Physically unclonable functions (PUFs) are designed to act as device ‘fingerprints.’ Given an input challenge, the PUF circuit should produce an unpredictable response for use in situations such as root-of-trust applications and other hardware-level cybersecurity applications. PUFs are typically subcircuits present within integrated circuits (ICs), and while conventional IC PUFs are well-understood, several implementations have proven vulnerable to malicious exploits, including those perpetrated by machine learning (ML)-based attacks. Such attacks can be difficult to prevent because they are often designed to work even when relatively few challenge-response pairs are known in advance. Hence the need for both more resilient PUF designs and analysis of ML-attack susceptibility. Previous work has developed a PUF for photonic integrated circuits (PICs). A PIC PUF not only produces unpredictable responses given manufacturing-introduced tolerances, but is also less prone to electromagnetic radiation eavesdropping attacks than a purely electronic IC PUF. In this work, we analyze the resilience of the proposed photonic PUF when subjected to ML-based attacks. Specifically, we describe a computational PUF model for producing the large datasets required for training ML attacks; we analyze the quality of the model; and we discuss the modeled PUF’s susceptibility to ML-based attacks. We find that the modeled PUF generates distributions that resemble uniform white noise, explaining the exhibited resilience to neural-network-based attacks designed to exploit latent relationships between challenges and responses. Preliminary analysis suggests that the PUF exhibits similar resilience to generative adversarial networks, and continued development will show whether more-sophisticated ML approaches better compromise the PUF and—if so—how design modifications might improve resilience.
While photonic quantum circuits may be implemented using polarization-encoded qubits, their photonic integrated circuit (PIC) realization has been limited by on-chip impairments such as mode dispersion and polarization state stability that do not hinder bulk-optic, table-top setups. In this paper, we will present an interpretation of on-chip polarization and provide the beginning of a portfolio of components that may be used for polarization-encoded qubits. Central to our work is the use of waveguides of square cross-section, which symmetrically support orthogonal TE and TM modes with perpendicular electric fields. Preliminary designs for components to manipulate these modes are presented, along with measurements relevant to polarization in PICs. The research has relevance, as well, to sensing and security.
A simple structure is proposed to generate slow and fast light simultaneously in a double-waveguide coupled disk
resonator (DCDR) launched with dual contra-propagating inputs. The interaction between the intra-cavity backscattering
and the losses out of cavity are investigated for the disk resonator while random surface defects (backscatters) are
intentionally introduced in the cavity to produce the backscattering. The theoretical investigation done in this work
shows that by adjusting the amplitude and/or phase difference between the dual inputs the interaction between the cavity
modes and backscatters can be controlled thus the transmission and dispersion of the output lights of the system can be
manipulated. This scheme is attractive for slow and fast light tuning applications especially when active tuning elements
such as a p-i-n diode or a heater is absent in the cavity.
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