作者丨欧雪 编辑丨袁斯来 随着人工智能、自动驾驶、量子通信等前沿技术迅猛发展，作为底层支撑的光子技术正成为全球科技竞争的战略高地。在这一背景下，陕西于2021年启 动"追光计划"。 11月4日，在西安举行的2025硬科技创新大会光子产业高峰会议上，陕西光电子先导院宣布，西北地区首条8英寸先进硅光集成技术创新平台（以下简称"8英 寸硅光平台"）正式通线。这一突破不仅填补了区域产业链空白，更成为陕西打造千亿级光子产业集群的核心支点。 "全球硅光中试资源多集中在欧美企业手中，国内企业依托国外平台流片需支付高昂费用，且面临周期长、产能受限等问题。"陕西光电子先导院总经理杨军 红在会议现场指出。她表示，自建产线所需巨额设备投入，更让中小企业和科研团队望而却步。 "这是西北首个硅光领域中试平台，加上我们之前建设的6英寸化合物平台，我们已成为国内少数具备全链条中试能力的创新载体。"杨军红介绍，该平台采 用"1+N"柔性工程模式，主工艺平台80%资源向中小企业开放中试流片服务，20%用于支持前沿研发，实现资源高效配置与产业协同。 陕西光电子先导院副总经理付鹏在接受36氪采访时指出，人工智能的爆发对算力和数据传输提出了更高要求 ...

Core Insights - DeepSeek has released a new multimodal model called DeepSeek-OCR, which has sparked significant discussion in the industry regarding its potential applications in optical and quantum computing [1] - The model's visual encoder enables efficient decoding, providing a clear technical pathway for integrating optical computing into large language models (LLMs) [1] Group 1: Contextual Optical Compression - DeepSeek has introduced "Contextual Optical Compression" technology, which processes text as images to achieve efficient information compression, theoretically allowing for infinite context [3] - This technology can compress tokens by 7 to 20 times; for instance, converting a page of text that typically requires 2000-5000 tokens down to just 200-400 visual tokens [3][4] - The model maintains 97% decoding accuracy at 20x compression, with 60% accuracy still achievable at 20x compression, which is crucial for implementing LLM memory's forgetting mechanism [4] Group 2: Optical Computing Integration - By transforming text problems into image problems, DeepSeek's OCR technology may pave the way for the integration of optical computing chips into large language models [5] - Optical computing chips are seen as a potential technology for the "post-Moore era," leveraging light-speed transmission, high parallelism, and low power consumption for AI and other computation-intensive tasks [5] - The DeepEncoder component of DeepSeek-OCR is particularly suited for execution by optical co-processors, while the text decoding will still be handled by electronic chips [5] Group 3: Challenges and Industry Landscape - Current challenges for optical computing include advanced optoelectronic integration and the maturity of the software ecosystem, which hinder large-scale development and optimization [6] - Key players in the domestic market include companies like Xizhi Technology and Turing Quantum, while international competitors include Lightmatter and Cerebras Systems [6][7] - Turing Quantum has made significant progress in the mass production of thin-film lithium niobate (TFLN) products, but it may take 3 to 5 years to compete with GPUs in data centers due to engineering, cost, and ecosystem challenges [7]

Core Viewpoint - Beijing is set to support general artificial intelligence (AI) operational services with a maximum funding of 30 million yuan to accelerate the development of a globally influential AI innovation hub and industrial base [1][2] Group 1: Support for Innovation - Beijing will implement multiple measures to support significant original innovations, including enhancing the innovation mechanisms of new research institutions like the Zhiyuan Institute [1] - The focus will be on forward-looking fields such as embodied intelligence, AI in life sciences, and AI for science, while also strengthening technological reserves for future industrial development [1] Group 2: Financial Support and Development - The city will provide up to 30 million yuan in support for general AI operational services that significantly enhance manufacturing efficiency and optimize production management [2] - There will be a push for integrated development of data and applications, promoting collaborative efforts across departments and regions to deploy large model products [2] Group 3: Talent and Environment - Efforts will be made to cultivate young talent in the AI field through national-level AI academies, aiming to establish a talent hub [2] - The technology finance service system will be improved, with increased direct investment from municipal and district-level AI industry funds to support enterprise growth [2] Group 4: International Collaboration - Beijing aims to enhance its international influence in AI by building a global "open-source capital," fostering international dialogue on AI safety and research cooperation [2] - The goal is to establish a global consensus on AI safety and governance, contributing to the realization of a shared future for humanity [2] Group 5: Industry Growth - By 2024, the number of AI enterprises in Beijing is expected to exceed 2,400, with a core industry scale nearing 350 billion yuan, representing half of the national totals in both enterprise count and industry scale [2]