Fixed various issues with imported code from CACTI, added CACTI as an option for analytical sim, added placeholder names in tech files for CACTI

This commit is contained in:
Hunter Nichols 2021-07-12 15:48:47 -07:00
parent 2c9f755a73
commit ebc91814e5
4 changed files with 40 additions and 36 deletions

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@ -428,8 +428,11 @@ class spice():
c_intrinsic = self.get_intrinsic_capacitance()
# Calculate tau with provided output load then calc delay
tf = rd*(c_intrinsic+c_load)
this_delay = horowitz(inrisetime, tf, 0.5, 0.5, True)
inrisetime = this_delay / (1.0 - 0.5);
# FIXME: horowitz disabled until other parameters have been
# fixed due to divide by zero issues
#this_delay = self.horowitz(inrisetime, tf, 0.5, 0.5, True)
this_delay = 0
inrisetime = this_delay / (1.0 - 0.5)
return delay_data(this_delay, inrisetime)
def analytical_delay(self, corner, slew, load=0.0):
@ -495,50 +498,39 @@ class spice():
self.cell_name))
return 0
def horowitz(inputramptime, # input rise time
def horowitz(self,
inputramptime, # input rise time
tf, # time constant of gate
vs1, # threshold voltage
vs2, # threshold voltage
rise): # whether input rises or fall
{
if inputramptime == 0 and vs1 == vs2:
return tf * (-math.log(vs1) if vs1 < 1 else math.log(vs1))
a = inputramptime / tf
if rise == RISE:
b = 0.5;
td = tf * sqrt(math.log(vs1)*math.log(vs1) + 2*a*b*(1.0 - vs1)) + tf*(math.log(vs1) - math.log(vs2))
if rise == True:
b = 0.5
td = tf * math.sqrt(math.log(vs1)*math.log(vs1) + 2*a*b*(1.0 - vs1)) + tf*(math.log(vs1) - math.log(vs2))
else:
b = 0.4;
td = tf * sqrt(math.log(1.0 - vs1)*math.log(1.0 - vs1) + 2*a*b*(vs1)) + tf*(math.log(1.0 - vs1) - math.log(1.0 - vs2))
b = 0.4
td = tf * math.sqrt(math.log(1.0 - vs1)*math.log(1.0 - vs1) + 2*a*b*(vs1)) + tf*(math.log(1.0 - vs1) - math.log(1.0 - vs2))
return td
def tr_r_on(width, is_nchannel, stack, _is_cell):
# FIXME: temp code until parameters have been determined
if _is_cell:
dt = tech.sram_cell #SRAM cell access transistor
else:
dt = tech.peri_global
restrans = dt.R_nch_on if is_nchannel else dt.R_pch_on
def tr_r_on(self, width, is_nchannel, stack, _is_cell):
restrans = tech.spice["r_nch_on"] if is_nchannel else tech.spice["r_pch_on"]
return stack * restrans / width
def gate_c(width, wirelength, _is_cell)
def gate_c(self, width, wirelength, _is_cell):
if _is_cell:
dt = tech.sram_cell #SRAM cell access transistor
return (tech.spice["c_g_ideal"] + tech.spice["c_overlap"] + 3*tech.spice["c_fringe"])*width +\
tech.spice["l_phy"]*tech.spice["cpolywire"]
else:
dt = tech.peri_global
return (dt.C_g_ideal + dt.C_overlap + 3*dt.C_fringe)*width + dt.l_phy*Cpolywire
def drain_c_(width,
def drain_c_(self,
width,
nchannel,
stack,
next_arg_thresh_folding_width_or_height_cell,
@ -551,11 +543,11 @@ class spice():
if _is_cell:
dt = tech.sram_cell # SRAM cell access transistor
else
else:
dt = tech.peri_global
c_junc_area = dt.C_junc;
c_junc_area = dt.C_junc
c_junc_sidewall = dt.C_junc_sidewall
c_fringe = 2*dt.C_fringe
c_overlap = 2*dt.C_overlap
@ -583,15 +575,15 @@ class spice():
num_folded_tr = int(ceil(width / w_folded_tr))
if num_folded_tr < 2:
w_folded_tr = width;
w_folded_tr = width
total_drain_w = (tech.w_poly_contact + 2 * tech.spacing_poly_to_contact) + # only for drain
(stack - 1) * tech.spacing_poly_to_poly
# only for drain
total_drain_w = (tech.w_poly_contact + 2 * tech.spacing_poly_to_contact) +\
(stack - 1) * tech.spacing_poly_to_poly
drain_h_for_sidewall = w_folded_tr
total_drain_height_for_cap_wrt_gate = w_folded_tr + 2 * w_folded_tr * (stack - 1)
if num_folded_tr > 1:
total_drain_w += (num_folded_tr - 2) * (tech.w_poly_contact + 2 * tech.spacing_poly_to_contact) +
total_drain_w += (num_folded_tr - 2) * (tech.w_poly_contact + 2 * tech.spacing_poly_to_contact) +\
(num_folded_tr - 1) * ((stack - 1) * tech.spacing_poly_to_poly)
if num_folded_tr%2 == 0:

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@ -636,6 +636,8 @@ class lib:
from .elmore import elmore as model
elif model_name_lc == "neural_network":
from .neural_network import neural_network as model
elif model_name_lc == "cacti":
from .cacti import cacti as model
else:
debug.error("{} model not recognized. See options.py for available models.".format(OPTS.model_name))

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@ -456,6 +456,11 @@ parameter["sa_inv_pmos_size"] = 0.54 # micro-meters
parameter["sa_inv_nmos_size"] = 0.27 # micro-meters
parameter["bitcell_drain_cap"] = 0.1 # In Femto-Farad, approximation of drain capacitance
# Spice Values uses to calculate analytical delay based on CACTI equations
# FIXME: temp values used currently. Need to be derived from simulations or the SPICE model
spice["r_nch_on"] = 0
spice["r_pch_on"] = 0
###################################################
# Technology Tool Preferences
###################################################

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@ -403,6 +403,11 @@ parameter["sa_inv_pmos_size"] = 18 * _lambda_
parameter["sa_inv_nmos_size"] = 9 * _lambda_
parameter["bitcell_drain_cap"] = 0.2 # In Femto-Farad, approximation of drain capacitance
# Spice Values uses to calculate analytical delay based on CACTI equations
# FIXME: temp values used currently. Need to be derived from simulations or the SPICE model
spice["r_nch_on"] = 0
spice["r_pch_on"] = 0
###################################################
# Technology Tool Preferences
###################################################