Predictive Autoranging ADC for Neural Signal Saturation Control
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Solution Overview
Problem
Conventional analog-to-digital converters struggle to accurately convert small-amplitude neural electrophysiological signals due to saturation from large-amplitude transients, resulting in distorted digital outputs.
Innovation Solution
A predictive digital autoranging (PDA) analog-to-digital converter using a delta sigma modulator with a digital predictor and an analog approximator, dynamically adjusting the quantization step size to expand or contract the dynamic range based on signal polarity, allowing for efficient conversion of small-amplitude signals with reduced noise and distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional analog-to-digital converter uses a fixed dynamic range, then the device complexity is low, but small-amplitude signals are distorted due to saturation from large-amplitude transients
Solution Approach 1:
The patent implements dynamic range adjustment by changing the quantization step size based on signal characteristics. The system transitions from a fixed dynamic range to a variable dynamic range that adapts to the input signal amplitude, allowing accurate conversion of both small-amplitude neural signals and large-amplitude transients without saturation
Solution Approach 2:
The patent changes the quantization step size parameter dynamically based on the detected signal polarity and amplitude. By modifying this key parameter, the system optimizes the dynamic range to match the input signal characteristics, resolving the contradiction between handling small signals and avoiding saturation from large transients
2Adaptability or versatility
If the quantization step size is increased to handle large-amplitude transients, then the dynamic range increases, but the resolution for small-amplitude signals decreases
Solution Approach 1:
The system dynamically adjusts the quantization step size based on the input signal characteristics. When large-amplitude transients are detected, the step size increases to expand dynamic range and prevent saturation. When small-amplitude signals are present, the step size decreases to maximize resolution, thus resolving the trade-off between dynamic range and resolution
Solution Approach 2:
The digital predictor detects signal characteristics in advance and adjusts the quantization step size before the actual quantization occurs. This preliminary adjustment ensures that the quantizer is properly configured for the upcoming signal amplitude, preventing both saturation and loss of resolution
3Reliability
If a fixed quantization step size is used, then the device complexity is low, but saturation occurs from large-amplitude transients causing distortion
Solution Approach 1:
The system employs feedback mechanisms where the digital predictor monitors the quantizer output and adjusts the quantization step size accordingly. This closed-loop control prevents saturation by detecting large-amplitude transients and increasing the step size in real-time, thereby improving conversion reliability without requiring a fundamentally more complex quantizer structure
Solution Approach 2:
The digital predictor acts as an intermediary between the signal source and the quantizer. It analyzes the input signal characteristics and generates control signals to adjust the quantization step size, thereby protecting the quantizer from saturation while maintaining simple quantizer architecture
Data Source
AI summary
An apparatus may include a delta sigma modulator. A first portion of the delta sigma modulator may form a digital predictor while a second portion of the delta sigma modulator may form an analog approximator. An output of the analog approximator may be coupled with a quantizer. The digital predictor, the analog approximator, and the quantizer may form a digitizing loop configured to convert an analog input into a digital output. The digital predictor may be configured to generate, based on a polarity of one or more digital outputs from the quantizer, a digital prediction of an expected amplitude of the analog input. The quantizer may be configured to respond to the digital prediction by adjusting a dynamic range of the digitizing loop including by changing a quantization step size used by the quantizer to quantize the analog input. Related methods are also provided.