Transient Data Acquisition with Pre-Conversion Activity Detection
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Solution Overview
Problem
Existing data acquisition systems face challenges in efficiently handling high sampling rates and accurate timing measurements for short-duration events in applications like particle physics experiments and Lidar, leading to increased complexity, power consumption, and cost due to the need to convert and store large amounts of data, much of which is not of interest.
Innovation Solution
A data acquisition system utilizing an array of sampling circuits, analog storage cells, and an activity detector to identify and convert only data of interest, reducing the conversion rate and storage requirements by temporarily storing samples at gigahertz rates and using time-interleaved sampling arrays with a Dynamic Window Selector to manage data flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high sampling rates are used to accurately capture short-duration collision artifacts, then measurement precision is improved, but the quantity of data to be converted and stored increases significantly
Solution Approach 1:
The patent implements preliminary action by using a coarse quantizer and activity detector to identify regions of interest in the input signal before performing high-precision analog-to-digital conversion. This pre-screening process marks only those time periods containing collision artifacts for subsequent high-resolution conversion, avoiding the need to convert and store all sampled data at high rates.
Solution Approach 2:
The patent applies local quality by using different conversion strategies for different portions of the input signal. Regions identified as containing collision artifacts undergo high-precision gigahertz-rate analog-to-digital conversion, while regions without artifacts use lower-resolution coarse quantization. This localized approach optimizes measurement precision where needed while minimizing overall data volume.
2Measurement precision
If all sampled data is converted to digital domain at high rates, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the data acquisition process into multiple stages: coarse quantization of all samples, activity detection to identify regions of interest, and selective high-precision conversion only for marked regions. This segmentation allows the system to achieve high measurement precision for collision artifacts while avoiding the complexity of converting and processing all data at high rates throughout the entire system.
3Productivity
If large amounts of data are stored in digital domain, then productivity is improved, but use of energy increases
Solution Approach 1:
The patent uses preliminary action by performing coarse quantization and activity detection before high-precision conversion, thereby identifying and marking only the small fraction of data containing collision artifacts. This preliminary screening enables the system to maintain high productivity for capturing short-duration events while dramatically reducing the volume of data requiring high-energy digital storage and processing operations.
Data Source
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AI summary
Diverse applications in particle physics experiments and emerging technologies such as Lidar are driving performance increase and cost reduction in giga-hertz sampling-rate high-resolution data conversion. In applications such as these, critical aspects of the data may occur only during relatively short nanosecond portions of observation periods lasting microseconds. Data acquisition architectures that key in on regions of the data containing activity, digitize the data, and provide info to accurately measure the position of the data in time relative to a time reference are described. These architectures may facilitate system implementation and reduce overall system cost.