However PUF have been studied and attacked by various methods Machine learning

However puf have been studied and attacked by various

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However, PUF have been studied and attacked by various methods. Machine learning (ML) is a modelling method for predicting the result using a collection of CRPs. It is a frequently used approach for modelling strong PUFs. Ex- amples have been shown to break the protection of Arbiter PUFs, [2], [3]. In contrast, other methods have also aimed to improve the resilience of PUFs. A permutation method has been used to change the routing of the original inputs for certain PUFs, [4]. Elsewhere, a current mirror was used to control a current switch in an arbiter PUF to increase the resilience, [5]. In this paper, we focus on resource-constrained applications into which a strong PUF can be integrated by using a simple method that increases their resilience against ML attacks. Artificial neural networks (ANN) are considered as a learning method to measure the resilience of the proposed PUF. A current mirror PUF and an arbiter PUF in TSMC 65nm technology are analyzed. By putting these two PUFs in serial a PUF with improved resilience against ML is created. This paper is organized as follows: Section 2 has the background to some existing PUFs.The proposed PUF is described in Section 3. Section 4 evaluates characteristics of the PUF and compares it with other designs. Section 5 has the conclusion and suggestions for further work. II. B ACKGROUND A current mirror is a commonly used analogue circuit structure that mirrors the input current at one node in a circuit to another node of the circuit. In reality, there always exist small differences between the input and output currents due to mismatches in transistors. Additionally, transistors operating in the sub-threshold region further amplify the differences. This makes it an important circuit for use in PUF designs. An arbiter structure was proposed at the very early stages of PUF development. It is based on the signal delay caused by the manufacturing variations in a circuit such as wires and gates. It also has an exponential relationship between the number of CRPs and the number of input bits, making it a strong PUF. A. Implementing Current Mirror PUF A current mirror (CM) PUF consists of three parts: the current mirror banks, sensing amplifier, and feedback control 52 978-1-5386-6544-2/IVSW18/ c 2018 IEEE
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Fig. 1. Current Mirror PUF [6] logic as shown in Figure 1, [6]. Voltage vb1 is used to make sure that all transistors are working in the sub-threshold region. Vb3 is used to adjust the additional current to the current bank. This function is controlled by the feedback circuitry. The output response of the current mirror PUF is determined by threshold voltage differences in the transistors of the current banks. 500 Monte Carlo (MC) simulations show a maximum of 220mV difference in the threshold voltages. The timing sequence of the current mirror PUF is shown in Figure 2. An error correction scheme was made by comparing two results to give a stable response. An enable signal is used to control the switches and enables the current addition. If there is an
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