Paper
Physics-Informed AI for Laser-Enhanced Contact Optimization in Silicon PV: Electrothermal Activation, Degradation Regimes, and Process Control
Authors
Donald Intal, Abasifreke U. Ebong
Abstract
Laser-enhanced contact optimization (LECO) is increasingly used method to reduce contact resistance and recover fill factor in advanced crystalline silicon solar cells. However, the industrial transferability is limited because the same localized activation that improves carrier transport can also create kinetically unstable interface states. LECO can be viewed as a coupled Multiphysics process that links microstructural evidence to device-level signature that uses instantaneous regime map together with a reliability classification based on time-dependent drift. Thus, a predictive workflow is outlined in the review that couples (i) transient electrothermal modeling to reduced state metrics, (ii) effective diffusion depth and local areal energy density, and (iii) propagated calibrated thresholds across recipe space. The framework separates stable optimization from marginal activation and latent damage and explains why fine-line scaling and copper-containing stacks tighten stability margins through current localization and diffusion-barrier constraints. It sum up the next generation AI guided optimization digital twin.
Metadata
Related papers
Vibe Coding XR: Accelerating AI + XR Prototyping with XR Blocks and Gemini
Ruofei Du, Benjamin Hersh, David Li, Nels Numan, Xun Qian, Yanhe Chen, Zhongy... • 2026-03-25
Comparing Developer and LLM Biases in Code Evaluation
Aditya Mittal, Ryan Shar, Zichu Wu, Shyam Agarwal, Tongshuang Wu, Chris Donah... • 2026-03-25
The Stochastic Gap: A Markovian Framework for Pre-Deployment Reliability and Oversight-Cost Auditing in Agentic Artificial Intelligence
Biplab Pal, Santanu Bhattacharya • 2026-03-25
Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA
Saahil Mathur, Ryan David Rittner, Vedant Ajit Thakur, Daniel Stuart Schiff, ... • 2026-03-25
MARCH: Multi-Agent Reinforced Self-Check for LLM Hallucination
Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, Mengyu Zhou, Hao Li, Shujie... • 2026-03-25
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.23351v1</id>\n <title>Physics-Informed AI for Laser-Enhanced Contact Optimization in Silicon PV: Electrothermal Activation, Degradation Regimes, and Process Control</title>\n <updated>2026-03-24T15:52:34Z</updated>\n <link href='https://arxiv.org/abs/2603.23351v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.23351v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Laser-enhanced contact optimization (LECO) is increasingly used method to reduce contact resistance and recover fill factor in advanced crystalline silicon solar cells. However, the industrial transferability is limited because the same localized activation that improves carrier transport can also create kinetically unstable interface states. LECO can be viewed as a coupled Multiphysics process that links microstructural evidence to device-level signature that uses instantaneous regime map together with a reliability classification based on time-dependent drift. Thus, a predictive workflow is outlined in the review that couples (i) transient electrothermal modeling to reduced state metrics, (ii) effective diffusion depth and local areal energy density, and (iii) propagated calibrated thresholds across recipe space. The framework separates stable optimization from marginal activation and latent damage and explains why fine-line scaling and copper-containing stacks tighten stability margins through current localization and diffusion-barrier constraints. It sum up the next generation AI guided optimization digital twin.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='physics.app-ph'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cond-mat.mtrl-sci'/>\n <published>2026-03-24T15:52:34Z</published>\n <arxiv:primary_category term='physics.app-ph'/>\n <author>\n <name>Donald Intal</name>\n <arxiv:affiliation>Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, Charlotte, USA</arxiv:affiliation>\n </author>\n <author>\n <name>Abasifreke U. Ebong</name>\n <arxiv:affiliation>Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, Charlotte, USA</arxiv:affiliation>\n </author>\n </entry>"
}