| Title | Analog cellular neural network for application in physical unclonable functions |
| Publication Type | Conference Paper |
| Year of Publication | 2016 |
| Authors | Takalo, H., Ahmadi, A., Mirhassani, M., Ahmadi, M. |
| Conference Name | 2016 IEEE International Symposium on Circuits and Systems (ISCAS) |
| Date Published | may |
| Keywords | analog cellular neural network, analogue circuits, cellular neural nets, Cellular Neural Network (CNN), Cellular neural networks, challenge-response security system, circuit dynamical behavior, CMOS analogue integrated circuits, CMOS integrated circuits, CMOS technology, composability, cryptography, device identification-authentication, Hamming distance, Hardware, hardware security, integrated circuit design, Integrated circuit modeling, Monte Carlo methods, Monte Carlo simulation, network on chip, network on chip security, neural chips, physical unclonable function design, Physically Unclonable Function (PUF), process variation, pubcrawl, PUF instances, Resiliency, Scalability, secret key generation, Semiconductor device modeling, size 45 nm, Trajectory, unclonable core module, unpolarized Gaussian-shaped distribution, word length 100 bit |
| Abstract | In this paper an analog cellular neural network is proposed with application in physical unclonable function design. Dynamical behavior of the circuit and its high sensitivity to the process variation can be exploited in a challenge-response security system. The proposed circuit can be used as unclonable core module in the secure systems for applications such as device identification/authentication and secret key generation. The proposed circuit is designed and simulated in 45-nm bulk CMOS technology. Monte Carlo simulation for this circuit, results in unpolarized Gaussian-shaped distribution for Hamming Distance between 4005 100-bit PUF instances. |
| DOI | 10.1109/ISCAS.2016.7539134 |
| Citation Key | takalo_analog_2016 |
Analog cellular neural network for application in physical unclonable functions