Fuzzy logic based Truly Random number generator for high-speed BIST applications

Abstract Communication security is a major concern in the developed world. A key feature of security is the facility to detect the person on the other side of communication with almost 100% inevitability. This concern can be disabled by using the random number generators, that generating exclusive identities for each person in a network. The same random number generators find application in the field of testing to apply test patterns for the circuit under test. One such method is proposed here. The proposed work is the implementation of three ring oscillator based truly random number generators with application of fuzzy logic concept. The randomness from this type of generator instigates from phase noise in a ring oscillator. The proposed fuzzy based random number generators were intended to ensure a low slew rate at the inverter switching threshold. The output of the proposed system depicts a great increase in timing compared to the existing design. The ring oscillator based Truly Random Generator is used as a Test pattern generator in Built in Self Test (BIST) applications and the results are compared with other existing approaches.

[1]  Guang Gong,et al.  Truly Random Number Generator Based on a Ring Oscillator Utilizing Last Passage Time , 2014, IEEE Transactions on Circuits and Systems II: Express Briefs.

[2]  Himanshu Kaul,et al.  2.4 Gbps, 7 mW All-Digital PVT-Variation Tolerant True Random Number Generator for 45 nm CMOS High-Performance Microprocessors , 2012, IEEE Journal of Solid-State Circuits.

[3]  Ulkuhan Guler,et al.  A high speed, fully digital IC random number generator , 2012 .

[4]  Alessandro Trifiletti,et al.  A High-Speed Oscillator-Based Truly Random Number Source for Cryptographic Applications on a Smart Card IC , 2003, IEEE Trans. Computers.

[5]  Edgar Sánchez-Sinencio,et al.  Multiloop High-Power-Supply-Rejection Quadrature Ring Oscillator , 2012, IEEE Journal of Solid-State Circuits.

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

[7]  Sehraneh Ghaemi,et al.  Application of type-2 fuzzy logic system for load frequency control using feedback error learning approaches , 2014, Appl. Soft Comput..

[8]  Ganesh K. Balachandran,et al.  A 440-nA True Random Number Generator for Passive RFID Tags , 2008, IEEE Transactions on Circuits and Systems I: Regular Papers.

[9]  J. A. Connelly,et al.  Modeling and simulation of oscillator-based random number generators , 1996, 1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World. ISCAS 96.

[10]  V. Michal.  On the low-power design, stability improvement and frequency estimation of the CMOS ring oscillator , 2012, Proceedings of 22nd International Conference Radioelektronika 2012.

[11]  Jing Wang,et al.  A 1.04 µW Truly Random Number Generator for Gen2 RFID tag , 2009, 2009 IEEE Asian Solid-State Circuits Conference.

[12]  Chik How Tan,et al.  Analysis and Enhancement of Random Number Generator in FPGA Based on Oscillator Rings , 2009, Int. J. Reconfigurable Comput..

[13]  Timothy A. Hall,et al.  The Importance of Entropy to Information Security , 2014, Computer.

[14]  Mieczyslaw Jessa,et al.  Enhancing the Randomness of a Combined True Random Number Generator Based on the Ring Oscillator Sampling Method , 2011, 2011 International Conference on Reconfigurable Computing and FPGAs.

[15]  Nathalie Bochard,et al.  Towards an Oscillator Based TRNG with a Certified Entropy Rate , 2015, IEEE Transactions on Computers.

[16]  Wayne P. Burleson,et al.  Entropy and Energy Bounds for Metastability Based TRNG with Lightweight Post-Processing , 2015, IEEE Transactions on Circuits and Systems I: Regular Papers.

[17]  Jean-Charles Fabre,et al.  A Metaobject Architecture for Fault-Tolerant Distributed Systems: The FRIENDS Approach , 1998, IEEE Trans. Computers.