The impact of discharge inversion effect on learning SRAM power-up statistics

The authentication and verification of devices require lightweight and cost-effective solutions such as the use of fingerprints of the components contained in the device. The power-up state of Static Random Access Memory (SRAMs) has a significant effect on some security and authentication applications, such as SRAM Physical Unclonable Function (PUFs) and Random Number Generation (RNG). In this paper, a detailed data collection scheme and corresponding statistical and physical analysis are presented. The results of tests on different models of SRAM chips show that the power-up state of SRAM need to be carefully collected in order to avoid increasing the volume of enrollment data. Otherwise, the probability of failure to verify will increase. Furthermore, this method provides a stable and reliable power-up state of SRAM chips and can be used as a guidance for data acquisition and analysis of similar applications. We illustrate the Discharge Inversion Effect (DIE) for SRAM chips from different manufacturers, and demonstrate how it may impact SRAM power-up applications unless our guidelines for proper data collection and use are followed.

[1]  Srinivas Devadas,et al.  Silicon physical random functions , 2002, CCS '02.

[2]  Jorge Guajardo,et al.  FPGA Intrinsic PUFs and Their Use for IP Protection , 2007, CHES.

[3]  Ingrid Verbauwhede,et al.  Intrinsic PUFs from Flip-flops on Reconfigurable Devices , 2008 .

[4]  J. Levine,et al.  The Hill cryptographic system with unknown cipher alphabet but known plaintext , 1984 .

[5]  Jorge Guajardo,et al.  Extended abstract: The butterfly PUF protecting IP on every FPGA , 2008, 2008 IEEE International Workshop on Hardware-Oriented Security and Trust.

[6]  Marten van Dijk,et al.  A technique to build a secret key in integrated circuits for identification and authentication applications , 2004, 2004 Symposium on VLSI Circuits. Digest of Technical Papers (IEEE Cat. No.04CH37525).

[7]  Maximilian Hofer,et al.  Physical Unclonable Functions in Theory and Practice , 2012 .

[8]  Y. Shtarkov,et al.  The context-tree weighting method: basic properties , 1995, IEEE Trans. Inf. Theory.

[9]  Daniel E. Holcomb,et al.  Power-Up SRAM State as an Identifying Fingerprint and Source of True Random Numbers , 2009, IEEE Transactions on Computers.

[10]  Sergei Skorobogatov Low temperature data remanence in static RAM , 2002 .

[11]  Helena Handschuh,et al.  Efficient Implementation of True Random Number Generator Based on SRAM PUFs , 2012, Cryptography and Security.

[12]  Suela Kodra Fuzzy extractors : How to generate strong keys from biometrics and other noisy data , 2015 .

[13]  Gustavus J. Simmons,et al.  A System for Verifying User Identity and Authorization at the Point-of Sale or Access , 1984, Cryptologia.

[14]  Elaine B. Barker,et al.  A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications , 2000 .

[15]  Daniel E. Holcomb,et al.  Initial SRAM State as a Fingerprint and Source of True Random Numbers for RFID Tags , 2007 .

[16]  Said Hamdioui,et al.  Adapting voltage ramp-up time for temperature noise reduction on memory-based PUFs , 2013, 2013 IEEE International Symposium on Hardware-Oriented Security and Trust (HOST).