Windowed Sequencing Sensor Array Noise Reduction
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Solution Overview
Problem
Large-scale chemical sensor arrays face susceptibility to noise, which affects the accuracy of signal processing in detecting chemical and biological processes, particularly in nucleic acid sequencing.
Innovation Solution
A method is described where the operational characteristics of sensors in a sensor array are determined, and specific groups of sensors are selected based on these characteristics to improve signal quality, enabling readout of relevant sensors while bypassing others, using chemically-sensitive field effect transistors arranged in rows and columns, and converting analog output signals to digital for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If large-scale chemical sensor arrays are used to detect chemical reactions, then the detection coverage and quantity of analytes increase, but the susceptibility to noise and signal accuracy deteriorate
Solution Approach 1:
The sensor array is divided into multiple groups or regions, with each group independently read out through separate readout circuits. This segmentation allows parallel processing of signals from different sensor groups, reducing the total readout time and minimizing noise accumulation that would occur with sequential reading of all sensors, thereby maintaining measurement precision while achieving large-scale detection coverage
Solution Approach 2:
Reference sensors are introduced as intermediary elements to distinguish true chemical signals from noise. These reference sensors do not detect the target analyte but provide baseline signals that can be used to subtract background noise from the measurement signals of active sensors, thereby improving signal accuracy in large-scale arrays
2Loss of information
If all sensors in the array are read out, then complete data coverage is achieved, but readout time and processing complexity increase
Solution Approach 1:
The sensor array is divided into multiple groups with independent readout circuits, allowing parallel readout of multiple sensor groups simultaneously. This reduces the total readout time from sequential reading of all N sensors to reading M groups in parallel, where M is the number of readout circuits, thereby minimizing time loss while maintaining complete data coverage
Solution Approach 2:
The system reads out more sensor groups in parallel than strictly necessary for minimal detection, using available readout circuits to their full capacity. This excessive parallel reading capability ensures that even if some sensors are defective or noisy, sufficient data is collected efficiently, reducing readout time while maintaining data completeness
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 enhances the signal-to-noise ratio and improves the accuracy of nucleic acid sequencing by identifying and measuring sequencing reaction byproducts, leading to better sequence determination of template nucleic acids.
Implementation Method 1
The operation of the chemFET is based on the modulation of channel conductance, caused by changes in charge at the sensitive area due to a chemical reaction occurring nearby
Implementation Method 2
The presence of ions in an analyte solution alters the surface potential at the interface between the ion-sensitive layer and the analyte solution, due to the protonation or deprotonation of surface charge groups
Data Source
AI summary
In one implementation, a method is described. The method includes determining an operational characteristic of sensors of a sensor array. The method further includes selecting a group of sensors in the array based on the operational characteristic of sensors in the group. The method further includes enabling readout of the sensors in the selected group. The method further includes receiving output signals from the enabled sensors.