Lee’s research while affiliated with National University of Singapore and other places

Colonoscopic continuum robots often lack sensing capabilities, risking tissue damage. An ideal robot electronic skin should offer full‐body coverage, multiplexing, stretchability, and multifunctionality, but integration is challenging due to the robot's elongated structure. This work presents a stretchable electronic armor (E‐armor) with a 3D crosslinked structure that enables 300 mm full coverage while accomplishing multiplexed simultaneous tactile and strain sensing through bioinspired artificial synapse mechanisms. The E‐armor integrates 48 tactile sensing points through bilayer co‐electrode strategy, reducing wiring while combining triboelectric encoding intelligence with innovative stretchable triboelectric interlinked films (TIFs) to form a triboelectric artificial synapse that generates digitally encoded signal pairs upon contact. A convolutional neural network and long short‐term memory network (CNN‐LSTM) deep learning framework achieve 99.31% accuracy in identifying multi‐point tactile signals. A sodium alginate/polyacrylamide/sodium chloride (SA/PAM/NaCl) conductive hydrogel serves as a strain sensing element, providing excellent stretchability and biocompatibility, and allowing precise inference of bending angles at 12 strain sensing edges. A compliance control strategy coordinates tactile and strain signals to autonomously adjust continuum robot postures while ensuring smooth operation. The digital twin‐based 3D visualization interface enhances human–robot interaction by digitally reconstructing both tactile and strain feedback, enabling real‐time visualization of the continuum robot's intracolonic posture.

Optical sensors have undergone a significant evolution, transitioning from discrete optical microsystems toward sophisticated photonic integrated circuits (PICs) that leverage artificial intelligence (AI) for enhanced functionality. This review systematically explores the integration of optical sensing technologies with AI, charting the advancement from conventional optical microsystems to AI-driven smart devices. First, we examine classical optical sensing methodologies, including refractive index sensing, surface-enhanced infrared absorption (SEIRA), surface-enhanced Raman spectroscopy (SERS), surface plasmon-enhanced chiral spectroscopy, and surface-enhanced fluorescence (SEF) spectroscopy, highlighting their principles, capabilities, and limitations. Subsequently, we analyze the architecture of PIC-based sensing platforms, emphasizing their miniaturization, scalability, and real-time detection performance. The review then introduces the emerging paradigm of in-sensor computing, where AI algorithms are integrated directly within photonic devices, enabling real-time data processing, decision-making, and enhanced system autonomy. Finally, we offer a comprehensive outlook on current technological challenges and future research directions, addressing integration complexity, material compatibility, and data processing bottlenecks. This review provides timely insights into the transformative potential of AI-enhanced PIC sensors, setting the stage for future innovations in autonomous, intelligent sensing applications.

The rise of large-scale artificial intelligence (AI) models, such as ChatGPT, DeepSeek, and autonomous vehicle systems, has significantly advanced the boundaries of AI, enabling highly complex tasks in natural language processing, image recognition, and real-time decision-making. However, these models demand immense computational power and are often centralized, relying on cloud-based architectures with inherent limitations in latency, privacy, and energy efficiency. To address these challenges and bring AI closer to real-world applications, such as wearable health monitoring, robotics, and immersive virtual environments, innovative hardware solutions are urgently needed. This work introduces a near-sensor edge computing (NSEC) system, built on a bilayer AlN/Si waveguide platform, to provide real-time, energy-efficient AI capabilities at the edge. Leveraging the electro-optic properties of AlN microring resonators for photonic feature extraction, coupled with Si-based thermo-optic Mach–Zehnder interferometers for neural network computations, the system represents a transformative approach to AI hardware design. Demonstrated through multimodal gesture and gait analysis, the NSEC system achieves high classification accuracies of 96.77% for gestures and 98.31% for gaits, ultra-low latency (< 10 ns), and minimal energy consumption (< 0.34 pJ). This groundbreaking system bridges the gap between AI models and real-world applications, enabling efficient, privacy-preserving AI solutions for healthcare, robotics, and next-generation human–machine interfaces, marking a pivotal advancement in edge computing and AI deployment. Supplementary Information The online version contains supplementary material available at 10.1007/s40820-025-01743-y.

This review traces the evolution from classical to integrative plasmonics, detailing the optical multi-effects of plasmons and their acousto-optic-electric-thermal fusion. It discusses key integration mechanisms, applications, and future prospects.

Photonic neural networks (PNNs) based on silicon photonic integrated circuits (Si‐PICs) offer significant advantages over microelectronic counterparts, including lower energy consumption, higher bandwidth, and faster computing speeds. However, the analog nature of optical signal in PNNs makes Si‐PIC solutions highly sensitive to device noise, especially when using fixed‐value deterministic models, which are not robust to hardware fluctuation. Furthermore, current PNNs are unable to handle data uncertainty, a critical factor in applications such as autonomous driving, medical diagnostics, and financial forecasting. Herein, a photonic Bayesian neural network (PBNN) architecture that incorporates Bayesian principles to enhance robustness and address uncertainty is proposed. In the PBNN, device noise is leveraged through photonic‐noise‐based random number generators, which combine Mach‐Zehnder interferometers and micro‐ring resonators to independently control output mean and standard deviation. Based on modelling with experimentally extracted data, the PBNN achieves a classification accuracy of up to 98% for handwritten digit recognition, matching full‐precision models on conventional computers. Beyond classification, the PBNN excels in multimodal data processing, regression, and outlier detection. This scalable, energy‐efficient architecture transforms photonic noise into computational value, addressing the limitations of deterministic PNNs and enabling uncertainty‐aware computing for real‐world applications.

Photodetection technology is vital to the evolving information society, with two-dimensional (2D) materials photodetectors emerging as promising candidates due to their unique electronic and optical properties. Despite significant advancements, 2D material photodetectors still struggle with limitations in responsivity, detectable wavelength range, response time, and polarization sensitivity. To address these challenges, researchers are actively exploring the integration of ferroelectrics as functional materials with 2D material photodetectors. The synergy between 2D materials and ferroelectrics introduces unique working mechanisms and new possibilities for high-performance photodetectors. This article reviews the latest developments in 2D materials/ferroelectrics hybrid system photodetectors. It begins with an introduction to the fundamentals of photodetectors and mate- rials. The review then explores the use of ferroelectric properties, such as polarization, piezoelectricity, and thermoelectricity, to modulate the hybrid system. The role of ferroelectrics in enhancing the performances of hybrid system photodetectors is highlighted by comparing different structures, supplemented with the showcase of various applications. The article concludes by summarizing the current status of this field and suggesting future research directions, aiming to guide the design and application of next-generation 2D materials/ferro- electrics hybrid system photodetectors.

Real-time monitoring of plant immune responses is crucial for understanding plant immunity and mitigating economic losses from pathogen and pest attacks. However, current methods relying on molecular-level assessment are destructive and time-consuming. Here, we report an ultrathin, substrate-free, and highly conductive electronic tattoo (e-tattoo) designed for plants, enabling immune response monitoring through non-invasive electrical impedance spectroscopy (EIS). The e-tattoo’s biocompatibility, high conductivity, and sub-100 nm thickness allow it to conform to leaf tissue morphology and provide robust impedance data. We demonstrate continuous EIS analysis of live transgenic Arabidopsis thaliana plants for over 24 h, capturing the onset of NLR-mediated acute immune responses within three hours post-induction, prior to visible symptoms. RNA-seq and tissue ion leakage tests validate that EIS data accurately represent the physiological and molecular changes associated with immune activation. This non-invasive tissue-assessment technology has the potential to enhance our comprehension of immune activation mechanisms in plants and paves the way for real-time monitoring for plant health management.