Inverse Neural Network for Solid-State Detector Signal Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solid-state detectors face challenges in accurately estimating the location, energy level, and time of incident radiation due to material property variations and imperfect fabrication processes, leading to sub-optimal detector performance and increased manufacturing costs.
Innovation Solution
A machine-learned model, such as a neural network, is trained to estimate the location, energy level, and time of incident radiation on a solid-state detector, accounting for material property variations and enabling rapid, accurate calculations with minimal data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional inversion methods are used to solve the inverse problem, then measurement accuracy can be maintained, but computational power and time requirements increase significantly
Solution Approach 1:
The patent pre-calculates and stores response functions for all possible interaction positions in a lookup table during system initialization. This preliminary action eliminates the need for complex real-time matrix inversions during actual particle detection, reducing computational power requirements while maintaining measurement precision through direct signal matching against pre-computed reference data
Solution Approach 2:
The patent creates simplified copies of the complex inverse problem by pre-computing response functions that represent the detector's behavior under various conditions. These copied response patterns are stored and used for rapid comparison during operation, replacing computationally intensive real-time calculations with faster lookup and matching operations
2Measurement precision
If traditional inversion methods with large databases are used, then location estimation accuracy improves, but device complexity and data storage requirements increase
Solution Approach 1:
The patent extracts only the essential response function characteristics needed for particle detection from the complex detector system. By identifying and storing only the critical response patterns in the lookup table rather than complete raw data, the system achieves accurate location estimation with reduced data storage requirements and lower device complexity
Solution Approach 2:
The patent performs preliminary processing to distill complex detector responses into compact, essential response function representations before storage. This pre-extraction of key features reduces the volume of data that needs to be stored in the lookup table while preserving the information necessary for accurate particle location determination
3Measurement precision
If optimal material properties are required for detector quality, then measurement precision improves, but manufacturing cost increases
Solution Approach 1:
The patent changes the approach from requiring optimal physical material parameters to using computational parameters (response functions) that can be pre-calculated and stored. This parameter transformation allows the use of lower-quality, less expensive materials while maintaining measurement precision through software-based compensation for material imperfections
Solution Approach 2:
The patent creates computational models (response functions) that copy and represent the detector's behavior under various conditions. These models allow the system to account for material variations and imperfections through data processing rather than requiring physically perfect materials, thereby reducing manufacturing costs while maintaining detector quality
4Measurement precision
If detailed material property variations are accounted for in real-time, then measurement precision improves, but computational time increases
Solution Approach 1:
The patent pre-calculates response functions that incorporate material property variations for all possible interaction positions during system initialization. By performing this computationally intensive work in advance, the system can rapidly compensate for material variations during actual particle detection through simple lookup and comparison operations, maintaining measurement precision while minimizing real-time computational time
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
The proposed solution enables more accurate and efficient detection of incident radiation, allowing for the use of detectors with worse material uniformity while maintaining high-quality estimations, and reducing computational power and time requirements.
Implementation Method 1
Signals induced due to the drift of charges (e.g., electrons and holes) within a solid-state device are characteristics of the material properties of the device itself
Data Source
AI summary
For training to and/or estimating location, energy level, and/or time of occurrence of incident radiation on a solid-state detector, a machine-learned model, such as a neural network, performs the inverse problem. An estimate of the location, energy level, and/or time is output by the machine-learned model in response to input of the detected signal (e.g., voltage over time). The estimate may account for material property variation of the solid-state detector in a rapid and easily calculated way, and with a minimal amount of data.