async def _search_helper(
self,
w: int,
seg_min: int,
seg_max: Optional[int],
data_min: Optional[Any],
data_max: Optional[Any],
) -> Tuple[Any, int, int, int]:
# first, binary search on segments without changing width
bin_iter = BinaryIterator(seg_min,
seg_max,
search_step=self._search_step)
bval_min = bval_max = None
if data_max is not None:
bval_max = self.get_bin_search_info(data_max)[0]
if data_min is not None:
bval_min = self.get_bin_search_info(data_min)[0]
bin_iter.set_current(seg_min)
bin_iter.up(val=bval_min)
elif seg_max is not None and data_max is not None:
bin_iter.set_current(seg_max)
bin_iter.down(val=bval_max)
bounds = [[seg_min, bval_min, data_min], [seg_max, bval_max, data_max]]
while bin_iter.has_next():
cur_seg = bin_iter.get_next()
cur_data = await self.get_data(cur_seg, w)
cur_bval, up = self.get_bin_search_info(cur_data)
if up:
bounds[0][0] = cur_seg
bounds[0][1] = cur_bval
bounds[0][2] = cur_data
bin_iter.up(val=cur_bval)
else:
bounds[1][0] = cur_seg
bounds[1][1] = cur_bval
bounds[1][2] = cur_data
bin_iter.down(val=cur_bval)
if bounds[1][1] is None:
idx = 0
seg_min = seg_max = bounds[0][0]
elif bounds[0][1] is None:
idx = 1
seg_min = seg_max = bounds[1][0]
else:
idx = int(abs(bounds[1][1]) < abs(bounds[0][1]))
seg_min = bounds[0][0]
seg_max = bounds[1][0]
opt_bnd = bounds[idx]
opt_seg = opt_bnd[0]
opt_data = opt_bnd[2]
a_min = seg_min * w
a_max = seg_max * w
return opt_data, opt_seg, a_min, a_max
def design_amp(amp_specs, nch_db, pch_db):
sim_env = amp_specs['sim_env']
vdd = amp_specs['vdd']
vtail = amp_specs['vtail']
vgs_res = amp_specs['vgs_res']
gain_min = amp_specs['gain_min']
bw_min = amp_specs['bw_min']
cload = amp_specs['cload']
fun_ibiasn = nch_db.get_function('ibias', env=sim_env)
fun_gmn = nch_db.get_function('gm', env=sim_env)
fun_gdsn = nch_db.get_function('gds', env=sim_env)
fun_cdn = nch_db.get_function('cdb', env=sim_env) + nch_db.get_function(
'cds', env=sim_env)
fun_cgsn = nch_db.get_function('cgs', env=sim_env)
fun_ibiasp = pch_db.get_function('ibias', env=sim_env)
fun_gdsp = pch_db.get_function('gds', env=sim_env)
fun_cdp = pch_db.get_function('cdd', env=sim_env)
vgsn_idx = nch_db.get_fun_arg_index('vgs')
vgsn_min, vgsn_max = fun_ibiasn.get_input_range(vgsn_idx)
num_pts = int(math.ceil((vgsn_max - vgsn_min) / vgs_res))
vgs_list = np.linspace(vgsn_min, vgsn_max, num_pts + 1).tolist()
vgsp_idx = pch_db.get_fun_arg_index('vgs')
vgsp_min, vgsp_max = fun_ibiasp.get_input_range(vgsp_idx)
# sweep vgs, find best point
performance = None
for vgsn_cur in vgs_list:
vout = vgsn_cur + vtail
narg = nch_db.get_fun_arg(vgs=vgsn_cur, vds=vgsn_cur, vbs=vtail)
ibiasn_unit = fun_ibiasn(narg)
gmn_unit = fun_gmn(narg)
gdsn_unit = fun_gdsn(narg)
cdn_unit = fun_cdn(narg)
cgsn_unit = fun_cgsn(narg)
# find max gain
def gain_fun1(vgsp_test):
parg_test = pch_db.get_fun_arg(vgs=vgsp_test,
vds=vout - vdd,
vbs=0)
ibiasp_unit_test = fun_ibiasp(parg_test)
gdsp_unit_test = fun_gdsp(parg_test)
return gmn_unit / ibiasn_unit / (gdsn_unit / ibiasn_unit +
gdsp_unit_test / ibiasp_unit_test)
result = minimize_cost_golden_float(gain_fun1,
gain_min,
vgsp_min,
vgsp_max,
tol=vgs_res / 10)
opt_vgsp = result.x
if opt_vgsp is None:
print('vgsn = %.4g, max gain: %.4g' % (vgsn_cur, result.vmax))
break
# get number of input fingers needed to achieve gain_max with minimum number of load fingers
seg_in_init = fun_ibiasp(
pch_db.get_fun_arg(vgs=opt_vgsp, vds=vout - vdd,
vbs=0)) * 2 / ibiasn_unit
seg_in_init = int(round(seg_in_init / 2)) * 2
# sweep gm size
seg_in_iter = BinaryIterator(2, None, step=2)
seg_in_iter.set_current(seg_in_init)
while seg_in_iter.has_next():
seg_in = seg_in_iter.get_next()
ibiasn = seg_in * ibiasn_unit
gmn = seg_in * gmn_unit
gdsn = seg_in * gdsn_unit
# sweep load size
seg_load_iter = BinaryIterator(2, None, step=2)
while seg_load_iter.has_next():
seg_load = seg_load_iter.get_next()
vbp, sgn = find_load_bias(pch_db, vdd, vout, vgsp_min,
vgsp_max, ibiasn, seg_load,
fun_ibiasp)
if vbp is None:
if sgn > 0:
seg_load_iter.up()
else:
seg_load_iter.down()
else:
parg = pch_db.get_fun_arg(vgs=vbp - vdd,
vds=vout - vdd,
vbs=0)
gdsp = seg_load * fun_gdsp(parg)
if gmn / (gdsp + gdsn) >= gain_min:
seg_load_iter.save_info((vbp, parg))
seg_load_iter.down()
else:
seg_load_iter.up()
seg_load = seg_load_iter.get_last_save()
if seg_load is None:
# no load solution -> cannot meet gain spec.
break
vbp, parg = seg_load_iter.get_last_save_info()
gdsp = seg_load * fun_gdsp(parg)
cdp = seg_load * fun_cdp(parg)
cdn = seg_in * cdn_unit
cgsn = seg_in * cgsn_unit
ro_cur = 1 / (gdsp + gdsn)
gain_cur = gmn * ro_cur
cpar_cur = cdn + cdp + (1 + 1 / gain_cur) * cgsn
# check intrinsic bandwidth good
if 1 / (ro_cur * cpar_cur * 2 * np.pi) < bw_min:
break
cload_cur = cload + cpar_cur
bw_cur = 1 / (ro_cur * cload_cur * 2 * np.pi)
if bw_cur < bw_min:
seg_in_iter.up()
else:
seg_in_iter.save_info(
(seg_load, vbp, ibiasn, gain_cur, bw_cur))
seg_in_iter.down()
if seg_in_iter.get_last_save() is None:
continue
seg_in = seg_in_iter.get_last_save()
seg_load, vbp, ibiasn, gain_cur, bw_cur = seg_in_iter.get_last_save_info(
)
if performance is None or performance[0] > ibiasn:
performance = (ibiasn, gain_cur, bw_cur, seg_in, seg_load,
vgsn_cur, vbp)
if performance is None:
return None
ibias_opt, gain_opt, bw_opt, seg_in, seg_load, vgs_in, vload = performance
vio = vtail + vgs_in
seg_tail, vbias = find_tail_bias(fun_ibiasn, nch_db, vtail, vgsn_min,
vgsn_max, seg_in, ibias_opt)
return dict(
ibias=2 * ibias_opt,
gain=gain_opt,
bw=bw_opt,
seg_in=seg_in,
seg_load=seg_load,
seg_tail=seg_tail,
vtail=vbias,
vindc=vio,
voutdc=vio,
vload=vload,
vgs_in=vgs_in,
)
