IC Clock Drift Holdover Control Using a Machine Learning Model
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Solution Overview
Problem
Existing methods for controlling clock drift in integrated circuits during holdover conditions, where remote time input signals are unavailable, often rely on costly and power-hungry oscillators or physics-based models that provide coarse estimations, leading to significant drift inaccuracies due to varying device parameters.
Innovation Solution
A system that trains a machine learning model using device parameters such as temperature, supply voltage, and component ages to generate a precise clock drift control signal, allowing the system clock to remain synchronized even without remote time input signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If physics-based models are used to estimate clock drift, then device complexity is reduced, but measurement precision deteriorates due to coarse estimations
Solution Approach 1:
The patent creates a virtual copy of the remote time source behavior through a machine learning model that learns the drift characteristics during synchronization mode. This software-based copy replaces the need for expensive physical oscillators while achieving accurate drift compensation during holdover mode, thus reducing device complexity without sacrificing measurement precision
Solution Approach 2:
The patent transforms the approach from using fixed physics-based models to dynamic machine learning models that adapt parameters based on learned patterns. The ML model adjusts drift compensation parameters in real-time based on historical data, enabling precise measurements without the complexity of expensive hardware oscillators
2Measurement precision
If expensive oscillators with compensation circuitry are used, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent replaces expensive mechanical/electrical oscillator compensation circuitry with a software-based machine learning model. The ML model runs on standard processing units and provides equivalent or superior drift compensation accuracy without the high power consumption of active compensation circuitry, thus improving measurement precision while reducing energy usage
Solution Approach 2:
Instead of using power-hungry physical compensation mechanisms, the patent creates a virtual model that copies the time drift behavior and compensates for it computationally. This software-based approach achieves the same precision goals with minimal additional power consumption
3Reliability
If expensive oscillators are used to maintain synchronization during holdover, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent creates a software-based virtual oscillator that copies the behavior of expensive physical oscillators during holdover conditions. The machine learning model, trained during synchronization mode, accurately predicts and compensates for drift without requiring the complex hardware of expensive oscillators, thus maintaining reliability while reducing device complexity
Solution Approach 2:
The system uses its own operational data collected during synchronization mode to train the ML model, which then serves itself during holdover mode. The model learns from the device's own drift characteristics and autonomously compensates without external intervention or complex hardware, improving reliability while keeping the system simple
Data Source
AI summary
Systems and methods for controlling clock drift of an integrated circuit device are provided. Such a system may include a local oscillator to provide a reference clock signal, a phase-locked loop to provide a system clock signal based on the reference clock signal and a drift control signal, and processing circuitry to generate the drift control signal. In a synchronization mode, the processing circuitry may generate the drift control signal based on an input time reference signal. In a holdover mode, the processing circuitry may generate the drift control signal based on a trained machine learning model.