Photoelectric Conversion Signal Processing With Parallel AI Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing photoelectric conversion apparatuses with a single processing unit based on a learned model face challenges in processing speed and load, which can lead to decreased performance in advanced signal processing tasks.
Innovation Solution
The apparatus incorporates multiple AI processing units with different learned models on a second substrate, each processing signals from photoelectric conversion units with distinct color filters, allowing for parallel processing and optimized signal handling for various color filters, thereby increasing processing speed and reducing heat generation and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single processing unit performs signal processing based on a learned model, then the apparatus structure is simplified, but processing speed decreases and the processing load increases
Solution Approach 1:
The patent divides the single processing unit into multiple AI processing units (first AI processing unit, second AI processing unit, etc.), each capable of independently performing signal processing based on learned models. This segmentation enables parallel processing of signals from different photoelectric conversion units, thereby increasing overall processing speed and reducing the processing load on each individual unit while maintaining relatively simple apparatus structure.
2Device complexity
If a single processing unit performs signal processing, then device complexity is reduced, but heat generation increases and power consumption rises
Solution Approach 1:
By segmenting the processing function across multiple AI processing units, the patent distributes heat generation across multiple components rather than concentrating it in a single unit. This dispersion of thermal load reduces the temperature increase in any one location and allows for better heat management, while still achieving the desired signal processing functionality with relatively simple apparatus structure.
3Productivity
If multiple AI processing units with different learned models are used, then processing speed and image quality are enhanced, but device complexity increases
Solution Approach 1:
The patent applies local quality by assigning different learned models to different AI processing units based on their specific functions. Each AI processing unit is optimized with a learned model tailored to its particular processing requirements (e.g., different color filters, different processing tasks), thereby achieving high processing speed and image quality for each specific function while maintaining overall apparatus simplicity through this targeted, localized optimization approach.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This configuration enhances the processing speed and efficiency of signal processing, allows for optimized processing of signals from different color filters, and reduces heat generation and power consumption, leading to improved image quality and accuracy.
Implementation Method 1
a photoelectric conversion region 101 in which a plurality of photoelectric conversion units are arranged
Data Source
AI summary
A photoelectric conversion apparatus has a first filter arranged so as to correspond to a first photoelectric conversion unit and a second filter arranged so as to correspond to a second photoelectric conversion unit. The photoelectric conversion apparatus has a first processing unit configured to process an output signal from the first photoelectric conversion unit and having a first learned model, and a second processing unit configured to process an output signal from the second photoelectric conversion unit and having a second learned model different from the first learned model.