In 2011, a notable trend emerged where CCD image sensors gained more traction than expected over CMOS alternatives. However, this shift was viewed as merely a slow adoption phase rather than a long-term indicator. Despite differing data interpretations, the consensus remains that CMOS will dominate machine vision applications.

The rapid expansion of consumer electronics propelled significant quality improvements for CMOS sensors. In recent years, their resolutions and data quality have approached those of CCD sensors. Many now feature global or snapshot shutters, enabling distortion-free capture of moving objects—a development making CMOS a highly viable alternative even in high-precision machine vision and industrial settings.

System designers were initially captivated by the enhanced image quality, higher frame rates, and potential cost savings offered by CMOS technology. It appeared to possess all advantages without drawbacks—like many emerging technologies. Yet, adoption for machine vision has been remarkably slow. After testing CMOS-based cameras, numerous issues surfaced, such as dark current unless manufacturers had addressed it. These artifacts were distinct from those in CCD systems, which were well understood.

Furthermore, transitioning to CMOS required developing new algorithms and software—a significant hurdle that slowed its integration into existing workflows. This phase aligns with the “trough of disillusionment” stage of Gartner’s Hype Cycle, where initial excitement gives way to challenges as designers work to understand and refine the technology.

Now, we are entering a period of “slope of enlightenment,” marked by increased system integrations and full-scale deployment of CMOS-based solutions. Given that CCD usage remains limited in consumer electronics compared to CMOS, further adoption is inevitable. Supported by extensive R&D and substantial investment, CMOS image sensors’ advantages are poised for continued growth.

Last Updated: 2025-09-04 19:05:25