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Contact resistivity quantifies the charge transport barrier, which is one of the key parameters for choosing the carrier selective contact for silicon solar cells. Optically transparent and electrically selective contacts such as transition metal oxide (TMO)-based contacts are employed in solar cell applications. Therefore, the extraction of contact resistivity for such Schottky contacts requires apposite validation. In this article, we have extracted the contact resistivity of TiOx/LiFx/Al stack over n-type c-Si wafer using two conventional techniques: 1) Shockley's transfer length method (TLM) and 2) Cox and Strack method (CSM). The extracted contact resistivity is validated by comparing with the solar cell's contact resistivity ( true contact resistivity) and fill factor (FF). We found that TLM overestimates the contact resistivity for highly resistive TiOx films, due to the asymmetrical nature of the TMO barrier and correlated with the reported experimental data. In contrast, CSM extracts more accurate contact resistivity for TiOx contacts when total resistance is extracted closer to the J mpp (38 mA cm−2). Besides, we recommend including the passivating layer along with the contacts (in the case of passivating contacts) to extract the actual contact resistivity felt by the solar cell.

This article, proposes a reconfigurable quantum photonic convolutional layer (QPCL) based on the reconfigurable photonic gates. The QPCL is used in the classical photonic CNN, where, an array of reconfigurable photonic gates (RPG) are arranged in a systematic way. The designed reconfigurable photonic gate serves as a unit cell for quantum photonic operations such as beam splitting, rotation, displacement, squeezing, and cubic- phase shifting. The designed RPG provides the features namely broadband operation, low insertion loss and compact layout. The entangled states are created based on the normalized pixel value of the input image. The configuration of reconfigurable photonic gate is accomplished using electro-optic P-i-N carrier injection mechanism. As compared to Mach-Zehnder interferometer (MZI) based realization, the proposed silicon reconfigurable photonic gate provides scalable operation and compact footprint. The reconfigurable photonic gate is modeled using 2D finite element beam propagation method (FEBPM). Finally, a compact numerical model is developed which performs Gaussian based continuous-variable (CV) quantum photonic operations and are verified with Xanadu’s strawberryfields quantum photonic simulator and PennyLane deep learning framework. The optimized accuracy (loss) is obtained with the utilization of QPCL layer and the values are 0.7627 (0.9595), this optimum result is obtained using a single QPCL layer with an epoch number of 30. Finally, a comparative analysis is made between quantum CNN and classical photonic CNN, where the quantum CNN resulted in 6.553% high accuracy and 6.988% low loss compared to the classical photonic CNN.

This article proposes a reconfigurable silicon photonic processing core (PPC), an array of reversible photonic gates and reconfigurable switch matrix (RSM) arranged in a systematic way. The RSM includes tunable directional couplers (TDC), provides the features namely broadband operation, low insertion loss and compact layout. The reconfiguration of photonic processing core is accomplished using the silicon based electro-optic P-i-N carrier injection mechanism. The photonic processing core is modeled using the 2D finite difference beam propagation method (FD-BPM). Finally, a compact numerical model (CNM) is developed utilizing the transmission and loss characteristics. The all-pass ring-resonator (RR) is configured in the proposed photonic processing core and the performance metrics are measured with the values of free-spectral range (FSR) of 8 nm, full-width half-maximum (FWHM) of 0.63 nm, finesse value of 12.6984, and quality factor (Q-factor) of 2460. The developed CNM is coded using MATLAB to verify its suitability for reconfigurable electronic-photonic integrated circuit (RePIC) that supports high-performance computing applications.