We investigated the effect of different gradient doping methods on the charge collection efficiency of the electron multiplication layer of EBCMOS devices. Exponential doping of the electron multiplication layer can form a built-in electric field in the electron multiplication layer that is favorable for photoelectron transport, so exponential doping instead of uniform doping in the electron multiplication layer of EBCMOS can effectively improve the charge collection efficiency. It is shown that exponential heavy doping on the side of the electron multiplication layer near the dead layer and exponential light doping on the side near the depletion region can improve the built-in electric field structure and increase the lifetime of the multiplied electrons, thereby improving the charge collection efficiency of EBCMOS. The optimized device achieves a charge collection efficiency of 94.48 at an incident electron energy of 4keV, an electron beam diameter of 20nm, a dead layer thickness of 60nm, and a P-type silicon epitaxial layer thickness of 10μm.

In order to improve the charge collection efficiency of EBCMOS, we predicted and experimentally confirmed the doping structure of the electron multiplication layer. In this study, we simulate the charge collection efficiency of EBCMOS using various doping strategies by modeling the collisional scattering of electrons with solid atoms in semiconductor materials and combining the travel paths of electrons in the electron multiplication layer. The simulation findings demonstrate that the electric field distribution in EBCMOS could be optimized by using a layered doping structure to decrease the thickness of the index strongly doped layer. The enhanced doping structure directly contributes to the enhanced charge collecting efficiency. Based on the results of the simulations, doped samples were created and evaluated. The charge collection efficiency of EBCMOS devices might be significantly increased by decreasing the thickness of the index highly doped layer to 0.1 μm, reaching 86.27%. The testing was done on a 7 μm thick P-type silicon substrate with a 1 μm thick index highly doped layer.

In this work, AlN films were grown using gallium (Ga) as surfactant on 4° off-axis 4H-SiC substrates via microwave plasma chemical vapor deposition (MPCVD). We have found that AlN growth rate can be greatly improved due to the catalytic effect of trimethyl-gallium (TMGa), but AlN crystal structure and composition are not affected. When the proportion of TMGa in gas phase was low, crystal quality of AlN can be improved and three-dimensional growth mode of AlN was enhanced with the increase of Ga source. When the proportion of TMGa in gas phase was high, two-dimensional growth mode of AlN was presented, with the increase of Ga source results in the deterioration of AlN crystal quality. Finally, employing a two-step growth approach, involving the initial growth of Ga-free AlN nucleation layer followed by Ga-assisted AlN growth, high quality of AlN film with flat surface was obtained and the full width at half maximum (FWHM) values of 415 nm AlN (002) and (102) planes were 465 and 597 arcsec.

According to the interaction model between low-energy electrons and solids, combined with Monte Carlo calculation method, MATLAB software was used to simulate the electron scattering trajectories of a large number of photoelectrons incident on EBCMOS substrates. The energy loss rate of different incident photoelectrons on the substrate was analyzed. The influence of electron incidence depth and the range of electron motion. When the incident photoelectron energy increases, the energy loss rate gradually decreases, and the electron incident depth and the electron motion range increase. The simulation results can provide reference and basis for the design and preparation of the back-illuminated CMOS device substrate.

Neuromorphic imaging sensors (event cameras) offer a promising paradigm for computational imaging and human pose estimation (HPE) under extreme illumination conditions. Nevertheless, dark-scene background activity originating from photodiode dark current and circuit thermal noise, together with hot-pixel noise, severely corrupts event streams and impedes reliable HPE in low-light scenarios. To address this issue, we propose an adaptive event denoising framework built upon a spatiotemporal Gaussian-weighted neighborhood model with a dynamic thresholding mechanism. It can effectively suppress background activity and hot-pixel noise while preserving edge and motion details critical for pose estimation. Leveraging this denoising front-end, we construct a complete dark-scene neuromorphic HPE pipeline by transferring the pre-trained MediaPipe model onto event-based time-surfaces. Quantitative and qualitative evaluations on public and self-collected datasets demonstrate that our approach outperforms state-of-the-art denoising methods with an improvement of over 20% on public benchmarks and over 30% on self-collected dark-scene data. We expect our work to pave the way toward reliable dark-scene human–robot interaction through robust neuromorphic pose estimation.