Abstract: A method for modeling a transistor includes providing a transistor model having at least a source node, a drain node, and a gate node, simulating operation of a device using the transistor model in a computing apparatus, and generating an offset voltage at the gate node depending on a magnitude of a current passing through the device.

Abstract: An approach for providing sub-circuit models with corner instances for VLSI designs is disclosed. Embodiments include: determining a circuit design that includes a plurality of sub-circuit models having a plurality of characteristics; and associating, by a processor, a sub-circuit model of the plurality of sub-circuit models with a corner instance value, and another sub-circuit model of the plurality of sub-circuit models with another corner instance value. Other embodiments include analyzing, by the processor, the circuit design according to the corner instance value and the other corner instance value.

Abstract: An approach for providing sub-circuit models with corner instances for VLSI designs is disclosed. Embodiments include: determining a circuit design that includes a plurality of sub-circuit models having a plurality of characteristics; and associating, by a processor, a sub-circuit model of the plurality of sub-circuit models with a corner instance value, and another sub-circuit model of the plurality of sub-circuit models with another corner instance value. Other embodiments include analyzing, by the processor, the circuit design according to the corner instance value and the other corner instance value.

Abstract: A method for modeling a transistor includes providing a transistor model having at least a source node, a drain node, and a gate node, simulating operation of a device using the transistor model in a computing apparatus, and generating an offset voltage at the gate node depending on a magnitude of a current passing through the device.

Abstract: Worst case performance of an SRAM cell may be simulated more accurately with less intensive computations. An embodiment includes determining, by a processor, a process corner G of an SRAM cell, having pull-down, pass-gate, and pull-up devices, process corner G being defined as the worst performance of the cell when only global variations of parameters of the SRAM cell are included, setting each of the pull-down, pass-gate, and pull-up devices at process corner G, performing, on the processor, a number of Monte Carlo simulations of the SRAM cell devices around process corner G with only local variations of the parameters, generating a normal probability distribution for Iread based on the local Monte Carlo simulations around process corner G, extrapolating the worst case Iread from the normal probability distribution of Iread to define a process corner SRM representing a slowest SRAM bit on a chip, and validating an SRAM cell based on the SRM corner.

Abstract: Worst case performance of an SRAM cell may be simulated more accurately with less intensive computations. An embodiment includes determining, by a processor, a process corner G of an SRAM cell, having pull-down, pass-gate, and pull-up devices, process corner G being defined as the worst performance of the cell when only global variations of parameters of the SRAM cell are included, setting each of the pull-down, pass-gate, and pull-up devices at process corner G, performing, on the processor, a number of Monte Carlo simulations of the SRAM cell devices around process corner G with only local variations of the parameters, generating a normal probability distribution for Iread based on the local Monte Carlo simulations around process corner G, extrapolating the worst case Iread from the normal probability distribution of Iread to define a process corner SRM representing a slowest SRAM bit on a chip, and validating an SRAM cell based on the SRM corner.