arXiv subjects

Guohua Hu

Publications and source records attributed to Guohua Hu.

13 recordsLinked to original sources

arXiv2025

Pseudomagnetic Control of Light Waves in the Electrically Tunable Photonic Crystals with Deformation Engineering

With the demonstrations of pseudo-magnetism in optical systems, the pursuits of its practical applications require not only the use of pseudomagnetic fields to create functional optical devices but also a reliable method to manipulate pseudo-magnetism-affected light waves. Here, we experimentally demonstrate an ultracompact Si-based cavity formed by triaxially deformed photonic honeycomb lattices. The triaxial deformation could lead to Landau quantization, showing the possibilities of realizing the localization and resonating of photons with pseudomagnetic fields. Through adopting the Si waveguides for directional coupling, we successfully obtain the transmission spectra for the proposed cavities in the photonic integrated circuits. This opens a novel avenue for highly efficient excitations and detections of Landau-quantized photonic density of states, totally on chip. Moreover, we verify a linear electrical tunability of -0.018 THz/mW for the pseudo-magnetism-induced optical resonant states, fulfilling the manipulation of photons without varying deformations. Our work introduces a mechanism for performing tunable light waves in triaxial deformation-engineered systems, which enriches the design principles of integrated optical devices.

physics.optics↗

arXiv2024

Hardware implementation of timely reliable Bayesian decision-making using memristors

Brains perform decision-making by Bayes theorem. The theorem quantifies events as probabilities and, based on probability rules, renders the decisions. Learning from this, Bayes theorem can be applied to enable efficient user-scene interactions. However, given the probabilistic nature, implementing Bayes theorem in hardware using conventional deterministic computing can incur excessive computational cost and decision latency. Though challenging, here we present a probabilistic computing approach based on memristors to implement the Bayes theorem. We integrate memristors with Boolean logics and, by exploiting the volatile stochastic switching of the memristors, realise probabilistic logic operations, key for hardware Bayes theorem implementation. To empirically validate the efficacy of the hardware Bayes theorem in user-scene interactions, we develop lightweight Bayesian inference and fusion hardware operators using the probabilistic logics and apply the operators in road scene parsing for self-driving, including route planning and obstacle detection. The results show our operators can achieve reliable decisions in less than 0.4 ms (or equivalently 2,500 fps), outperforming human decision-making and the existing driving assistance systems.

cs.LG↗

arXiv2024

GEM: A GEneral Memristive Transistor Model

Neuromorphic devices, with their distinct advantages in energy efficiency and parallel processing, are pivotal in advancing artificial intelligence applications. Among these devices, memristive transistors have attracted significant attention due to their superior stability and operation flexibility compared to two-terminal memristors. However, the lack of a robust model that accurately captures their complex electrical behavior has hindered further exploration of their potential. In this work, we introduce the GEneral Memristive transistor (GEM) model to address this challenge. The GEM model incorporates time-dependent differential equation, a voltage-controlled moving window function, and a nonlinear current output function, enabling precise representation of both switching and output characteristics in memristive transistors. Compared to previous models, the GEM model demonstrates a 300% improvement in modeling the switching behavior, while effectively capturing the inherent nonlinearities and physical limits of these devices. This advancement significantly enhances the realistic simulation of memristive transistors, thereby facilitating further exploration and application development.

physics.app-ph↗

arXiv2024

Deep Learning-Based Longitudinal Prediction of Childhood Myopia Progression Using Fundus Image Sequences and Baseline Refraction Data

Childhood myopia constitutes a significant global health concern. It exhibits an escalating prevalence and has the potential to evolve into severe, irreversible conditions that detrimentally impact familial well-being and create substantial economic costs. Contemporary research underscores the importance of precisely predicting myopia progression to enable timely and effective interventions, thereby averting severe visual impairment in children. Such predictions predominantly rely on subjective clinical assessments, which are inherently biased and resource-intensive, thus hindering their widespread application. In this study, we introduce a novel, high-accuracy method for quantitatively predicting the myopic trajectory and myopia risk in children using only fundus images and baseline refraction data. This approach was validated through a six-year longitudinal study of 3,408 children in Henan, utilizing 16,211 fundus images and corresponding refractive data. Our method based on deep learning demonstrated predictive accuracy with an error margin of 0.311D per year and AUC scores of 0.944 and 0.995 for forecasting the risks of developing myopia and high myopia, respectively. These findings confirm the utility of our model in supporting early intervention strategies and in significantly reducing healthcare costs, particularly by obviating the need for additional metadata and repeated consultations. Furthermore, our method was designed to rely only on fundus images and refractive error data, without the need for meta data or multiple inquiries from doctors, strongly reducing the associated medical costs and facilitating large-scale screening. Our model can even provide good predictions based on only a single time measurement. Consequently, the proposed method is an important means to reduce medical inequities caused by economic disparities.

cs.CV↗

arXiv2024

Self-reconfigurable Multifunctional Memristive Nociceptor for Intelligent Robotics

Artificial nociceptors, mimicking human-like stimuli perception, are of significance for intelligent robotics to work in hazardous and dynamic scenarios. One of the most essential characteristics of the human nociceptor is its self-adjustable attribute, which indicates that the threshold of determination of a potentially hazardous stimulus relies on environmental knowledge. This critical attribute has been currently omitted, but it is highly desired for artificial nociceptors. Inspired by these shortcomings, this article presents, for the first time, a Self-Directed Channel (SDC) memristor-based self-reconfigurable nociceptor, capable of perceiving hazardous pressure stimuli under different temperatures and demonstrates key features of tactile nociceptors, including 'threshold,' 'no-adaptation,' and 'sensitization.' The maximum amplification of hazardous external stimuli is 1000%, and its response characteristics dynamically adapt to current temperature conditions by automatically altering the generated modulation schemes for the memristor. The maximum difference ratio of the response of memristors at different temperatures is 500%, and this adaptability closely mimics the functions of biological tactile nociceptors, resulting in accurate danger perception in various conditions. Beyond temperature adaptation, this memristor-based nociceptor has the potential to integrate different sensory modalities by applying various sensors, thereby achieving human-like perception capabilities in real-world environments.

physics.app-ph↗

arXiv2024

Harnessing physical entropy noise in structurally metastable 1T' molybdenum ditelluride for true random number generation

True random numbers are essential in various research and engineering problems. Their generation depends upon a robust physical entropy noise. Here, we present true random number generation by harnessing the conductance noise probed in structurally metastable 1T' molybdenum ditelluride (MoTe2). The noise, well-fitting a Poisson process, is proved a robust physical entropy noise at low and even cryogenic temperatures. Noise characteristic analysis suggests the noise may originate from the polarization variations of the underlying ferroelectric dipoles in 1T' MoTe2. We demonstrate the noise allows for true random number generation, enabling their use as seed for generating high-throughput secure random numbers exceeding 1 Mbit/s, appealing for practical applications in, for instance, cryptography where data security is now a severe issue. As an example, we show biometric information safeguarding in neural networks by using the random numbers as mask, proving a promising data security measure in big data and artificial intelligence.

cs.CR↗

arXiv2024

Analysis on reservoir activation with the nonlinearity harnessed from solution-processed molybdenum disulfide

Reservoir computing is a recurrent neural network designed for approximating complex dynamics in, for instance, motion tracking, spatial-temporal pattern recognition, and chaotic attractor reconstruction. Its implementation demands intense computation for the nonlinear transformation of the reservoir input, i.e. activating the reservoir. Configuring physical nonlinear networks as the reservoir and employing the physical nonlinearity for the reservoir activation is an emergent solution to address the challenge. In this work, we analyze the feasibility of harnessing the nonlinearity from solution-processed molybdenum disulfide (MoS2) for reservoir activation. We fit the high-order nonlinearity, achieved by Stark modulation of MoS2, as the activation function to facilitate implementation of a reservoir computing model. Due to the high-order nonlinearity, the model can achieve long-term synchronization and robust generalization for complex dynamical system regression. As a potential application exploring this ability, we appoint the model to generate chaotic random numbers for secure data encryption. Given this reservoir activation capability, and the scalability of solution-processed MoS2, our results suggest the potential for realizing physical reservoir computing with solution-processed MoS2.

physics.app-ph↗

arXiv2024

Lightweight, error-tolerant edge detection using memristor-enabled stochastic logics

The demand for efficient edge vision has spurred the interest in developing stochastic computing approaches for performing image processing tasks. Memristors with inherent stochasticity readily introduce probability into the computations and thus enable stochastic image processing computations. Here, we present a stochastic computing approach for edge detection, a fundamental image processing technique, facilitated with memristor-enabled stochastic logics. Specifically, we integrate the memristors with logic circuits and harness the stochasticity from the memristors to realize compact stochastic logics for stochastic number encoding and processing. The stochastic numbers, exhibiting well-regulated probabilities and correlations, can be processed to perform logic operations with statistical probabilities. This can facilitate lightweight stochastic edge detection for edge visual scenarios characterized with high-level noise errors. As a practical demonstration, we implement a hardware stochastic Roberts cross operator using the stochastic logics, and prove its exceptional edge detection performance, remarkably, with 95% less computational cost while withstanding 50% bit-flip errors. The results underscore the great potential of our stochastic edge detection approach in developing lightweight, error-tolerant edge vision hardware and systems for autonomous driving, virtual/augmented reality, medical imaging diagnosis, industrial automation, and beyond.

cs.ET↗

arXiv2024

Macroscopic electro-optical modulation of solution-processed molybdenum disulfide

Molybdenum disulfide (MoS2) has drawn great interest for tunable photonics and optoelectronics advancement. Its solution processing, though scalable, results in randomly networked ensembles of discrete nanosheets with compromised properties for tunable device fabrication. Here, we show via density-functional theory calculations that the electronic structure of the individual solution-processed nanosheets can be modulated by external electric fields collectively. Particularly, the nanosheets can form Stark ladders, leading to variations in the underlying optical transition processes and thus, tunable macroscopic optical properties of the ensembles. We experimentally confirm the macroscopic electro-optical modulation employing solution-processed thin-films of MoS2 and ferroelectric P(VDF-TrFE), and prove that the localized polarization fields of P(VDF-TrFE) can modulate the optical properties of MoS2, specifically, the optical absorption and photoluminescence on a macroscopic scale. Given the scalability of solution processing, our results underpin the potential of electro-optical modulation of solution-processed MoS2 for scalable tunable photonics and optoelectronics. As an illustrative example, we successfully demonstrate solution-processed electro-absorption modulators.

cond-mat.mtrl-sci↗

arXiv2023

Intelligent machines work in unstructured environments by differential neuromorphic computing

Efficient operation of intelligent machines in the real world requires methods that allow them to understand and predict the uncertainties presented by the unstructured environments with good accuracy, scalability and generalization, similar to humans. Current methods rely on pretrained networks instead of continuously learning from the dynamic signal properties of working environments and suffer inherent limitations, such as data-hungry procedures, and limited generalization capabilities. Herein, we present a memristor-based differential neuromorphic computing, perceptual signal processing and learning method for intelligent machines. The main features of environmental information such as amplification (>720%) and adaptation (<50%) of mechanical stimuli encoded in memristors, are extracted to obtain human-like processing in unstructured environments. The developed method takes advantage of the intrinsic multi-state property of memristors and exhibits good scalability and generalization, as confirmed by validation in two different application scenarios: object grasping and autonomous driving. In the former, a robot hand experimentally realizes safe and stable grasping through fast learning (in ~1 ms) the unknown object features (e.g., sharp corner and smooth surface) with a single memristor. In the latter, the decision-making information of 10 unstructured environments in autonomous driving (e.g., overtaking cars, pedestrians) is accurately (94%) extracted with a 40*25 memristor array. By mimicking the intrinsic nature of human low-level perception mechanisms, the electronic memristive neuromorphic circuit-based method, presented here shows the potential for adapting to diverse sensing technologies and helping intelligent machines generate smart high-level decisions in the real world.

eess.SP↗

arXiv2023

Conduction modulation of solution-processed two-dimensional materials

Solution-processed two-dimensional (2D) materials hold promise for their scalable applications. However, the random, fragmented nature of the solution-processed nanoflakes and the poor percolative conduction through their discrete networks limit the performance of the enabled devices. To overcome the problem, we report conduction modulation of the solution-processed 2D materials via the Stark effect. Using liquid-phase exfoliated molybdenum disulfide (MoS2) as an example, we demonstrate nonlinear conduction modulation with a switching ratio of >105 by the local fields from the interfacial ferroelectric P(VDF-TrFE). Through density-functional theory calculations and in situ Raman scattering and photoluminescence spectroscopic analysis, we understand the modulation arises from a charge redistribution in the solution-processed MoS2. Beyond MoS2, we show the modulation may be viable for the other solution-processed 2D materials and low-dimensional materials. The effective modulation can open their electronic device applications.

physics.app-ph↗

arXiv2019

Conformal printing of graphene for single and multi-layered devices on to arbitrarily shaped 3D surfaces

Printing has drawn a lot of attention as a means of low per-unit cost and high throughput patterning of graphene inks for scaled-up thin-form factor device manufacturing. However, traditional printing processes require a flat surface and are incapable of achieving patterning on to 3D objects. Here, we present a conformal printing method to achieve functional graphene-based patterns on to arbitrarily-shaped surfaces. Using experimental design, we formulate a water-insoluble graphene ink with optimum conductivity. We then print single and multi-layered electrically functional structures on to a sacrificial layer using conventional screen printing. The print is then floated on water, allowing the dissolution of the sacrificial layer, while retaining the functional patterns. The single and multilayer patterns can then be directly transferred on to arbitrarily-shaped 3D objects without requiring any post deposition processing. Using this technique, we demonstrate conformal printing of single and multilayer functional devices that include joule heaters, resistive strain sensors and proximity sensors on hard, flexible and soft substrates, such as glass, latex, thermoplastics, textiles, and even candies and marshmallows. Our simple strategy offers great promises to add new device and sensing functionalities to previously inert 3D surfaces.

physics.app-ph↗

arXiv2015

Surfactant-aided exfoliation of molydenum disulphide for ultrafast pulse generation through edge-state saturable absorption

We use liquid phase exfoliation to produce dispersions of molybdenum disulphide (MoS2) nanoflakes in aqueous surfactant solutions. The chemical structures of the bile salt surfactants play a crucial role in the exfoliation and stabilization of MoS2. The resultant MoS2 dispersions are heavily enriched in single and few (<6) layer flakes with large edge to surface area ratio. We use the dispersions to fabricate free-standing polymer composite wide-band saturable absorbers to develop mode-locked and Q- switched fibre lasers, tunable from 1535-1565 and 1030-1070 nm, respectively. We attribute this sub-bandgap optical absorption and its nonlinear saturation behaviour to edge-mediated states introduced within the material band-gap of the exfoliated MoS2 nanoflakes.

cond-mat.mtrl-sci↗