A PUF is a physical security primitive that allows to extract intrinsic digital identifiers from electronic devices. It is a promising candidate to improve security in lightweight devices targeted at IoT applications due to its low cost nature. The Arbiter PUF or APUF has been widely studied in the technical literature. However it often suffers from disadvantages such as poor uniqueness and reliability, particularly when implemented on FPGAs due to physical layout restrictions. To address these problems, a new design known as FF-APUF has been proposed; it offers a compact architecture, combined with good uniqueness and reliability properties, and is well suited to FPGA implementation. Many PUF designs have been shown to be vulnerable to machine learning (ML) based modelling attacks. In this paper, initial tests show that to attack the FF-APUF design requires more effort for the adversary than a conventional APUF design. A comprehensive analysis of the experimental results for the FF-APUF design is presented to show this outcome. An improved APUF design with a balanced routing, and the proposed FF-APUF design are both implemented on an Xilinx Artix-7 FPGA at 28 nm technology. The empirical min-entropy of the FF-APUF design across different devices is shown to be more than twice that of the conventional APUF design.