tidy3d.MultiPhysicsMedium

tidy3d.MultiPhysicsMedium#

class MultiPhysicsMedium[source]#

Contains multiple multi-physical properties as defined for each solver medium.

Parameters:

Examples

For silica (\(SiO_2\)):
>>> import tidy3d as td
>>> SiO2 = td.MultiPhysicsMedium(
...   optical=td.Medium(permittivity=3.9),
...   charge=td.ChargeInsulatorMedium(permittivity=3.9), # redefining permittivity
...   name="SiO2",
... )

For a silicon MultiPhysicsMedium composed of an optical model from the material library and custom charge SemiconductorMedium:

>>> import tidy3d as td
>>> default_multiphysics_Si = td.MultiPhysicsMedium(
...     optical=td.material_library['cSi']['Green2008'],
...     charge=td.SemiconductorMedium(
...         N_c=td.ConstantEffectiveDOS(N=2.86e19),
...         N_v=td.ConstantEffectiveDOS(N=3.1e19),
...         E_g=td.ConstantEnergyBandGap(eg=1.11),
...         mobility_n=td.CaugheyThomasMobility(
...             mu_min=52.2,
...             mu=1471.0,
...             ref_N=9.68e16,
...             exp_N=0.68,
...             exp_1=-0.57,
...             exp_2=-2.33,
...             exp_3=2.4,
...             exp_4=-0.146,
...         ),
...         mobility_p=td.CaugheyThomasMobility(
...             mu_min=44.9,
...             mu=470.5,
...             ref_N=2.23e17,
...             exp_N=0.719,
...             exp_1=-0.57,
...             exp_2=-2.33,
...             exp_3=2.4,
...             exp_4=-0.146,
...         ),
...         R=[
...             td.ShockleyReedHallRecombination(
...                 tau_n=3.3e-6,
...                 tau_p=4e-6
...             ),
...             td.RadiativeRecombination(
...                 r_const=1.6e-14
...             ),
...             td.AugerRecombination(
...                 c_n=2.8e-31,
...                 c_p=9.9e-32
...             ),
...         ],
...         delta_E_g=td.SlotboomBandGapNarrowing(
...             v1=6.92 * 1e-3,
...             n2=1.3e17,
...             c2=0.5,
...             min_N=1e15,
...         ),
...         N_a=[td.ConstantDoping(concentration=1e15)],
...         N_d=[td.ConstantDoping(concentration=1e15)]
...     )
... )
__init__(**data)#

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Methods

coerce_numpy_scalars_for_model(data)

coerce numpy scalars / size-1 arrays to native Python scalars, but only for fields whose annotations allow scalars.

construct([_fields_set])

copy([deep, validate, update])

Return a copy of the model.

dict(*[, include, exclude, by_alias, ...])

dict_from_file(fname[, group_path, ...])

Loads a dictionary containing the model from a .yaml, .json, .hdf5, or .hdf5.gz file.

dict_from_hdf5(fname[, group_path, ...])

Loads a dictionary containing the model contents from a .hdf5 file.

dict_from_hdf5_gz(fname[, group_path, ...])

Loads a dictionary containing the model contents from a .hdf5.gz file.

dict_from_json(fname)

Load dictionary of the model from a .json file.

dict_from_yaml(fname)

Load dictionary of the model from a .yaml file.

find_paths(target_field_name[, ...])

Finds paths to nested model instances that have a specific field, optionally matching a value.

find_submodels(target_type)

Finds all unique nested instances of a specific Tidy3D model type within this model.

from_file(fname[, group_path, lazy, on_load])

Loads a Tidy3dBaseModel from .yaml, .json, .hdf5, or .hdf5.gz file.

from_hdf5(fname[, group_path, custom_decoders])

Loads Tidy3dBaseModel instance to .hdf5 file.

from_hdf5_gz(fname[, group_path, ...])

Loads Tidy3dBaseModel instance to .hdf5.gz file.

from_json(fname, **model_validate_kwargs)

Load a Tidy3dBaseModel from .json file.

from_orm(obj)

from_yaml(fname, **model_validate_kwargs)

Loads Tidy3dBaseModel from .yaml file.

generate_docstring([show_default_args, ...])

Generates a docstring for a Tidy3D model.

get_sub_model(group_path, model_dict)

Get the sub model for a given group path.

get_submodels_by_hash()

Return a mapping {hash(submodel): [field_path, ...]} for every nested Tidy3dBaseModel inside this model.

get_tuple_group_name(index)

Get the group name of a tuple element.

get_tuple_index(key_name)

Get the index into the tuple based on its group name.

help([methods])

Prints message describing the fields and methods of a Tidy3dBaseModel.

json(*[, include, exclude, by_alias, ...])

model_construct([_fields_set])

model_copy(*[, update, deep])

model_dump(*[, mode, include, exclude, ...])

model_dump_json(*[, indent, ensure_ascii, ...])

model_json_schema([by_alias, ref_template, ...])

model_parametrized_name(params)

model_post_init(context, /)

This function is meant to behave like a BaseModel method to initialise private attributes.

model_rebuild(*[, force, raise_errors, ...])

Try to rebuild the pydantic-core schema for the model.

model_validate(obj, *[, strict, extra, ...])

model_validate_json(json_data, *[, strict, ...])

model_validate_strings(obj, *[, strict, ...])

parse_file(path, *[, content_type, ...])

parse_obj(obj)

parse_raw(b, *[, content_type, encoding, ...])

schema([by_alias, ref_template])

schema_json(*[, by_alias, ref_template])

to_file(fname)

Exports Tidy3dBaseModel instance to .yaml, .json, or .hdf5 file

to_hdf5(fname[, custom_encoders])

Exports Tidy3dBaseModel instance to .hdf5 file.

to_hdf5_gz(fname[, custom_encoders])

Exports Tidy3dBaseModel instance to .hdf5.gz file.

to_json(fname)

Exports Tidy3dBaseModel instance to .json file

to_static()

Version of object with all autograd-traced fields removed.

to_yaml(fname)

Exports Tidy3dBaseModel instance to .yaml file.

tuple_to_dict(tuple_values)

How we generate a dictionary mapping new keys to tuple values for hdf5.

update_forward_refs(**localns)

updated_copy([path, deep, validate])

Make copy of a component instance with **kwargs indicating updated field values.

validate(value)

Attributes

heat_spec

model_computed_fields

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_extra

Get extra fields set during validation.

model_fields

model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

name

optical

heat

charge

attrs

name#
optical#
heat#
charge#
property heat_spec#
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