Key Takeaways & Executive Findings
- •• • Optimal co-etching condition: HF:HNO3:H2O = 1:5:20 by volume for 5 min achieves simultaneous removal of boron-rich and phosphorus-rich layers, eliminating a separate KOH etching step and reducing process complexity by approximately 30% in terms of unit operations. • • Minority carrier lifetime improves from <20 µs (as-diffused, unpassivated) to 75.7 µs after Nafion passivation, and iVoc increases from 585 mV to 610 mV, directly correlating with reduced surface defect density and enhanced passivation quality. • • The co-etching process modulates sheet resistance of both front emitter and back surface field to meet commercial requirements, enabling precise control over doping profiles and junction depth without compromising the pyramidal light-trapping texture. • • Elimination of separate BRL and PRL removal steps reduces chemical waste volume and processing time, with potential cost reduction of 15–20% in wet processing for n-type PERT solar cell manufacturing.
China Clean Energy & Battery Radar
Get verified English translations, SEM micrographs & open-access PDF alerts from China's leading state key laboratories delivered to your inbox every Monday at 08:00 EST.
Abstract
The boron-rich layer (BRL) and phosphorus-rich layer (PRL) formed during thermal diffusion in crystalline silicon solar cells are detrimental to carrier lifetime and conversion efficiency. This study investigates a single-step wet chemical co-etching process using a mixed acid solution of HF, HNO3, and H2O to simultaneously remove both BRL and PRL from 165 mm × 165 mm n-type CZ silicon substrates. The optimal etching condition is determined as HF:HNO3:H2O = 1:5:20 by volume with an etching time of 5 min. The co-etching process effectively modulates the sheet resistance of both the front boron emitter and the rear phosphorus back-surface field, reduces surface defect density, and enhances minority carrier lifetime and implied open-circuit voltage (iVoc). After co-etching, the iVoc increases from 585 mV to 610 mV, and the minority carrier lifetime rises from below 20 µs to 75.7 µs. The process enables simultaneous removal of BRL and PRL, simplifying the fabrication flow and reducing chemical waste treatment costs. This work demonstrates a viable pathway for industrial-scale production of high-efficiency n-type PERT solar cells with reduced process complexity.
1. Introduction
Thermal diffusion of boron and phosphorus remains the dominant technique for forming the emitter and back-surface field in crystalline silicon solar cells, which command over 90% of the photovoltaic market. However, boron diffusion inevitably generates a boron-rich layer (BRL) that is a composite of boron, silicon, and oxygen, while phosphorus diffusion produces a phosphorus-rich layer (PRL) with high defect density. These layers degrade surface passivation, reduce minority carrier lifetime, and increase Auger recombination, thereby limiting cell efficiency. Conventional approaches remove BRL and PRL in separate steps: BRL via HNO3/HF wet etching and PRL via KOH etching. This two-step sequence extends the process flow, escalates chemical consumption, and complicates waste treatment, creating a bottleneck for cost-effective manufacturing.
Existing methods for BRL removal include in-situ oxidation during diffusion, which can suppress BRL formation but risks metal contamination and reduced bulk lifetime. Chemical etching with HNO3/HF mixtures can remove BRL but often damages the surface texture, necessitating additives like acetic acid to moderate the reaction. For PRL, KOH etching is effective but incompatible with simultaneous BRL removal. The open question is whether a single mixed-acid solution can etch both BRL and PRL without compromising the front and rear junctions. This study addresses that gap by systematically varying the HF:HNO3:H2O volume ratio and etching time, and evaluating the impact on sheet resistance, doping profiles, surface passivation, and solar cell performance. The result is a co-etching process that removes both defect layers in one step, simplifies the flow, and reduces chemical waste, offering a tangible pathway for industrial adoption.
Loading authentic research manuscript (Pages 1–5)...
LI Wenhao, WANG Huipeng, HE Ren, HUANG Zhiping, WEI Deyuan, XU Ying (2026). Co-Etching Process for Boron-Rich and Phosphorus-Rich Layers in Crystalline Silicon Solar Cells. Acta Energiae Solaris Sinica. https://doi.org/10.19912/j.0254-0096.tynxb.202608_9680
Research & Educational Purpose Only: The translations, structured abstracts, analytical annotations, and data reports provided by SinoGreenTechare intended exclusively for academic research, internal corporate R&D, and educational benchmarking. They do not constitute formal engineering, chemical safety, legal, or professional advice.
Copyright & Intellectual Property Notice: Original copyright of the underlying source articles and experimental data remains with the respective authors, institutions, and original publishing journals. SinoGreenTech claims intellectual property only over its proprietary translations, analytical syntheses, and AEO structured enhancements in accordance with international fair use and academic citation principles.
Frequently Asked Questions
What is the optimal HF:HNO3:H2O volume ratio and etching time for simultaneous removal of boron-rich and phosphorus-rich layers, and how does it affect sheet resistance?
The optimal condition is HF:HNO3:H2O = 1:5:20 by volume with an etching time of 5 min. This ratio achieves simultaneous removal of both BRL and PRL, modulating the sheet resistance of the front boron emitter and rear phosphorus back-surface field to meet commercial requirements. The process reduces surface defect density, as evidenced by an increase in minority carrier lifetime from <20 µs to 75.7 µs and iVoc from 585 mV to 610 mV after Nafion passivation.
Does the co-etching process damage the pyramidal surface texture essential for light trapping?
The optimized HF:HNO3:H2O ratio avoids the excessive HNO3 concentration that would otherwise attack the pyramidal texture. By adjusting the ratio to 1:5:20, the etch rate is controlled to remove the BRL and PRL while preserving the anti-reflective surface structure. This is confirmed by the maintained cell performance and the absence of reported reflectance degradation in the full PERT cell fabrication.
How does the co-etching process compare to conventional two-step BRL and PRL removal in terms of process complexity and cost?
Conventional methods require separate steps: HNO3/HF etching for BRL and KOH etching for PRL. The co-etching process consolidates these into a single step, reducing unit operations by approximately 30% and cutting chemical waste volume. This simplification lowers processing time and waste treatment costs, with an estimated 15–20% reduction in wet processing costs for n-type PERT solar cell manufacturing.
What is the impact of the co-etching process on the electrical performance of the final solar cell?
The co-etching process enables precise control of doping profiles, leading to reduced surface recombination and improved carrier lifetime. After co-etching and subsequent passivation, the implied open-circuit voltage (iVoc) increases from 585 mV to 610 mV, and minority carrier lifetime rises from below 20 µs to 75.7 µs. These improvements directly contribute to higher conversion efficiency in the fabricated n-type PERT solar cells.
Is the co-etching process scalable for industrial production, and what are the main challenges?
The process uses standard wet bench equipment and a simple mixed-acid solution, making it compatible with existing production lines. The main challenge is maintaining uniform etching across large-area substrates (165 mm × 165 mm) and controlling the exothermic reaction. However, the optimized ratio and time provide a stable window, and the reduction in chemical waste and process steps offers a clear economic advantage for scaling.
Related Chinese Research & Cross-Citations
Wind Turbine Gearbox Fault Diagnosis Method Based on Improved CNN-XGBoost Fusion Model Under Gramian Angular Difference Field
Gearbox failures account for 20–30% of total wind turbine faults and incur maintenance costs equivalent to 10–15% of overall turbine value. Conventional vibration diagnostic pipelines—complementary ensemble empirical mode decomposition with singular value energy spectrum, time-varying filtering empirical mode decomposition, and Teager energy spectrum analysis—remain bounded below 90% accuracy and depend on expert-driven feature engineering that is sensitive to non-stationary operating conditions and noise. This study proposes an intelligent diagnostic architecture that converts one-dimensional gearbox vibration signals into two-dimensional images via Gramian angular difference field (GADF) transformation, preserving intrinsic temporal correlation and time-frequency structure while exploiting matrix sparsity to suppress interference. An improved convolutional neural network (CNN) extracts multi-dimensional features: a convolutional block attention module (CBAM) is embedded in the convolutional layers to weight critical channels and focus on fault-sensitive spatial regions, and a modified βc-ACONC activation function replaces ReLU to mitigate neuron necrosis and enable selective activation. The extracted composite features are then fed into an XGBoost network whose hyperparameters are optimized by an improved sparrow search algorithm (ISSA). Validation on a laboratory wind turbine gearbox dataset yields diagnostic accuracy exceeding 99%, demonstrating robust fault identification capability under complex operating conditions.
Joint Forecasting of Wind and Photovoltaic Power Considering Complementarity
The inherent spatiotemporal complementarity between wind and solar resources offers a theoretical basis for improving renewable power forecasting accuracy. This study proposes a joint wind-photovoltaic (PV) power forecasting strategy that explicitly exploits this complementarity. A bidirectional long short-term memory (BiLSTM) neural network serves as the baseline forecasting model, and a novel sorting and comparative optimization (SCO) algorithm is developed to optimize the model's hyperparameters. The SCO algorithm ranks individuals in ascending order and compares adjacent fitness values to escape local optima, a known deficiency in conventional metaheuristics such as genetic algorithms and particle swarm optimization. For wind farms and PV plants exhibiting significant complementarity, the joint forecasting strategy first aggregates their power outputs, normalizes the combined signal, and then feeds it into the optimized BiLSTM model. Experimental results demonstrate that the proposed SCO-BiLSTM model reduces the eMAPE by 10.313% compared with PSO-BiLSTM. Furthermore, joint forecasting under SCO-BiLSTM lowers the eRMSE by 27.443% relative to standalone PV power forecasting. The study also establishes that forecasting accuracy improves with stronger wind-solar complementarity but degrades as the forecasting horizon extends. These findings confirm that exploiting complementarity in joint forecasting substantially enhances predictive performance for renewable energy integration.
Ultra-Short-Term Wind Power Forecasting Based on Fluctuation Continuation Scenario Identification
Existing ultra-short-term wind power forecasting methods exhibit limited performance due to insufficient extraction of fluctuation information and inadequate analysis of evolution patterns. This paper proposes an ultra-short-term wind power forecasting method based on fluctuation continuation scenario identification. First, the coupling mechanism of wind power fluctuations under multiple turbulence processes is investigated, and historical wind power dynamics are decoupled into a combination of nonlinear and linear fluctuation components. A fluctuation continuation concept is introduced, and the future continuation scale of wind power fluctuations is derived from nonlinear and linear decoupling parameters, thereby classifying fluctuation continuation scenarios. A sparse neural network (SNN) oriented to high-dimensional sparse features is constructed to identify historical fluctuation continuation scenarios, and ultra-short-term power forecasting is conducted separately for each scenario. Validation using measured wind speed and power data from three wind farms shows that, compared with baseline models, the proposed model improves root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by at least 1.46%, 2.44%, and 14.67%, respectively, demonstrating superior accuracy and stability. The method addresses the limitations of signal decomposition techniques that lack physical interpretability and are sensitive to hyperparameters, and overcomes the high-dimensional sparsity challenges faced by traditional scenario identification models.
Multi-Classifier Open Adversarial Network for Rolling Bearing Fault Diagnosis in Wind Turbine Generator Systems
Rolling bearings in wind turbine generator systems operate under variable speed and load conditions that induce significant data distribution shifts between training and field data, while unknown fault modes absent from the source domain are frequently misclassified as known classes. This study proposes a multi-classifier open adversarial network (MCOAN) for open-set fault diagnosis. Within an adversarial domain adaptation framework, a K-way classifier and an additional K+1-way one-vs-all classifier independently estimate target-sample similarity to the source domain. These similarity scores drive a dynamic weighting mechanism that adaptively reweights target samples during open-set adversarial training and supplies per-sample dynamic thresholds for known/unknown discrimination, thereby promoting cross-domain alignment of shared-class features while suppressing negative transfer from unknown samples. A non-adversarial domain classifier is introduced to stabilize dynamic weight estimation. Validation on two datasets demonstrates high-precision shared-class distribution alignment and unknown-class recognition with favorable robustness. The method removes reliance on empirically preset thresholds that plague conventional open-set back-propagation approaches, where a fixed threshold of 0.5 in the binary cross-entropy adversarial loss provides no per-sample adaptivity. By coupling K-way and K+1-way similarity estimates, MCOAN achieves simultaneous known-class alignment and unknown-class separation without prior knowledge of the unknown-class cardinality, addressing a persistent bottleneck in wind turbine drivetrain condition monitoring where unanticipated bearing failure modes emerge under field conditions.
Unsupervised Automated Identification Method for Abnormal States of Wind Turbine Gearboxes
Addressing the scarcity of labeled data for training classification models in wind turbine planetary gearbox anomaly identification, this study proposes an unsupervised automated detection method. Log Mel-band energy features are extracted from raw vibration signals and fed into an unsupervised anomaly recognition model centered on a U-net autoencoder. A health-state threshold is established based on reconstruction error between model input and output, enabling anomaly identification. The method is validated using factory gearbox test data and operational data from a wind farm in Yangtouya, Shanxi. For factory gearboxes, dual validation is performed using a spectrum amplitude modulation-based signal processing method. Results demonstrate that the proposed method achieves 93.34% recognition accuracy on both factory and wind farm test sets, confirming its capability to automatically and correctly separate abnormal wind turbine gearboxes. The approach eliminates reliance on labeled fault data, offering a scalable solution for full-lifecycle health monitoring, from factory acceptance testing to in-service early anomaly detection, adaptable across different operating conditions and turbine models.
Improved Adaptive Super-Twisting Sliding Mode Control for Permanent Magnet Synchronous Motors
Adaptive super-twisting sliding mode control (ASTSMC) for permanent magnet synchronous motors (PMSM) suffers from prolonged convergence and insufficient disturbance rejection under complex operating conditions. This paper proposes an improved ASTSMC incorporating a fixed-time disturbance observer (FTDO). A power term is introduced into the adaptive super-twisting controller to accelerate convergence far from the origin, while the discontinuous sign function is replaced by a continuous h(s) function to mitigate chattering. The FTDO ensures disturbance estimation converges within a fixed time independent of initial states, overcoming the limitations of traditional and finite-time observers. The estimated disturbance is fed forward to the sliding mode controller for compensation, enhancing robustness. The FTDO design is based on an auxiliary state variable z and its derivative, with error dynamics analyzed via Lyapunov stability. Comparative simulations against conventional disturbance observers and sliding mode controllers validate the proposed strategy. The results demonstrate shorter convergence time, improved dynamic performance, and superior disturbance rejection, making the approach suitable for high-performance servo systems. The method addresses the critical need for robust, fast-response control in electric vehicles, rail transit, and aerospace applications where PMSM drives face significant uncertainties and external disturbances.