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<p>The rapidly growing computational demands of artificial intelligence (AI) and complex optimization tasks are increasingly straining conventional electronic architectures, driving the search for novel, energy-efficient processing paradigms. Photonic computing, which harnesses the unique properties of light to perform computation, has emerged as a compelling alternative. This perspective highlights a key advancement: a versatile nonlinear optoelectronic engine based on integrated photodetectors and micro-ring modulators (PD + MRM). This engine enables crucial functionalities like nonlinear activation and signal relay, forming a core building block for monolithic photonic processors. Its application in integrating optical Ising machines for optimization and optical recurrent neural networks (RNNs) for AI has been examined recently. The PD + MRM unit’s inherent compactness, efficiency, and on-chip reconfigurable nonlinearity address historical photonic computing challenges, signaling a shift towards more versatile and scalable monolithic photonic processors.</p>

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Nonlinear optoelectronic engine drives monolithic integrated photonic computing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月4日

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  • Sha Zhu,
  • Ning Hua Zhu

摘要

The rapidly growing computational demands of artificial intelligence (AI) and complex optimization tasks are increasingly straining conventional electronic architectures, driving the search for novel, energy-efficient processing paradigms. Photonic computing, which harnesses the unique properties of light to perform computation, has emerged as a compelling alternative. This perspective highlights a key advancement: a versatile nonlinear optoelectronic engine based on integrated photodetectors and micro-ring modulators (PD + MRM). This engine enables crucial functionalities like nonlinear activation and signal relay, forming a core building block for monolithic photonic processors. Its application in integrating optical Ising machines for optimization and optical recurrent neural networks (RNNs) for AI has been examined recently. The PD + MRM unit’s inherent compactness, efficiency, and on-chip reconfigurable nonlinearity address historical photonic computing challenges, signaling a shift towards more versatile and scalable monolithic photonic processors.