bag3_testbenches.measurement.mos.sim

This package contains measurement class for transistors.

Module Contents

Classes

MOSIdTB

This class sets up the transistor drain current measurement testbench.

MOSSPTB

This class sets up the transistor S parameter measurement testbench.

MOSNoiseTB

This class sets up the transistor small-signal noise measurement testbench.

MOSCharSS

This class measures small signal parameters of a transistor using Y parameter fitting.
class bag3_testbenches.measurement.mos.sim.MOSIdTB(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

This class sets up the transistor drain current measurement testbench.

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

pre_setup(sch_params: Optional[Mapping[str, Any]])[source]

Override to perform any operations prior to calling the setup() function.

Parameters:

sch_params – the testbench schematic parameters. None means the previous testbench will be reused. This dictionary should not be modified.

Returns:

the schematic parameters to use. Could be a modified copy of the original.

Return type:

new_params

classmethod get_vgs_range(data: bag.simulation.data.SimData, ibias_min_seg: float, ibias_max_seg: float, vgs_resolution: float, seg: int, is_nmos: bool, **kwargs: Dict[str, Any]) → Tuple[float, float][source]
classmethod _get_best_crossing(xvec, yvec, val)[source]
class bag3_testbenches.measurement.mos.sim.MOSSPTB(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

This class sets up the transistor S parameter measurement testbench.

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

pre_setup(sch_params: Optional[Mapping[str, Any]]) → Optional[Mapping[str, Any]][source]

Override to perform any operations prior to calling the setup() function.

Parameters:

sch_params – the testbench schematic parameters. None means the previous testbench will be reused. This dictionary should not be modified.

Returns:

the schematic parameters to use. Could be a modified copy of the original.

Return type:

new_params

classmethod get_ss_params(data: bag.simulation.data.SimData, sim_envs: List[str], cfit_method: str, sp_freq: float, seg: int, is_nmos: bool, **kwargs: Dict[str, Any]) → Dict[str, Any][source]
classmethod mos_y_to_ss(sim_data: bag.simulation.data.SimData, char_freq: float, seg: int, ibias: numpy.ndarray, cfit_method: str = 'average') → Dict[str, numpy.ndarray][source]

Convert transistor Y parameters to small-signal parameters.

This function computes MOSFET small signal parameters from 3-port Y parameter measurements done on gate, drain and source, with body bias fixed. This functions fits the Y parameter to a capcitor-only small signal model using least-mean-square error.

Parameters:

  • sim_data (Dict[str, np.ndarray]) – A dictionary of Y parameters values stored as complex numpy arrays.

  • char_freq (float) – the frequency Y parameters are measured at.

  • seg (int) – number of transistor fingers used for the Y parameter measurement.

  • ibias (np.ndarray) – the DC bias current of the transistor. Always positive.

  • cfit_method (str) – method used to extract capacitance from Y parameters. Currently supports ‘average’ or ‘worst’

Returns:

ss_dict – A dictionary of small signal parameter values stored as numpy arrays. These values are normalized to 1-finger transistor.

Return type:

Dict[str, np.ndarray]

class bag3_testbenches.measurement.mos.sim.MOSNoiseTB(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

This class sets up the transistor small-signal noise measurement testbench.

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

pre_setup(sch_params: Optional[Mapping[str, Any]]) → Optional[Mapping[str, Any]][source]

Override to perform any operations prior to calling the setup() function.

Parameters:

sch_params – the testbench schematic parameters. None means the previous testbench will be reused. This dictionary should not be modified.

Returns:

the schematic parameters to use. Could be a modified copy of the original.

Return type:

new_params

classmethod get_integrated_noise(data: bag.simulation.data.SimData, ss_data: Dict[str, Any], freq_start: float, freq_stop: float, seg: int, scale: float = 1.0, **kwargs: Dict[str, Any]) → Dict[str, Any][source]
class bag3_testbenches.measurement.mos.sim.MOSCharSS(*args, **kwargs)[source]

Bases: bag.simulation.measure.MeasurementManager

This class measures small signal parameters of a transistor using Y parameter fitting.

This measurement is performed as follows:

  1. First, given a user specified current density range, we perform a DC current measurement to find the range of vgs needed across corners to cover that range.

  2. Then, we run a S parameter simulation and record Y parameter values at various bias points.

  3. If user specify a noise testbench, a noise simulation will be run at the same bias points as S parameter simulation to characterize transistor noise.

Parameters:

  • data_dir (str) – Simulation data directory.

  • meas_name (str) – measurement setup name.

  • impl_lib (str) – implementation library name.

  • specs (Dict[str, Any]) – the measurement specification dictionary.

  • wrapper_lookup (Dict[str, str]) – the DUT wrapper cell name lookup table.

  • sim_view_list (Sequence[Tuple[str, str]]) – simulation view list

  • env_list (Sequence[str]) – simulation environments list.

property tbm_order: List[str][source]

Returns a list of measurement manager names in the order which should be run

commit()[source]

Commit changes to specs dictionary. Perform necessary initialization.

get_tbm_specs(tbm_name: str) → Dict[str, Any][source]

Get testbench manager specs by key (tbm_name)

Parameters:

tbm_name (str) – name of testbench manager

Return type:

Testbench manager specs

add_tbm(tbm_name: str, sim_env: str) → bag.simulation.core.TestbenchManager[source]

Add/create a testbench manager

Parameters:

  • tbm_name (str) – name of testbench manager

  • sim_env (str) – simulation environment/corner

Return type:

Newly created testbench manager

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[str, Any][source]

A coroutine that performs measurement.

Since some technology nodes don’t support multi-corner simulations, individual simulations are launched per corner. Post-processed values will be stored in an hdf5 file

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 last dictionary returned by process_output().

Return type:

Mapping[str, Any]

static get_work_dir(sim_db: bag.simulation.cache.SimulationDB, sim_dir: Union[pathlib.Path, str]) → Optional[pathlib.Path][source]

Returns the work directory to which long-term files should be saved. The simulation directory may point to a temporary directory to store short-term simulation data. If so, compute the long-term directory. If not, return simulation directory.

Parameters:

  • sim_db (SimulationDB) – the simulation database object.

  • sim_dir (Path) – simulation directory.

Returns:

output – the long-term work directory. If unable to compute, this is None.

Return type:

Optional[Path]

async static _run_sim(tbm_name: str, sim_dir: pathlib.Path, sim_db: bag.simulation.cache.SimulationDB, dut: Optional[bag.simulation.cache.DesignInstance], tbm: bag.simulation.core.TestbenchManager) → bag.simulation.data.SimData[source]

Runs a simulation.

Parameters:

  • tbm_name (str) – name of testbench manager

  • sim_dir (Path) – simulation directory.

  • sim_db (SimulationDB) – the simulation database object.

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

  • tbm (TestbenchManager) – the testbench manager object.

Returns:

output – the simulation data.

Return type:

SimData

combine_data_across_corners(data_list: List[bag.simulation.data.SimData]) → bag.simulation.data.SimData[source]

Combines simulation data from separate simulations into one SimData object. Each simulation is expected to have the same setup (testbench configuration, sweep variables, etc.) except for corner.

Parameters:

data_list (List[SimData]) – list of simulation data. The order of this list should correspond to the order of sim_envs.

Returns:

output – the combined simulation data.

Return type:

SimData