Bridging Sensor and SoC: Co-Optimization Strategies for Next-Generation Vision

As imaging moves toward smarter, faster, and more efficient systems, modern vision applications demand more than incremental improvement. They require deeper synergy and better co-optimization between sensors and SoCs.

Design choices made across the imaging pipeline can influence system performance, image quality, power, latency, complexity, and integration effort. Shutter architecture, RGB-IR processing, and the placement of intelligence within the system therefore need to be considered as connected parts of the same vision solution.

At ImagingNext 2026, Marie-Charlotte Leclerc, Product Marketing Manager at STMicroelectronics, presents “Bridging Sensor and SoC: Co-Optimization Strategies for Next-Generation Vision” – a session on the practical decisions involved in building smarter and more efficient imaging systems.

About Marie-Charlotte Leclerc

Marie-Charlotte Leclerc is Product Marketing Manager for CMOS Image Sensors at STMicroelectronics. With an engineering degree in optics & semiconductor physics alongside an MBA, she specializes in translating complex imaging technologies into commercial go-to-market strategies. Building on her track record in machine vision at Teledyne, she leads global product definition and strategic partnerships across industrial automation, smart IoT, and embedded vision sectors. Most notably, she drove the strategy behind ST BrightSense, ST’s novel open-market image sensor portfolio engineered to eliminate integration barriers through intelligent features and a comprehensive ecosystem.

Why sensor and SoC co-optimization matters

What does it take to build the next generation of vision systems? As imaging moves toward smarter, faster, and more efficient solutions, the answer lies in better co-optimization between sensors and SoCs.

The session will examine the practical trade-offs behind different shutter architectures and show how on-chip processing can simplify RGB-IR imaging. Moving processing closer to the sensor can affect how the system handles imaging data and how its different components are integrated.

Another important decision is how to partition intelligence across sensors, ISPs, SoCs, and MCUs. The placement of processing within the imaging pipeline affects image quality, power consumption, latency, integration effort, overall performance, and system complexity.

Understanding these relationships can provide a clearer view of where processing should take place. It can also reveal new opportunities for robotics and edge AI, where imaging systems need to combine performance and efficiency with manageable integration requirements.

The presentation will reference sensor-side pixel and processing innovations featured in the latest public CMOS image sensors from STMicroelectronics. These references will be used as technical examples of approaches for improving image quality, system efficiency, and overall sensor and ISP co-optimization within a broader technical narrative.

The session will also look ahead to multi-data-path smart vision and the role of open ecosystems in accelerating innovation across the imaging value chain. An open-source-oriented approach to imaging can make integration, experimentation, and collaboration easier for developers and the wider ecosystem.

What you’ll take away

• How shutter architectures and on-chip processing can simplify RGB-IR imaging.
• How to partition intelligence across sensors, ISPs, SoCs, and MCUs for better performance and lower system complexity.
• How processing choices affect image quality, power, latency, integration effort, robotics, and edge AI opportunities.
• How open ecosystems can support integration, experimentation, collaboration, and innovation across the imaging value chain.