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Lock-in carrierography (LIC), a recently emerging camera-based imaging technique, is proving to be very promising for noncontact and quantitative characterization of electrical/electronic properties of semiconductor and optoelectronic materials/devices at different stages of research, fabrication, and manufacturing. This tutorial is devoted to LIC and it contains four sections. First, the background of the terminology, the needs from the electronics industry, and the research progress of LIC are briefly introduced. Section II is regarding homodyne LIC, including the relevant basics (semiconductor and photoluminescence physics, digital lock-in imaging strategy, experimental configuration, etc.) and its applications to carrier effective lifetime imaging, resistivity imaging, and Si solar cell electrical characterization, while Sec. III is for heterodyne LIC, including the relevant basics (high-frequency carrier density waves, heterodyne photoluminescence signal generation mechanisms, nonlinear carrier recombination dynamics, etc.) and its applications to surface recombination velocity imaging, carrier trapping dynamic parameters imaging, and quantum-dot solar cell characterization. Comments and advice on the future study of LIC are given in the Outlook section. [ABSTRACT FROM AUTHOR]

In this article, a non-destructive photothermal radiometry (PTR) method is proposed to quantitatively evaluate the aging degrees of field-servicing silicone rubber composite insulators via a thermal diffusivity ratio and degradation layer thickness. A sensitivity analysis is performed to investigate the sensitivity of the PTR amplitude and phase to the thermo-physical, optical, and structural parameters of the aged silicone rubber composite insulators consisting of a degradation layer and a substrate layer. It is found that the PTR amplitude and phase are highly sensitive to the thermal diffusivity, effective infrared absorption coefficient, and thickness of the degradation layer as well as the thermal diffusivity of the substrate layer but are much less sensitive to optical absorption coefficients of the degradation and substrate layers and the effective infrared absorption coefficient of the substrate layer. Thus, parameters with high sensitivity coefficients are set as free parameters and determined by multi-parameter fitting the frequency dependences of the PTR amplitude and phase measured directly from the surfaces of the field-serviced silicone rubber composite insulators to a two-layer PTR theoretical model, while in the multi-parameter fitting, the parameters with low sensitivity coefficients are set as "known" parameters whose values are either measured separately or taken from literature. By doing so, the thermal diffusivity ratio and degradation layer thickness are accurately determined in a non-destructive way. Experimental results demonstrate that the thermal diffusivity ratio and degradation layer thickness determined by the proposed non-destructive PTR method are in good agreement with that determined by a conventional destructive PTR method, thus, proving the feasibility of the non-destructive PTR method for quantitative evaluation of aging degrees of the silicone rubber composite insulators being serviced in the high-voltage power transmission systems. [ABSTRACT FROM AUTHOR]

The aim of pansharpening is to fuse panchromatic (PAN) images and the corresponding low-resolution multispectral (LRMS) images to generate high-resolution multispectral (HRMS) images. In recent years, convolutional neural network (CNN)-based methods have obtained excellent performance in this field. In order to break through the limitations of standard convolution, the dynamic convolution is also proposed for pansharpening. However, the existing dynamic convolution ignores the context along channel dimension and does not consider the mutual enhancement between spatial and spectral information. In this letter, we propose a novel cross-interaction kernel attention network (CIKANet). The proposed model consists of two branches to extract spectral and spatial information. In particular, spatial and spectral kernel attention modules that can dynamically scale the parameters of convolutional kernels are applied. To strengthen the interactions between branches and obtain complementary information, we adopt a cross kernel attention structure. A series of experiments conducted on the WV3 and QB datasets suggest that CIKANet outperforms other state-of-the-art (SOTA) models visually and quantitatively. [ABSTRACT FROM AUTHOR]

Methane (CH4) and nitrous oxide (N2O) are among the most important atmospheric greenhouse gases. A gas sensor based on a tunable 7.6 μm continuous-wave external-cavity mode-hop-free (EC-MHF) quantum cascade laser (from 1290 to 1350 cm−1) cavity ring-down spectroscopy (CRDS) technique was developed for the simultaneous detection of CH4 and N2O in ambient air with water vapor (H2O) mostly removed via molecular sieve drying to minimize the impact of H2O on the simultaneous measurements. Still, due to the broad and strong absorption spectrum of H2O in the entire mid-infrared (mid-IR) spectral range, residual H2O in the dried ambient air due to incomplete drying and leakage, if not properly accounted for, could cause a significant influence on the measurement accuracy of the simultaneous CH4 and N2O detection. In this paper, the impact of residual H2O on the simultaneous CH4 and N2O measurements were analyzed by comparing the CH4 and N2O concentrations determined from the measured spectrum in the spectral range from 1311 to 1312.1 cm−1 via simultaneous CH4 and N2O measurements and that determined from the measured spectrum in the spectral range from 1311 to 1313 cm−1 via simultaneous CH4, N2O, and H2O measurements. The measured dependence of CH4 and N2O concentration errors on the simultaneously determined H2O concentration indicated that the residual H2O caused an under-estimation of CH4 concentration and over-estimation of N2O concentration. The H2O induced CH4 and N2O concentration errors were approximately linearly proportional to the residual H2O concentration. For the measurement of air flowing at 3 L per min, the residual H2O concentration was stabilized to approximately 14 ppmv, and the corresponding H2O induced errors were −1.3 ppbv for CH4 and 3.7 ppbv for N2O, respectively. [ABSTRACT FROM AUTHOR]

Cavity ringdown (CRD) is employed for the first time, to the best of our knowledge, to precisely measure the optical scattering of highly reflective (HR) optics with measurement sensitivity greatly enhanced via power trapping inside the ringdown cavity. The scattering measurement accuracy is significantly improved by calibrating the photo-detector for the scattering measurement with the low transmittance of the cavity mirror or test HR mirror, which is also accurately measured by CRD. The influence of environmental stray light (such as the probe light scattered by optics and mechanical parts outside the ringdown cavity) and other background noises on the scattering measurement is greatly eliminated by the temporal behavior of the scattering CRD signal. A scattering measurement sensitivity of 4.0 × 10 -13 is experimentally achieved with a laser with output power of 12 mW.