def htron(nanowire_width = 0.15, nanowire_spacing = 0.1,
meander_num_squares = 5000, heater_num_squares = 1):
# Create blank device
D = Device(name = 'htron')
# Basic calculations
extra_meander_width = 2
extra_meander_height = 1
area_per_meander_sq = (nanowire_width+nanowire_spacing)*nanowire_width
meander_area = area_per_meander_sq*meander_num_squares
meander_total_width = np.sqrt(meander_area/heater_num_squares)
meander_total_height = heater_num_squares*meander_total_width
meander_size = np.array([meander_total_width, meander_total_height])
heater_size = meander_size
meander_size = meander_size + [extra_meander_width,extra_meander_height]
# meander_size = heater_size + np.array([meander_extra_width,0])
meander_pitch = nanowire_width + nanowire_spacing
# heater_standoff_y = 1
# Create components
Meander = pg.snspd_expanded(wire_width = nanowire_width, wire_pitch = meander_pitch, size = meander_size,
terminals_same_side = False, connector_width = nanowire_width*4, layer = lys['m2_nw'])
# heater_size_actual = heater_size + np.array([0, heater_standoff_y])
Heater = pg.compass(size = heater_size, layer = lys['m3_res'])
# Add references to components
m = D.add_ref(Meander)
h = D.add_ref(Heater)
h.center = m.center
# Record meta-information
heater_area = heater_size[0]*heater_size[1]
D.info['nanowire_width'] = nanowire_width
D.info['nanowire_pitch'] = nanowire_width + nanowire_spacing
D.info['meander_num_squares'] = np.round(m.info['num_squares'],2)
D.info['meander_size'] = np.round((m.xsize, m.ysize),2).tolist()
D.info['heater_size'] = np.round(heater_size,2).tolist()
D.info['heater_area'] = np.round(heater_size[0]*heater_size[1],2)
D.info['heater_num_squares'] = np.round(heater_num_squares,2)
D.info['overlap_area'] = np.round(m.ysize*heater_size[0],1)
D.info['overlap_num_squares'] = np.round(heater_area/area_per_meander_sq,1)
D.add_port(name = 1, port = h.ports['N'])
D.add_port(name = 2, port = h.ports['S'])
D.add_port(name = 3, port = m.ports[1])
D.add_port(name = 4, port = m.ports[2])
return D
# Now we can remove two of the elements we don't want anymore D.remove(mytee2) D.remove(mypoly2) qp(D) #============================================================================== # Save / export to SVG #============================================================================== # For figure-quality publications sometimes you want to save your geometry # as a more convenient vector file format like SVG (for Inkscape, Illustrator, # etc). For that purpose you can use the write_svg() command from phidl.utilities import write_svg D = Device() D << pg.snspd_expanded(layer=1) D << pg.snspd_expanded(layer=2).rotate(45) write_svg(D, filename='MyGeometryFigure.svg') #============================================================================== # Advanced: Using the LRU Cache decorator #============================================================================== # Let's assume you have a Device-making function which takes a long time, # for instance because it requires extensive computations to calculate polygon # points. PHIDL has a LRU cache decorator you can use, similar to the # built-in Python functools.lru_cache. The cache can significantly speed up import time from phidl import device_lru_cache @device_lru_cache
# example-snspd
import phidl.geometry as pg
from phidl import quickplot as qp
D = pg.snspd(wire_width = 0.2, wire_pitch = 0.6, size = (10,8),
num_squares = None, turn_ratio = 4,
terminals_same_side = False, layer = 0)
qp(D) # quickplot the geometry
create_image(D, 'snspd')
# example-snspd_expanded
import phidl.geometry as pg
from phidl import quickplot as qp
D = pg.snspd_expanded(wire_width = 0.3, wire_pitch = 0.6, size = (10,8),
num_squares = None, connector_width = 1, connector_symmetric = False,
turn_ratio = 4, terminals_same_side = False, layer = 0)
qp(D) # quickplot the geometry
create_image(D, 'snspd_expanded')
# example-packer1
import phidl.geometry as pg
from phidl import quickplot as qp
import numpy as np
np.random.seed(5)
D_list = [pg.ellipse(radii = np.random.rand(2)*n+2) for n in range(50)]
D_list += [pg.rectangle(size = np.random.rand(2)*n+2) for n in range(50)]
D_packed_list = pg.packer(