In the past few years, there has been a rise in incidents involving gas leaks, raising serious concerns for human safety and well-being. It is of utmost importance to develop an advanced gas detection platform that ensures accurate identification of gases and enables timely alerts to prevent accidents. Field-effect transistor (FET) gas sensors have a significant role in real-time environmental monitoring, medical pre-diagnosis, industrial control, and other scenarios due to their unique ability to amplify gate electrode signals and detect even the smallest amounts of gases. In this study, we have developed a gas sensor based on carbon field effect transistor (FET) technology to detect hydrogen (H2) at room temperature. The sensing gate layer of the sensor consists of Pd/Ag, while the conductive channel is made up of carbon nanotubes (CNTs). To mitigate the negative effects caused by channel exposure, we have incorporated yttrium oxide as the gate dielectric layer. Our fabricated carbon-based FET gas sensor demonstrates an impressive detection limit of 70 ppm for H2 at ambient conditions and can effectively function as an early warning system for gas leakage incidents. Moreover, our designed sensor exhibits good selectivity, repeatability, and anti-humidity property. We anticipate that our research will contribute significantly to advancing high-performance and highly integrated trace gas sensors by establishing solid theoretical and experimental foundations.
KEYWORDS: Signal to noise ratio, Interference (communication), Tunable filters, Sensors, Signal processing, Electronic filtering, Education and training, Denoising, Neural networks, Deep learning
Self-Mixing Interference (SMI) is promising high-precision measurement technology with advantage of compact structure, low implementation cost and high measurement resolution, which has been used in various applications in laboratory and engineering fields. However, measurement performance of an SMI sensor can be significantly affected by noises. In this paper we propose a solution based on the U-net, a popular deep learning scheme, to remove noises from SMI signals. U-net based deep learning is used to learn noise patterns and inherent levels from large sample data, and finally to denoise the SMI signals. Our proposed method can perform end-to-end neural network model training and directly process the original waveform. The results show that this method can effectively improve the signal-to-noise ratio of SMI signals. It is believed that this unified and precise method is able to lead to enhancement of performance of SMI laser sensors operating under noisy practical engineering conditions.
Self-mixing interferometry is an interferometric technique based on the self-mixing effect. Its unique structure and operating principle allow part of the light emitted by the laser to be reflected back into the laser cavity via a remote target, producing optical output power modulations and displayed as interference waveforms. The shape of these waveforms is determined by the optical feedback factor and the linewidth enhancement factor, which also affect the behavior and measurement performance of the self-mixing interferometric system. To address the need for optical measurements in the medium feedback case, this paper proposes a method to accurately estimate the optical feedback factor and linewidth enhancement factor using the non-dominated sorting genetic algorithm II (NSGA-II). The effectiveness and robustness of the method are verified by simulation and experimental results, and preliminary tests show that it can achieve an accuracy of 0.35% and 0.56% in estimating the optical feedback factor and linewidth enhancement factor. The method has potential for practical engineering applications and promotes the development of self-mixing interferometry.
The refractive index of a material is one of the most important optical parameters. In this paper, we propose the method of Self-Mixing Interferometry (SMI) to measure the refractive index of materials. SMI is superior to other laser interferometry methods because of its characteristics of simplicity and compactness. However, SMI signals are not easy to be analyzed due to the low signal-to-noise ratio and the loss of phase information. Based on the advantages of Convolutional Neural Network (CNN), in this work, we propose a scheme to reconstruct the refractive index of materials from SMI signals based on CNN. With the injection current to the laser being driven by a sawtooth wave, we first obtain different SMI signals by letting the light passing through materials with different refractive indexes under the condition of known material thickness, and then train CNN with SMI signals. The trained network is then used to estimate the refractive indexes of materials. The results show that the method is noise-proof and has high adaptability to the measurement under different conditions.
Self-mixing interferometry (SMI) is superior to other laser interferometry methods due to its simplicity and compactness. However, SMI signals are often complex and difficult to process due to interference in the form of variations in the effective reflectivity of the target, noisy signals, complex signal shapes, and other dependencies. Deep neural networks have been a very popular area of research in computer artificial intelligence in recent years, allowing more implicit features to be uncovered than traditional shallow machine learning. It has been shown that convolutional and back-propagation neural networks can be used for SMI signal processing. There are also studies that have used machine learning genetic algorithms for absolute distance measurement. Based on the above, this study used convolutional neural networks to form a deep neural network for absolute distance measurement based on SMI technology. We first trained the deep convolutional neural network at different feedback strengths. The results of the Convolutional Neural Network (CNN) model showed a coefficient of determination of 0.9987. which is consistent with the required model. The trained network was then used to estimate absolute distances with and without the addition of noise. The comparison proves that the proposed method is noise-proof and has high adaptability for measurements under different conditions.
Semiconductor laser (SL) with optical feedback presents rich nonlinear dynamics, e.g., Period-One Oscillation (POO), quasi-periodic oscillation, and chaotic states. It is of significance to study the state boundary between different dynamic states for an SL with optical feedback from the perspective of both suppressing and using these dynamics, especially the boundary of POO state. This paper reveals the POO boundary of an SL with Dual Optical Feedback (DOF). The modified Lang-Kobayashi equations were first numerically solved to develop a bifurcation diagram to determine the boundary between POO and other states. Then the POO-DOF boundary was compared with that of an SL with single optical feedback. Moreover, the effect of system parameters on the POO-DOF boundaries is researched. The results obtained are helpful to promote the development of the applications of POO dynamics.
Damping vibration is one of the most common physical phenomena and an important research topic in the field of mechanical engineering. Self-mixing interferometry (SMI) is a non-destructive and non-contact optical sensing and measurement method. An SMI system commonly operates at weak or moderate feedback regime. The strong feedback regime is always avoided because of the possible instability in this regime. Recently, it has been demonstrated that if an SMI is stable in the strong feedback region, its input and output may maintain a linear relationship under proper operation conditions. In this paper, we proposed to apply an SMI system at strong feedback regime for measurement of damping vibration. The results show that an SMI system at strong feedback regime can achieve linear sensing even without need of extra SMI fringe processing, contributing to a new simple solution for measurement of damping vibration.
Line-width enhancement factor (α) is a fundamental parameter of semiconductor lasers (SLs). In this paper, we propose a method for measuring α of SLs. The method is based on back-propagations neural network (BPNN) for all feedback regimes. MATLAB was used to carry out the numerical calculations and simulations of the BPNN. We used the training set and the test set to train the prediction model, and then used the predictive model to output the predicated value. The results of the BPNN model showed that the R2 value was 0.99994, and the results were following the requirement model. The accuracy of the method has been confirmed and tested by computer simulations, which show that the method can estimate α with a relative error less than 2.5%.
Self-mixing interferometry (SMI) is a well-developed sensing technology. An SMI system can be described using a model derived from the well-known Lang and Kobayashi equations by setting the system operating in stable region. The features of an SMI signal are determined by the external optical feedback factor (denoted by C). Our recent work shows that when the factor C increases to a certain value, e.g. in moderate feedback regime with 1<C<4.6, the SMI system might enter unstable region and the existing SMI model is invalid. In this case, the SMI signals exhibit some novel features and contain higher-frequency components. To detect an SMI signal without distortion or take suitable correction on the SMI signal, it is must to make an analysis on the system bandwidth and its influence on SMI signals. The results in this paper provide useful guidance for developing an SMI sensing system.
Material parameters such as Young’s modulus and internal friction are important for estimation of material performance. This paper presents an experimental study for measuring material related parameters using a selfmixing interferometric (SMI) configuration. An SMI system consists of a laser diode (LD), a lens and an external target to be measured. When a part of the lasing light back-reflected or back-scattered by the external target re-enters the LD internal cavity, both optical frequency and intensity of the lasing light can be modulated. This modulated laser intensity is often referred as SMI signal. Generally, the target related movement or its surface information can be retrieved from this SMI signal. In this paper, an SMI system is implemented. A tested sample is used as the target to form the external cavity of the LD. The tested sample is stimulated in vibration. Continuous wavelet transform (CWT) is utilized to retrieve the vibration information of the tested sample from an SMI signal. We are able to obtain both Young’s modulus and internal friction from a piece of an experimental SMI signal. This work provides a novel, simple non-destructive solution for simultaneous measurement of Young’s modulus and internal friction.
This work proposes to measure the topography of microstructure surfaces using a self-mixing interference (SMI) configuration. The theoretical measurement model is built using beam-expanded plane wave method and considering SMI effect. The interference patterns for different objects are obtained based on the presented model. In addition, an algorithm for reconstructing the three-dimensional surface is implemented and applied onto an object with spherical surface. The presented work shows the potential application for topography measurement using a compact SMI configuration.
This paper reviews the self-mixing interference (SMI) in terms of its operation principle, the features of SMI signals and its configuration. SMI refers to a phenomenon that occurs when a small fraction of the light emitted by a laser is backscattered or reflected by an external target and re-enters the laser active cavity, thus leading to the modulation of the laser output power. This is a remarkably universal phenomenon, occurring in lasers regardless of type. A few application examples are presented based on the research work done in our group, including SMI sensing for displacement measurement, material parameters and laser parameters. An SMI with the laser operating at the relaxation oscillation is introduced which has potential for achieving more sensitive sensing.
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