## 1 Introduction  
Security is one of the key factors in current electronic devices, particularly in the Internet of Things (IoT) sector [1]. Some of the most important security requirements are trusted device identification methods and secured communication links between nodes. Physical unclonable functions (PUFs) have turned out to be promising techniques for secure key generation and reliable identification. A PUF is capable of providing a response that can be used as a unique identifier. This response is said to be physically unclonable because it is obtained from a physical device feature that is virtually impossible to replicate [2]. PUFs are gradually being used as an alternative to the traditional non-volatile memories approach to store identifier keys. They show better attack resilience, flexibility, and, depending on the PUF type, also less cost [3]. These factors are particularly crucial in the IoT field, where security requirements can be high. However, many traditional security approaches are not suitable because IoT devices often have tight constraints in terms of cost or power consumption [4]. A good PUF has three attributes that should have a high score: uniqueness, unpredictability and reliability [2]. Uniqueness measures the differences between the responses from different PUF instances, unpredictability assesses if PUF response can be guessed, and reliability evaluates if a single PUF provides always the same response [5]. Many circuits have been proposed as PUFs, like ring oscillators [6], delay-based arbiter PUFs [7], flip-flops PUFs [8], and others [9; 10; 11]. The main drawback of these strategies is that they often require additional hardware, which in turn has an impact on cost and power consumption. Nevertheless, using Static Random Access Memories (SRAMs) as PUF has the advantage that most digital electronic devices include SRAMs, and thus, they can be used for both storing data and for PUF generation. For this reason, using SRAM as PUF is an option that can offer high performance at a low cost [12]. PUF-based SRAMs rely on the effect of random process variations in the transistor parameters that form the basic SRAM cell [13]. Differences in transistors parameters are originated during the manufacturing process and are random from cell to cell, and more importantly, from chip to chip. Therefore, these differences are unique and unpredictable, and thus, they can be used to uniquely and securely identify a chip. With data obtained from various cells of the same chip, a unique digital signature can be extracted. This signature will not only be exclusive for the chip, but also it will be virtually impossible to be predicted or replicated on another chip because it is based on random differences originated in the manufacturing process. In SRAM PUFs, cell data is usually extracted from the start-up value of each cell, i.e., after a period where the supply voltage has been turned off [14]. Then, the cell is powered-up and its content is read-out. A cell is suitable to be used as PUF if its start-up value is the same every time it is started. Consequently, it is important to know the number of cells in a chip that will always start up to the same value and, thus, how many of them will be the best to be used as PUF. The objective of this paper is to develop a methodology to predict the statistical distribution of cells in a given SRAM as a function of their probability to start up to the same value. Those with a high probability will be the most appropriate to implement PUFs. The prediction is made from an electrically simulated parameter that measures cell asymmetry. However, these results do not directly provide the desired probability distribution. To achieve that, simulated data are converted into start-up probabilities by using a transfer function calibrated with experimental results. The paper is structured as follows. Section 2 introduces the working principles of SRAM PUFs. Section 3 describes the used SRAM. Section 4 presents the start-up experimental results.