bag3_testbenches.measurement.char

Package Contents

Classes

CharSPTB

A class that creates and setups up a testbench for simulation, then save the result.

CharSPMeas

A class that handles circuit performance measurement.

Functions

estimate_cap(→ float)

assume yc = jwC

estimate_ind(→ Mapping[str, float])

assume res and ind in series; cap in parallel

estimate_esd(→ Tuple[float, float])

assume yc = (1/R) + jwC; returns C, R

compute_passives(→ Mapping[str, Any])

class bag3_testbenches.measurement.char.CharSPTB(sim: Optional[bag.simulation.base.SimAccess], work_dir: pathlib.Path, tb_name: str, impl_lib: str, specs: Mapping[str, Any], sim_view_list: Optional[Sequence[Tuple[str, str]]], env_list: Optional[Sequence[str]], precision: int = 6, logger: Optional[pybag.core.FileLogger] = None)[source]

Bases: bag.simulation.core.TestbenchManager

A class that creates and setups up a testbench for simulation, then save the result.

This class is used by MeasurementManager to run simulations.

Parameters:

  • sim (SimAccess) – the simulator interface object.

  • work_dir (Path) – working directory path.

  • tb_name (str) – testbench name.

  • impl_lib (str) – unused. Remain for backward compatibility.

  • specs (Mapping[str, Any]) – testbench specs.

  • sim_view_list (Optional[Sequence[Tuple[str, str]]]) – unused. Remain for backward compatibility.

  • env_list (Optional[Sequence[str]]) – unused. Remain for backward compatibility.

  • precision (int) – numeric precision in simulation netlist generation.

  • logger (Optional[FileLogger]) – the logger object.

Notes

The specification dictionary for all testbenches have the following entries:

sim_envsSequence[str]

list of simulation environments.

sim_paramsMapping[str, Any]

simulation parameters dictionary.

swp_infoUnion[Sequence[Any], Mapping[str, Any]]

Optional. the parameter sweep data structure.

sim_options: Mapping[str, Any]

Optional. Simulator-specific options.

monte_carlo_params: Mapping[str, Any]

Optional. If specified, will run Monte Carlo with the given parameters.

classmethod get_schematic_class() → Type[bag.design.module.Module][source]
get_netlist_info() → bag.simulation.data.SimNetlistInfo[source]

Returns the netlist information object.

Returns:

netlist_info – the simulation netlist information object.

Return type:

SimNetlistInfo

class bag3_testbenches.measurement.char.CharSPMeas(meas_specs: Mapping[str, Any], log_file: str, log_level: pybag.enum.LogLevel = LogLevel.DEBUG, precision: int = 6)[source]

Bases: bag.simulation.measure.MeasurementManager

A class that handles circuit performance measurement.

This class handles all the steps needed to measure a specific performance metric of the device-under-test. This may involve creating and simulating multiple different testbenches, where configuration of successive testbenches depends on previous simulation results. This class reduces the potentially complex measurement tasks into a few simple abstract methods that designers simply have to implement.

async async_measure_performance(name: str, sim_dir: pathlib.Path, sim_db: bag.simulation.cache.SimulationDB, dut: Optional[bag.simulation.cache.DesignInstance], harnesses: Optional[Sequence[bag.simulation.cache.DesignInstance]] = None) → Mapping[int, Mapping[str, Any]][source]

A coroutine that performs measurement.

Parameters:

  • name (str) – name of this measurement.

  • sim_dir (Path) – simulation directory.

  • sim_db (SimulationDB) – the simulation database object.

  • dut (Optional[DesignInstance]) – the DUT to measure.

  • harnesses (Optional[Sequence[DesignInstance]]) – the list of DUT and harnesses to measure.

Returns:

output – the measurement results.

Return type:

Mapping[str, Any]

async async_meas_case(name: str, sim_dir: pathlib.Path, sim_db: bag.simulation.cache.SimulationDB, dut: Optional[bag.simulation.cache.DesignInstance], sim_env: str, ibias: float = 0.0) → Mapping[str, Any][source]
bag3_testbenches.measurement.char.estimate_cap(freq: numpy.ndarray, yc: numpy.ndarray) → float[source]

assume yc = jwC

bag3_testbenches.measurement.char.estimate_ind(freq: numpy.ndarray, zc: numpy.ndarray) → Mapping[str, float][source]

assume res and ind in series; cap in parallel

bag3_testbenches.measurement.char.estimate_esd(freq: numpy.ndarray, yc: numpy.ndarray) → Tuple[float, float][source]

assume yc = (1/R) + jwC; returns C, R

bag3_testbenches.measurement.char.compute_passives(meas_results: Mapping[str, Any], passive_type: str) → Mapping[str, Any][source]