光电子芯片是“后摩尔时代”未来光电信息领域核心技术之一。光电子学的发展为实现更高性能光电子器件与微电子电路的异质集成芯片奠定了基础。当电子芯片在物理极限悬崖边徘徊时，光电智能计算芯片已点燃新的火炬，引领着技术的变革。从“硅基囚笼”到“光子自由”，这场变革将突破电子芯片的算力瓶颈。同时，人工智能也为光电技术的研发提供了新的方法和工具。通过机器学习算法可以优化光电材料的合成工艺和器件的设计参数，提高研发效率和成功率。这种人工智能与光电技术的深度结合，不仅推动了光电技术的智能化发展，也为人工智能的硬件实现提供了新的可能性，是未来前沿科技发展的重要趋势之一。从前沿科技的角度来看，这种跨学科的研究模式是解决复杂科技问题的重要途径，也是推动科技创新的重要动力。因此，光电子交叉实验室旨在通过原子尺度电镜和光谱技术，深入理解半导体器件的材料物理和器件特性；并结合人工智能和机器学习开发高性能的半导体光电子材料，继而探索新一代的空间光伏器件、微型显示芯片、光电融合计算以及材料智能设计新范式。
特种空间光伏器件：研究有机半导体和有机-无机杂化半导体以及它们在太阳能电池和空间特种环境中的应用潜力，进而开发超轻质装备电源。
LED微型显示器件：研究聚焦新型有机和有机-无机杂化半导体发光材料，同时探索能够跟硅基CMOS工艺兼容的微型显示器芯片。
光电融合计算芯片：结合光计算和电子计算的新型芯片架构，旨在突破传统电子芯片的性能瓶颈，满足大规模计算需求，从而解决传统电子芯片算力瓶颈。
光电材料智能设计：利用机器学习算法和大规模数据分析，AI能够预测材料性能、优化合成路径，并识别新的材料候选对象，从而显著减少传统试错法在材料科学中所耗费的时间和成本。
罗德映 bhldy@cq-qh.com
Optoelectronics Interdisciplinary Laboratory
Photonic chips are pivotal for future optoelectronic technology in the post-Moore era. They enable the heterogeneous integration of high-performance optoelectronic devices with microelectronics, overcoming the physical limitations of electronic chips. This transition from “silicon cage” to “photonic freedom” addresses computational bottlenecks. Meanwhile, artificial intelligence (AI) enhances optoelectronics by optimizing semiconductor material synthesis and device design through machine-learning algorithms, improving efficiency and success rates. The integration of AI and optoelectronics accelerates optoelectronic evolution and offers new hardware solutions for AI, becoming a key trend in cutting-edge science and technology. This interdisciplinary research is crucial for solving complex problems and driving innovation. The Optoelectronics Interdisciplinary Lab aims to use atomic-scale electron microscopy and spectroscopy to understand semiconductor materials and device physics, integrate this knowledge with AI, and develop high-performance optoelectronic materials for applications in space, micro-display chips, optical computing, and intelligent materials design.
Space Photovoltaics: Our research is centered on emerging semiconductor materials, particularly organic semiconductors and organic-inorganic hybrid semiconductors, and their potential applications in space photovoltaics.
LED Microdisplay: The research focuses on novel organic and organic-inorganic hybrid semiconductor light-emitting materials, while exploring microdisplay chips compatible with silicon-based CMOS processes.
Optical Computing: By combining photonic and electronic computation, the new chip architecture is designed to shatter the performance ceiling of conventional electronics and meet the demands of large-scale computing — directly breaking the computational bottlenecks of traditional electronic chips.
Al for Materials: Leveraging machine-learning algorithms and large-scale data analytics, AI can predict material properties, optimize synthesis routes, and identify novel candidate materials — dramatically slashing the time and cost of traditional trial-and-error in materials science.
If you are interested, please contact Professor Deying Luo (bhldy@cq-qh.com).
