Variable Architecture Random Number Generator Microcontroller Entropy
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Solution Overview
Problem
Microcontrollers in the same production series often generate less varied random numbers due to similar entropy sources, leading to reduced randomness and increased power consumption and production complexity when using analog entropy sources, while digital entropy sources are more cost-effective but may not provide sufficient variation.
Innovation Solution
A variable architecture for random number generators that adjusts its internal circuitry based on microcontroller-specific data, such as unique chip identifiers and fuse bits, to generate distinct random sequences using digital entropy sources like ring oscillators and linear feedback shift registers, ensuring improved randomness and reduced power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If analog entropy sources are used, then randomness quality is improved, but power consumption increases and production complexity increases
Solution Approach 1:
The patent replaces analog entropy sources with digital entropy sources (ring oscillators, LFSRs) to eliminate the need for complex analog circuitry while maintaining randomness quality. This substitution reduces power consumption and simplifies production testing requirements.
Solution Approach 2:
The patent uses microcontroller-specific data (chip identifiers, fuse bits) as input parameters to configure the digital entropy source architecture. By varying these parameters across different microcontrollers, the system achieves uniqueness and high randomness quality without requiring complex analog components.
2Reliability
If analog entropy sources are used, then randomness quality is improved, but production testing complexity increases
Solution Approach 1:
The patent replaces analog entropy sources with purely digital implementations (ring oscillators, linear feedback shift registers). This substitution eliminates the need for complex analog testing procedures while maintaining high randomness quality through digital signal processing.
Solution Approach 2:
The patent leverages existing microcontroller-specific data (chip identifiers, fuse bits) that are already present in the device to configure the entropy source. This self-configuration approach eliminates the need for separate production testing to verify randomness quality, as each microcontroller automatically generates unique random sequences based on its inherent characteristics.
3Reliability
If microcontroller-specific data is used to vary architecture, then randomness variation between devices is improved, but device complexity increases
Solution Approach 1:
The patent implements a dynamic architecture where the random number generator configuration changes based on microcontroller-specific data. The system can adapt its internal circuitry (selecting different ring oscillators, configuring LFSR parameters) based on input from chip identifiers or fuse bits, enabling unique random sequences without requiring complex fixed hardware for each variant.
Solution Approach 2:
The patent uses a universal digital entropy source framework that can serve multiple microcontroller variants within the same family. By using configurable digital components (ring oscillators, LFSRs) that can be programmed based on microcontroller-specific data, the system achieves multi-functionality across different devices without requiring separate dedicated hardware for each variant.
Data Source
AI summary
A variable architecture for random number generators is disclosed. In some implementations, the architecture of a random number generator may be varied based on microcontroller-specific data stored on the microcontroller. For example, a random number generator module may be embedded in a microcontroller circuit. The random number generator module may be designed to receive input from data sources in the circuit that contain microcontroller-specific data (e.g., a unique chip identifier, data carried in fuse bits). In some implementations, the architecture of the random number generator module may be adjusted or varied based on the microcontroller-specific data.