Entropy source with magnetoresistive element for random number generator

Abstract

The present invention discloses an entropy source and a random number RN generator. In one aspect, the low energy entropy source includes a magnetoresistive MR element and sensing circuitry. The MR element is supplied with a static current and has a variable resistance determined based on the magnetization of the MR element. A sensing circuit senses the resistance of the MR element and provides a random value based on the sensed resistance of the MR element. In another aspect, the RN generator contains an entropy source and a post-processing module. The entropy source contains at least one MR element and provides a first random value based on the at least one MR element. The post-processing module receives and processes the first random value (eg, based on a cryptographic hash function, error detection code, stream cipher algorithm, etc.) and provides a second random value with improved randomness characteristics.

Description

[0001] This application claims 61 / 536,769 filed September 20, 2011 entitled "ENTROPY SOURCE WITH STT-MTJ SEMICONDUCTOR DEVICE FOR RANDOMNUMBER GENERATOR" Priority of U.S. Provisional Application No., which is incorporated herein by reference in its entirety. technical field [0002] The present invention relates generally to electronic circuits, and more particularly to techniques for generating random values. Background technique [0003] Random number (RN) generators are widely used to generate random values ​​for a variety of applications. For example, computer-like devices that implement cryptographically secure algorithms typically require a source of random values, typically random bits. The RN generator can be used to provide random values ​​to computer-like devices for use in cryptographically secure algorithms. The RN generator is also commonly referred to as RNG or Random Bit Generator (RBG). [0004] The RN generator can be implemented with an entropy source th...

AI Technical Summary

Problems solved by technology

However, most physical devices that can be used as entropy sources are expensive, require high power, and are slow to generate data

In addition, it may be difficult or impractical to model the state transitions of these physical devices as simple Markov processes, which may make the performance of physical devices more difficult to quantify

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