Microbolometer Detection via Frequency Profile Analysis
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Solution Overview
Problem
Current uncooled microbolometer detection systems face challenges in distinguishing between different types of microbolometers due to their unique spectral emission signatures, which are influenced by the pulse bias frequency, intensity, and thermal characteristics, making it difficult to accurately detect and identify them.
Innovation Solution
A system comprising a photodiode, analog-to-digital converter (ADC), and processor that samples and analyzes the frequency profile of emitted radiation to identify fundamental frequencies and harmonic ratios, allowing for the differentiation between microbolometer types and detection of their presence, using a frequency sampling rate at least two times the expected frequency range of known microbolometers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pulse bias readout is used to measure microbolometer resistance, then temperature measurement capability is improved, but device resistance remains low requiring complex readout circuits
Solution Approach 1:
The patent applies periodic pulse bias readout where the microbolometer is pulsed at a specific frequency (e.g., 100 Hz) to periodically heat and cool the detector element. This periodic action creates a characteristic thermal signature that can be detected and used to identify the microbolometer type, resolving the complexity of continuous readout while maintaining measurement precision.
Solution Approach 2:
The patent changes the readout parameter from continuous DC bias to periodic pulsed bias with specific frequencies and duty cycles. By varying the pulse frequency and width, the system creates distinct thermal responses that encode microbolometer type information, simplifying the readout circuit while improving measurement capability.
2Measurement precision
If microbolometers are made highly absorptive in the 8-12 μm band, then detection sensitivity is improved, but thermal isolation requirements increase
Solution Approach 1:
The patent utilizes the microbolometer's own thermal response to ambient temperature changes as a signature identifier. By monitoring how the detector element naturally heats and cools between pulses, the system extracts type information without requiring additional thermal isolation structures, allowing the device to serve its own identification needs.
3Measurement precision
If frequency sampling rate is increased to at least two times the expected frequency range, then microbolometer type identification accuracy is improved, but data processing load increases
Solution Approach 1:
The patent extracts only the relevant frequency components from the sampled data by analyzing the thermal response at the known pulse frequency and its harmonics. Instead of processing the entire frequency spectrum, the system isolates and analyzes only the characteristic frequencies that identify microbolometer types, reducing computational load while maintaining identification accuracy.
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
Enables accurate detection and identification of microbolometers by converting sampled sequences into frequency profiles, determining the presence and type based on fundamental and harmonic frequencies, thereby overcoming the limitations of existing systems in distinguishing between different microbolometer signatures.
Implementation Method 1
a photodiode, an analog to digital converter (ADC) and a processor
Implementation Method 2
The microbolometers are designed to be highly absorptive in the spectral band of interest (typically the 8-12 μm long-wave IR band). As the microbolometers absorb the optical energy their temperature rises. This temperature is measured by the bolometric effect
Implementation Method 3
the resistance of the microbolometer is a function of its temperature
Implementation Method 4
The 'pulse bias' readout also causes a microbolometer to heat through joule heating effect
Implementation Method 5
the spectral emission of the detectors is equal to the spectral absorption and detectors radiate in the long wave IR until thermal equilibrium is reached
Data Source
AI summary
Systems for detecting a presence of a microbolometer are provided. The systems may also discriminate a type of the microbolometer. The systems may comprise analog filtering and processing or digital filtering and processing. In some examples, both analog and digital filtering may be used. For example, a system may comprises a photodiode, an analog to digital converter (ADC) having a frequency sampling rate of at least two times an expected frequency range of known microbolometers and a processor. The ADC may receive an amplified output from the photodiode and produce a sampled sequence using the frequency sampling rate. The processor converts the sampled sequence into a frequency profile, examines the frequency profile to identity at least a fundamental frequency and determines whether a microbolometer is detected in a line of sight of the photodiode based on the fundamental frequency.