Abstract: Stacked voltage domain level shifting circuits for shifting signals low-to-high or high-to-low include a storage cell and control drivers powered by a mid-range supply rail of the stacked voltage domain level shifting circuit, wherein the control drivers are coupled to drive common-source configured devices coupled to storage nodes of the storage cell.

Abstract: A level-shifting circuits utilizing storage cells for shifting signals low-to-high or high-to-low include control drivers with moving supply voltages. The moving supply voltages may power positive or negative supply terminals of the control drivers. The control drivers drive gates of common-source configured devices coupled to storage nodes of the storage cell.

Abstract: Stacked voltage domain level shifting circuits for shifting signals low-to-high or high-to-low include a storage cell powered by a mid-range supply rail of the stacked voltage domain level shifting circuit, and control drivers powered by moving supply voltages generated by the storage cell, wherein the control drivers coupled to drive gates of common-source configured devices coupled to storage nodes of the storage cell.

Abstract: Voltage level conversion circuits include PMOS pull-down devices or NMOS pull-up devices, and inverters with outputs that determine gate voltages of these devices. The inverters are powered by moving supply voltages, for example complementary supply voltages generated from a pair of cross-coupled inverters. The cross-coupled inverters may implement a data storage latch with the moving supply voltages generated from the internal data storage nodes of the latch.

Abstract: A graph neural network to predict net parasitics and device parameters by transforming circuit schematics into heterogeneous graphs and performing predictions on the graphs. The system may achieve an improved prediction rate and reduce simulation errors.

Abstract: A graph neural network to predict net parasitics and device parameters by transforming circuit schematics into heterogeneous graphs and performing predictions on the graphs. The system may achieve an improved prediction rate and reduce simulation errors.

Abstract: A graph neural network to predict net parasitics and device parameters by transforming circuit schematics into heterogeneous graphs and performing predictions on the graphs. The system may achieve an improved prediction rate and reduce simulation errors.

Abstract: A multi-stage amplifier circuit equalizes an input signal through multiple signal amplification paths. DC gain is kept substantially constant over frequency, while adjustable high-frequency gain provides equalization (e.g., peaking). Various embodiments include a common source topology, a common gate topology, differential signaling topologies, and a topology suitable for stabilizing a voltage supply against high-frequency transient loads. A system may include one or more integrated circuits that may each include one or more instances of the multi-stage amplifier.

Abstract: A self-biased gyrator-based input receiver amplifies and equalizes single-ended signals. The input receiver implements inductive impedance useful for high-frequency peaking circuits using an active gyrator-C circuit comprising only resistive, capacitive, and transistor elements, which are easily and efficiently fabricated on a conventional integrated circuit. Transistors comprising the input receiver, along with resistive elements and capacitive elements may be implemented as digitally adjustable circuit elements, providing for adjustment of at least peak frequency, low-frequency gain, and termination impedance.