def main(): # build the model p = Path(4, 153.02/0.022/1.4) p.stages = [inv(fast_rise=True), inv(fast_fall=True), inv(fast_rise=True), inv(fast_fall=True), inv(fast_rise=True), ] # add a side load of 1024 lambda at the last nand # p.sideloads[5] = 1024 print "path effort: %0.01f" % p.pathEffort() print "stage effort: %0.02f" % p.optimalStageEffort() p.size() p.show()
def main():
# build the model
p = Path(12, 3218)
p.stages = [nand(3, fast_fall=True),
inv(be=4, fast_rise=True),
nand(2, fast_fall=True),
inv(be=16, fast_rise=True),
nand(2, fast_fall=True),
inv(fast_rise=True)]
# add a side load of 1024 lambda at the last nand
p.sideloads[3] = 731
print "path effort: %0.01f" % p.pathEffort()
print "stage effort: %0.02f" % p.optimalStageEffort()
p.size()
p.show()
sizes = p.getSizes()
template = open('decode_6s.ckt.in').read()
out = open('decode.ckt', 'w')
out.write(template.format(**sizes))
def sae_chain():
'''
size and return a schematic with the sae chain
'''
# build the model
p = Path(4, 1576)
p.stages = [nand(2, fast_fall=True),
inv(fast_rise=True),
inv(fast_fall=True),
inv(fast_rise=True)]
# add a side load of 1024 lambda at the last nand
print "path effort: %0.01f" % p.pathEffort()
print "stage effort: %0.02f" % p.optimalStageEffort()
p.size()
p.show()
#sizes = p.getSizes()
#template = open('sae_chain.ckt.in').read()
#out = open('sae_chain.ckt', 'w')
#out.write(template.format(**sizes))
out = '''
.subckt sae_inv_chain sae wl wl_pulldown sae_b
x123 sae wl net_sae_1 {nand_pcell}
x124 net_sae_1 net_sae_2 {inv_pcell1}
x125 net_sae_2 sae_b {inv_pcell2}
x126 sae_b wl_pulldown {inv_pcell3}
.ends sae_inv_chain
'''
names = {'nand_pcell': p.stages[0].getName(),
'inv_pcell1': p.stages[1].getName(),
'inv_pcell2': p.stages[2].getName(),
'inv_pcell3': p.stages[3].getName()}
out = out.format(**names)
for i in p.stages:
out += i.instantiate()
return out