True Random Number Generator Mitigating Manufacturing Mismatch Bias

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

True random number generators (TRNGs) face degradation in randomness due to manufacturing mismatch, leading to bias in logic values, resulting in statistically uneven distributions of logic ones and zeros.

Innovation Solution

A TRNG system that includes a TRNG cell, a period monitor, and a sampling circuit to generate and sample bits with mean frequency and random period jitter, effectively canceling out manufacturing mismatch biases by utilizing meta-stable latch and settling-time based TRNG cells, and configuring multiple TRNG cells in parallel with hierarchical XOR gates.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If conventional TRNG components are used, then the device is simple to manufacture, but the randomness is degraded due to manufacturing mismatch bias

Engineering Contradiction:

Improverandomness qualityVSAvoidTRNG structure

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The TRNG is divided into multiple independent TRNG cells (first TRNG cell, second TRNG cell) that operate in parallel. Each cell generates random bits independently, and their outputs are combined through XOR gates. This segmentation allows the system to overcome manufacturing mismatch in individual cells by aggregating multiple sources of randomness.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The outputs of multiple TRNG cells are merged using hierarchical XOR gates (first XOR gate, second XOR gate) to produce the final random bit sequence. The XOR operation combines the random bits from multiple cells in a way that cancels out systematic biases while preserving randomness, effectively merging multiple weak random sources into a stronger random source.

Inventive Principle:

Principle #5Merging (Combining)

2Productivity

If TRNG cells operate at higher frequencies, then productivity increases, but thermal noise decreases leading to reduced randomness

Engineering Contradiction:

Improvebit generation rateVSAvoidrandomness quality

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The TRNG cells operate as periodic oscillators with well-defined oscillation periods. The random bits are generated at specific phases of these periodic oscillations. This periodic operation allows the system to maintain stable high-frequency operation while still capturing thermal noise effects during the oscillation cycles, resolving the conflict between speed and randomness quality.

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The TRNG cells are designed with predetermined oscillation characteristics and are pre-configured to operate at target frequencies. The circuit topology and component values are selected in advance to ensure operation at desired speeds while maintaining sufficient thermal noise. This preliminary design approach allows high-frequency operation without sacrificing randomness quality.

Inventive Principle:

Principle #10Preliminary action

3Manufacturing precision

If manufacturing mismatch is reduced to improve randomness, then component precision must increase, but this increases manufacturing cost and complexity

Engineering Contradiction:

Improvecomponent matchingVSAvoidmanufacturing difficulty

Core Design Contradiction:

Manufacturing precisionVSEase of manufacture

Solution Approach 1:

Instead of trying to eliminate manufacturing mismatch through precise manufacturing, the invention accepts the mismatch as an inherent characteristic and uses statistical aggregation of multiple TRNG cells to cancel out the biases. The XOR combination of multiple cells with different mismatch characteristics transforms the harmful effect of individual mismatches into a beneficial cancellation effect, improving overall randomness without requiring higher manufacturing precision.

Inventive Principle:

Principle #22Blessing in disguise (Convert harm into benefit)

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

The system generates highly random bits with improved randomness by mitigating manufacturing mismatch effects, achieving closer to 50% probability for logic ones and zeros, and maintaining high randomness even with reduced thermal noise.

Implementation Method 1

The randomness, however, is often degraded by manufacturing mismatch in the components of the TRNG. The degradation in the randomness typically results in a bias in favor of a certain logic value

Methodology Applied

Scientific EffectThermal noise: Brownian Motion

Implementation Method 2

a period monitor configured to generate a first sequence of random bits based on a set of periods of the sequence of logically alternating bits

Methodology Applied

Scientific EffectPeriod jitter:

Implementation Method 3

a flip-flop including a data input coupled to an output of the period monitor, a clock input to receive a sampling clock, and a data output to generate a sequence of random bits

Methodology Applied

Scientific EffectSampling:

Data Source

PatentUS11334321B2True random number generator based on period jitter

Publication Date: 2022.05.17 QUALCOMM INC

AI summary

A true random number generator (TRNG) for generating a sequence of random numbers of bits is disclosed. The TRNG includes a TRNG cell configured to generate a sequence of bits logically alternating with a mean frequency and with substantially random period jitter; a period monitor configured to generate a first sequence of random bits based on a set of periods of the sequence of logically alternating bits; and a sampling circuit configured to sample the first sequence of random bits in response to a sampling clock to generate a second sequence of random bits.