Abstract: Embodiments relate to a computing device that may be configured as a biomimetic audiomorphic device. The device can include a field effect transistor (FET) having a split-gate architecture with different spacing between the split-gates. Embodiments of the device can include multiple split-gates. Some embodiments include the integration of delay elements and tunable resistor-capacitance (RC) circuits for imitating the interaural time delay neurons. Some embodiments include global back-gating structural features to provide neuroplasticity aspects so as to provide adaptation related changes.

Abstract: A reconfigurable and machine learning resilient on-chip cryptography for graphene-based devices can be configured to utilize inherent disorders associated with the carrier transport in grain boundary dominated graphene field effect transistors (GFETs). For instance, a method can be configured to model a GFET as one or more physically unclonable functions (PUFs). A GFET PUF can also be reconfigured in a way that does not involve any physical intervention and/or integration of additional hardware components. A GFET PUF can be designed to operate with ultra-low power and can be configured to be robust and reliable against variation in temperature and supply voltage in some embodiments.

Abstract: Embodiments relate to a computing device that may be configured as a biomimetic audiomorphic device. The device can include a field effect transistor (FET) having a split-gate architecture with different spacing between the split-gates. Embodiments of the device can include multiple split-gates. Some embodiments include the integration of delay elements and tunable resistor-capacitance (RC) circuits for imitating the interaural time delay neurons. Some embodiments include global back-gating structural features to provide neuroplasticity aspects so as to provide adaptation related changes.

Abstract: A reconfigurable and machine learning resilient on-chip cryptography for graphene-based devices can be configured to utilize inherent disorders associated with the carrier transport in grain boundary dominated graphene field effect transistors (GFETs). For instance, a method can be configured to model a GFET as one or more physically unclonable functions (PUFs). A GFET PUF can also be reconfigured in a way that does not involve any physical intervention and/or integration of additional hardware components. A GFET PUF can be designed to operate with ultra-low power and can be configured to be robust and reliable against variation in temperature and supply voltage in some embodiments.