Semiconductor Sequencing Biosensor Using Shared Pixels for Base Calling
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Solution Overview
Problem
Conventional solid-state imaging systems for fluorescent detection in biological or chemical analysis are limited by pixel density, leading to low throughput and increased costs due to the need for large optical systems and reduced accuracy in detecting nucleic acid arrays.
Innovation Solution
A device with a biosensor array that generates multiple sequences of pixel signals from a sample surface, allowing for base calling of multiple clusters per sensor pixel, using a signal processor to classify results from fewer sensors than clusters, and employing illumination stages to differentiate nucleotide bases A, C, T, and G.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional solid-state imaging systems are used for fluorescent detection, then the system structure is simplified compared to optical systems, but the throughput is limited due to pixel density constraints
Solution Approach 1:
Multiple clusters are merged into a single pixel area, allowing one sensor pixel to detect signals from multiple clusters simultaneously. This combining approach increases throughput without requiring additional pixels or increasing pixel density.
Solution Approach 2:
The system transitions from one-to-one mapping (one cluster per pixel) to many-to-one mapping (multiple clusters per pixel). By changing the dimensional relationship between clusters and pixels, the system achieves higher throughput while maintaining the same sensor array size.
2Productivity
If pixel density is increased to improve throughput, then more clusters can be detected per sensor, but the pixel pitch cannot be significantly decreased due to manufacturing limitations
Solution Approach 1:
Instead of decreasing pixel pitch to pack more pixels, the invention combines multiple clusters within existing pixel areas. This approach achieves higher throughput without requiring manufacturing improvements in pixel density.
Solution Approach 2:
The system changes the detection parameter from one-cluster-per-pixel to multi-cluster-per-pixel. This parameter change allows throughput to scale with the number of clusters per pixel rather than being constrained by pixel pitch manufacturing limits.
3Measurement precision
If one cluster is assigned per sensor pixel, then detection accuracy is maintained, but throughput is reduced due to the need for large sensor arrays
Solution Approach 1:
Multiple clusters are combined in each pixel area with signal processing that maintains detection accuracy. The system resolves individual cluster signals even when multiple clusters occupy the same pixel area, preserving measurement precision while increasing throughput.
Solution Approach 2:
The system uses computational methods to create virtual distinctions between clusters that share the same physical pixel. Through signal processing and analysis, the system effectively copies the one-to-one mapping relationship computationally even though multiple clusters physically share a pixel.
4Productivity
If the number of sensors is increased to detect more clusters simultaneously, then throughput increases, but the cost and device complexity increase
Solution Approach 1:
Multiple clusters are detected by a single sensor pixel through signal combining. This approach increases the effective capacity of each sensor without adding more sensors, thereby increasing throughput while reducing device complexity and cost.
Solution Approach 2:
Each sensor pixel is designed to perform multiple functions: detecting signals from multiple different clusters within the same pixel area. This multi-functionality allows a fixed sensor array to handle variable numbers of clusters, increasing throughput without proportionally increasing sensor count.
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 significantly increases throughput and reduces costs by enabling the detection of nucleic acid arrays with higher accuracy and efficiency, allowing for the simultaneous analysis of multiple clusters with fewer sensors, thus enhancing the speed and precision of sequencing processes.
Implementation Method 1
the controlled reactions occur immediately over a solid-state imager (e.g., charged-coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) sensor) that does not require a large optical assembly to detect the fluorescent emissions
Implementation Method 2
an optical system is used to direct an excitation light onto fluorescently-labeled analytes and to also detect the fluorescent signals that may emit from the analytes
Data Source
AI summary
A biosensor for base calling is provided. The biosensor comprises a sampling device, which includes a sample surface that has an array of pixel areas and a solid-state imager that has an array of sensors. Each sensor generates pixel signals in each base calling cycle. Each pixel signal represents light gathered in one base calling cycle from one or more clusters in a corresponding pixel area of the sample surface. The biosensor further comprises a signal processor configured for connection to the sampling device. The signal processor receives and processes the pixel signals from the sensors for base calling in a base calling cycle, and uses the pixel signals from fewer sensors than a number of clusters base called in the base calling cycle. The pixel signals from the fewer sensors include at least one pixel signal representing light gathered from at least two clusters in the corresponding pixel area.