spICP-MS Thresholding for Ultra-Low Nanoparticle Detection
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Solution Overview
Problem
Current ICP spectrometry methods face challenges in accurately analyzing ultra-low level concentration nanoparticles in semiconductor cleaning chemicals due to overlap with background interferences, leading to errors and variance in nanoparticle determinations.
Innovation Solution
The implementation of single particle inductively coupled plasma mass spectrometry (spICP-MS) systems with iterative filtering and local minimum analysis to determine particle baseline and detection thresholds specific to each chemical matrix and analyte, allowing for precise isolation of nanoparticle signals from background noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ICP spectrometry methods are used to analyze nanoparticles, then the analysis can be performed with standard equipment, but the measurement precision deteriorates due to overlap with background interferences
Solution Approach 1:
The patent segments the ion signal distribution into distinct components: background signal (ionic species) and nanoparticle signal (particle mode). By applying iterative filtering algorithms, the method separates these overlapping signals based on their statistical properties, allowing precise nanoparticle detection even when background interference is present. This segmentation resolves the contradiction by mathematically dividing the composite signal into its constituent parts.
Solution Approach 2:
The patent extracts the nanoparticle signal from the composite ion signal by identifying and removing background interference components. Through iterative filtering, the method isolates the particle mode signal from the ionic background, effectively taking out the harmful background interference and leaving only the nanoparticle detection data. This extraction process directly addresses the measurement precision problem caused by background overlap.
2Reliability
If standard ICP-MS analysis is used, then the equipment and methodology are simple and well-established, but the reliability deteriorates at ultra-low nanoparticle concentrations
Solution Approach 1:
The patent performs preliminary actions by establishing particle baseline intensity values and detection thresholds before actual nanoparticle quantification. The iterative filtering process pre-processes the data to remove background interference, and local minimum analysis pre-determines appropriate detection thresholds. These preliminary steps ensure that subsequent measurements at ultra-low concentrations are reliable, as the data processing framework is already optimized for detecting weak particle signals against background noise.
Solution Approach 2:
The patent implements feedback mechanisms through iterative filtering where the data processing continuously refines the separation of background and particle signals. The algorithm uses the detected signal characteristics to adjust and improve subsequent filtering iterations, creating a feedback loop that enhances detection reliability. This feedback-driven approach allows the system to adapt to varying background conditions and maintain high reliability even at ultra-low concentrations.
3Measurement precision
If conventional data processing is applied, then the analysis workflow is straightforward, but the measurement precision worsens due to inability to isolate particle signals from background
Solution Approach 1:
The patent employs dynamic data processing methodologies that adapt to the specific characteristics of each dataset. The iterative filtering process dynamically adjusts filtering parameters based on the observed signal distribution, and the local minimum analysis dynamically determines optimal detection thresholds. This dynamic approach contrasts with static conventional methods and enables precise signal isolation by continuously optimizing the separation criteria based on actual data characteristics.
Solution Approach 2:
The patent changes key processing parameters iteratively to improve signal isolation. The filtering threshold, baseline intensity values, and detection thresholds are not fixed but are adjusted through multiple iterations based on the observed data distribution. This parameter changing approach allows the methodology to optimize the separation between particle and background signals, directly improving measurement precision through adaptive parameter 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 approach enables reliable detection and quantification of nanoparticles at ultra-low concentrations, improving data accuracy and reducing variance, thus enhancing the reliability of semiconductor cleaning chemical analysis.
Implementation Method 1
When a sample is introduced to the plasma, the high temperature causes sample atoms to become ionized or emit light
Implementation Method 2
Inductively Coupled Plasma (ICP) spectrometry employs electromagnetically generated partially ionized argon plasma which reaches a temperature of approximately 7,000K
Implementation Method 3
a sample introduction system may transport an aliquot of sample to a nebulizer that converts the aliquot into a polydisperse aerosol suitable for ionization in plasma
Data Source
AI summary
Systems and methods for analyzing ultra-low level concentration nanoparticles in semiconductor cleaning chemicals by single particle inductively coupled plasma mass spectrometry (spICP-MS) are described. In aspects, the methods described herein include determination factors that are specific to each of the chemical matrix and the type of analyte associated with the nanoparticles present in a fluid sample for both a particle baseline intensity value and a nanoparticle detection threshold.