Significance As semiconductor manufacturing progresses towards sub-Xnm nodes and adopts 3D device architectures, such as gate-all-around field-effect transistors and 3D-NAND, wafer structures now feature extremely high aspect ratios and complex heterogeneous integration. This development imposes strict requirements on optical inspection and metrology, which are crucial for process control. Traditional inspection methods, which regard illumination as a static and passive source and mainly rely on backend algorithmic processing, are becoming increasingly insufficient. Their drawbacks, including instability in dynamic environments, substantial signal-to-noise issues in detecting nanoscale defects, and limited three-dimensional awareness, highlight the necessity for a revolutionary change. Therefore, next-generation optical inspection systems should transform into intelligent, integrated platforms based on the synergy of two key technologies: dynamic light field manipulation and multidimensional information fusion perception. This reconfigures the system from a passive observer to an active probe, where lighting and sensing are dynamically coupled to optimize detection from the physical signal generation stage.

Progress The application of light field shaping in engineering clearly and logically evolved to cope with ever more challenging demands of semiconductor inspection. The first generation was called static light field shaping, addressing the fundamental need of converting an inherently Gaussian profile output from most lasers into useful, uniform profiles required by scanning applications. This was done using passive optical elements such as a diffractive optical element and a microlens array that were able to create a flat-top line illumination or flat-top spot illumination, thus establishing a fundamental hardware framework. With increasing complexity in inspection conditions and the need for flexibility, it evolved into active light field control: active, programmable devices, such as digital micromirror devices or liquid-crystal spatial light modulators. All of these contributed to a vital capability: the ability to shift quickly from one type of light to another, or to generate complex, programmable, structured light patterns in real-time and thus match the light field to particle inspection tasks. However, only achieving dynamic control did not ensure stable, high-fidelity performance for multiple tools and over time. This realization gave rise to step three: adaptive light field correction. We now looked inwards at compensating for the intrinsic flaws of our systems and their variations. With tools such as wavefront correctors, integrated beam pre-processing modules, and computational algorithms that enabled active measurement and correction of the static aberrations due to component tolerances or assembly stresses. This “pre-standardization” of the input beam ensured that downstream modulation stages received a consistent, high-quality light field, which is critical for tool-to-tool matching and long-term stability. The ultimate stage in this progression is closed-loop light field self-calibration, which looks outward to combat real-time environmental perturbations. To build a high-bandwidth “sense-analyze-act” feedback loop—using fast sensors and smart processors (usually augmented with artificial intelligence), and fast actuators—whereby the system has the capability of sensing and correcting in real time, transient disturbances such as vibration or thermal drift, transforming into an autonomous adaptive agent. Based on such a four-phase evolutionary process, with the principle of “multi-dimensional fusion and collaborative control”, we develop a three-layer engineering application architecture for addressing the critical challenges in contemporary examination. It should be noted that this design can be viewed as a natural combination of these three levels: the physical execution layer forms its foundation, including devices such as spatial light modulators or digital micromirror devices, which directly shape the light field’s phase, amplitude, and pattern. The physical principal layer is the strategy layer that turns physical principles into practical strategies for breaking through conventional barriers. It applies techniques like structured illumination microscopy to obtain super-resolution images, active noise immunity and polarization engineering to improve the signal-to-noise ratio, and quantitative phase imaging for 3D topography sensing. Computational algorithm layer is an intelligent decision maker that drives the whole system by solving inverse problems, conducting AI-based defect classification, and allowing for adaptive optimization and closed-loop control, which ties everything together. There is a tight coupling between all three of those layers—with instructions guiding how they are implemented on hardware, and software refining both—that constitute an integrated smart system that is ready to tackle any challenging inspection task.

Conclusions and Prospects We have traced the evolution of light field control from a passive element into that which forms the heart of an active intelligent probing system for semiconductor optical inspection. Logical progression of the ideas, starting from static shaping and moving towards closed-loop self-calibration, paves the way forward towards realizing dynamic, resilient optical systems. Our suggested three-tiered application architecture provides an overall systematic approach for designing the next generation of inspection tools, effectively channeling advances in hardware, physics, and algorithms to overcome the intertwined problem of resolution, accuracy, speed, and three-dimensional vision. In the future, further development in this direction depends on closer integration with state-of-the-art computer intelligence and new optical materials. Highlights of the frontier include generative artificial intelligence driven inverse design that promises to discover non-intuitive, high-efficiency photonic architectures beyond human’s intuition; development of deep learning enhanced expert system, which is a hybridization between data driven learning and structured physics knowledge for robust and efficient optimization; and research on large model-based autonomous agent to perceive and make decisions about a whole scene in complicated inspection environment. Meanwhile, with the emergence of new hardware such as dynamic metasurface integrated photonic circuits, we may be able to make even more compact and flexible modulators for controlling light fields. We believe that this combination of smart software (algorithms) and hardware devices would enable autonomous and powerful light field manipulation, consolidating its position as the essential “eyes” for precision manufacturing of the semiconductor industry and beyond.