Photoelectric Conversion Layout With Parallel AI Color Processing
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Solution Overview
Problem
Existing photoelectric conversion apparatuses face challenges in processing speed and load due to the use of a single processing unit for advanced signal processing, which can lead to increased load and decreased performance.
Innovation Solution
The apparatus is designed with multiple AI processing units on a second substrate, each processing outputs from photoelectric conversion units with different color filters, allowing parallel processing and optimized learned models for each filter type, and employing distributed heat management to reduce temperature and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single processing unit is used for advanced signal processing, then device complexity is reduced, but processing speed and productivity decrease due to increased load
Solution Approach 1:
The patent divides the photoelectric conversion apparatus into multiple processing units, each dedicated to specific color filters. This segmentation allows parallel processing of different color channels, thereby increasing overall processing speed while distributing the computational load across multiple specialized units rather than overloading a single general-purpose processor.
Solution Approach 2:
The patent introduces a new dimension of specialization by creating processing units that are not just parallel instances but are specifically optimized for different color filter types. This dimensional differentiation in processing unit functionality enables more efficient signal processing by matching processing capabilities to the specific characteristics of each color channel.
2Measurement precision
If multiple processing units with different learned models are used, then signal processing accuracy is improved, but device complexity and power consumption increase
Solution Approach 1:
The patent applies local quality by equipping each processing unit with a learned model specifically optimized for its assigned color filter type. This localized optimization ensures that each processing unit achieves high accuracy for its specific function without requiring all units to run computationally intensive general-purpose models, thereby reducing overall power consumption while maintaining high signal processing accuracy.
3Productivity
If multiple processing units are deployed for parallel processing, then processing speed increases, but heat generation and power consumption increase
Solution Approach 1:
The patent changes the parameter of processing unit specialization, where each unit is optimized for specific color filters rather than using identical general-purpose units. This parameter change allows for more efficient processing that achieves parallel processing speed benefits while reducing the computational intensity and heat generation of each individual unit through targeted optimization.
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 processing speed, reduces heat generation, and optimizes power consumption while maintaining image quality and accuracy by enabling parallel and optimized signal processing for different color filters.
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.