xschem/xschem_library/examples/poweramp.sch

v {xschem version=3.0.0 file_version=1.2 }
G {}
K {}
V {}
S {}
E {}
L 18 845 -530 880 -530 {}
L 18 845 -530 845 -450 {}
L 18 845 -450 880 -450 {}
L 18 880 -450 900 -400 {}
L 18 900 -400 910 -400 {}
L 18 910 -580 910 -400 {}
L 18 900 -580 910 -580 {}
L 18 880 -530 900 -580 {}
L 18 880 -530 880 -450 {}
L 18 900 -580 900 -400 {}
B 2 1200 -500 1880 -310 {flags=graph
y1 = -0.0059
y2 = 11
divy = 6
x1=0.0367669
x2=0.0373883
divx=10
node="i(v.x1.vu)
i(v.x0.vu)
i(v.x1.vd)
i(v.x0.vd)"
color="11 13 12 7"
unitx=m}
B 2 1200 -830 1880 -520 {flags=graph
y1 = -49
y2 = 59
divy = 12
x1=0.0367669
x2=0.0373883
divx=10
node="outp
outm
vpp
vnn
x1.vboost
x0.vboost"
color="4 15 6 12 7 4"
unitx=m}
B 2 1780 -1130 1917 -1070 {flags=image
alpha=0.6
filter="gm convert png:- -transparent black png:-"
image=/home/schippes/sda7/1.png
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}
B 2 1200 -1020 1880 -830 {flags=graph
y1 = 2.4e-11
y2 = 840
divy = 6
x1=0.0367669
x2=0.0373883
divx=10


unitx=m
color="4 7"
node="\\"supply power;i(vcurrvnn) vnn * i(vcurrvpp) vpp * +\\"
\\"average supply power;i(vcurrvnn) vnn * i(vcurrvpp) vpp * + avg()\\""}
B 2 1200 -310 1880 -120 {flags=graph
y1 = 0.0077
y2 = 850
divy = 6
x1=0.0367669
x2=0.0373883
divx=10


unitx=m
color="4 7"
node="\\"Load power;outm outp - i(v.x1.v8) *\\"
\\"Average Load power;outm outp - i(v.x1.v8) * avg()\\""}
T {actual value
50u} 400 -820 0 0 0.4 0.4 {}
T {actual value
50u} 400 -360 0 0 0.4 0.4 {}
T {actual value
50u} 50 -750 0 0 0.4 0.4 {}
T {actual value
50u} 80 -290 0 0 0.4 0.4 {}
T {actual value
200} 860 -1280 0 0 0.4 0.4 {}
T {Different type of annotators.} 740 -960 0 0 0.4 0.4 {}
T {In this annotator (push method) 
the annotated value is "pushed"
into it as an attribute by the
tcl annotate script. This attribute
is persistent and may be saved to file.} 780 -920 0 0 0.2 0.2 {layer=4}
T {This annotator uses a "pull"
method, calling a tcl procedure 
to return the operating point data.
This information is not persistent,
however when used in multiple instances
each with different bias points these
annotator dinamically show the correct 
data.} 780 -830 0 0 0.2 0.2 {layer=4}
T {Select one or more graphs (and no other objects)
and use arrow keys to zoom / pan waveforms} 1110 -1120 0 0 0.3 0.3 {}
N 70 -1220 70 -1200 {lab=#net1}
N 70 -1080 70 -1060 {lab=#net2}
N 300 -1140 310 -1140 {lab=VSS}
N 710 -700 860 -700 {lab=OUTM}
N 500 -1150 570 -1150 {lab=VSS}
N 570 -1150 570 -1140 {lab=VSS}
N 570 -1140 610 -1140 {lab=VSS}
N 540 -1190 570 -1190 {lab=IN}
N 610 -1200 700 -1200 {lab=REFP}
N 260 -1070 260 -1060 {lab=VNN}
N 260 -1140 260 -1130 {lab=VSS}
N 70 -1060 100 -1060 {lab=#net2}
N 300 -1060 310 -1060 {lab=VNN}
N 70 -1220 100 -1220 {lab=#net1}
N 300 -1220 310 -1220 {lab=VPP}
N 260 -1140 270 -1140 {lab=VSS}
N 240 -1220 270 -1220 {lab=VPP}
N 240 -1060 260 -1060 {lab=VNN}
N 240 -1140 260 -1140 {lab=VSS}
N 550 -950 710 -950 {lab=OUTM}
N 400 -890 550 -890 {lab=FBN}
N 550 -770 550 -750 {lab=IN}
N 350 -890 350 -700 {lab=FBN}
N 710 -950 710 -700 {lab=OUTM}
N 710 -240 860 -240 {lab=OUTP}
N 260 -220 350 -220 {lab=INX}
N 550 -490 710 -490 {lab=OUTP}
N 400 -430 550 -430 {lab=FB}
N 350 -430 350 -240 {lab=FB}
N 710 -490 710 -240 {lab=OUTP}
N 240 -400 240 -380 {lab=VPP}
N 260 -220 260 -190 {lab=INX}
N 260 -130 260 -110 {lab=VSS}
N 500 -1070 570 -1070 {lab=VSS}
N 540 -1110 570 -1110 {lab=IN}
N 610 -1120 700 -1120 {lab=REFM}
N 200 -220 240 -220 {lab=INX}
N 550 -310 550 -290 {lab=VSS}
N 650 -700 710 -700 {lab=OUTM}
N 650 -240 710 -240 {lab=OUTP}
N 240 -220 260 -220 {lab=INX}
N 260 -680 350 -680 {lab=VSSX}
N 240 -860 240 -840 {lab=VPP}
N 260 -680 260 -650 {lab=VSSX}
N 260 -590 260 -570 {lab=VSS}
N 240 -680 260 -680 {lab=VSSX}
N 180 -680 240 -680 {lab=VSSX}
N 870 -1200 890 -1200 {lab=IN_INT}
N 870 -1200 870 -1170 {lab=IN_INT}
N 400 -1000 400 -980 {lab=VPP}
N 400 -540 400 -520 {lab=VPP}
N 860 -700 860 -520 {lab=OUTM}
N 860 -460 860 -240 {lab=OUTP}
N 350 -890 400 -890 {lab=FBN}
N 350 -430 400 -430 {lab=FB}
N 570 -1060 610 -1060 {lab=VSS}
N 570 -1070 570 -1060 {lab=VSS}
N 270 -1220 300 -1220 {lab=VPP}
N 260 -1060 300 -1060 {lab=VNN}
N 270 -1140 300 -1140 {lab=VSS}
N 270 -1150 270 -1140 { lab=VSS}
N 270 -1220 270 -1210 { lab=VPP}
N 240 -730 240 -680 { lab=VSSX}
N 240 -270 240 -220 { lab=INX}
N 400 -920 400 -910 { lab=FBN}
N 400 -910 400 -890 { lab=FBN}
N 400 -460 400 -450 { lab=FB}
N 400 -450 400 -430 { lab=FB}
N 240 -780 240 -760 { lab=VSSX}
N 240 -760 240 -730 { lab=VSSX}
N 240 -320 240 -300 { lab=INX}
N 240 -300 240 -270 { lab=INX}
N 160 -1220 180 -1220 {lab=#net3}
N 160 -1060 180 -1060 {lab=#net4}
N 70 -1140 180 -1140 {lab=#net5}
C {code.sym} 1020 -210 0 0 {name=STIMULI
only_toplevel=true
tclcommand="xschem edit_vi_prop"
value=".option PARHIER=LOCAL RUNLVL=6 post MODMONTE=1 warn maxwarns=400
.option ITL4=20000 ITL5=0
* .option sampling_method = SRS  
* .option method=gear
vvss vss 0 dc 0 
.temp 30

.param frequ=5k
.param gain=45
.option savecurrents

** models are generally not free: you must download
** SPICE models for active devices and put them  into the below 
** referenced file in simulation directory.
.include \\"models_poweramp.txt\\"
.control
save all
op
write poweramp.raw
set appendwrite
tran  8e-7 0.07 uic
* .FOUR 20k v(outm,outp)
* .probe i(*) 
plot outp outm
save p(r*) p(v*)
write poweramp.raw
.endc
"}
C {vsource.sym} 70 -1170 0 0 {name=V1 value="dc 50 pwl 0 0 1m 50"}
C {vsource.sym} 70 -1110 0 0 {name=V0 value="dc 50 pwl 0 0 1m 50"}
C {lab_pin.sym} 310 -1220 0 1 {name=p5 lab=VPP}
C {lab_pin.sym} 310 -1060 0 1 {name=p6 lab=VNN}
C {lab_pin.sym} 310 -1140 0 1 {name=p3 lab=VSS}
C {lab_pin.sym} 860 -240 0 1 {name=p14 lab=OUTP}
C {res.sym} 860 -490 0 1 {name=R1 m=1 value=8}
C {lab_pin.sym} 500 -1150 0 0 {name=p26 lab=VSS}
C {lab_pin.sym} 540 -1190 0 0 {name=p31 lab=IN}
C {vcvs.sym} 610 -1170 0 0 {name=E3 value='gain*0.99'}
C {lab_pin.sym} 700 -1200 0 1 {name=p32 lab=REFP}
C {capa.sym} 260 -1100 0 0 {name=C3 m=1 value="100u"}
C {res.sym} 130 -1220 1 1 {name=R11 m=1 value=0.3}
C {res.sym} 130 -1060 1 1 {name=R9 m=1 value=0.3}
C {res.sym} 550 -920 0 1 {name=R19 m=1 value='100k'}
C {res.sym} 550 -860 0 1 {name=R0 m=1 value="'100k/gain'"}
C {lab_pin.sym} 550 -750 0 0 {name=p108 lab=IN}
C {mos_power_ampli.sym} 500 -660 0 0 {name=x1}
C {lab_pin.sym} 350 -640 0 0 {name=p2 lab=VPP}
C {lab_pin.sym} 350 -620 0 0 {name=p4 lab=VNN}
C {lab_pin.sym} 860 -700 0 1 {name=p9 lab=OUTM}
C {mos_power_ampli.sym} 500 -200 0 0 {name=x0}
C {lab_pin.sym} 350 -180 0 0 {name=p12 lab=VPP}
C {lab_pin.sym} 350 -160 0 0 {name=p13 lab=VNN}
C {res.sym} 240 -350 0 1 {name=R6 m=1 value=100k}
C {lab_pin.sym} 240 -400 0 0 {name=p7 lab=VPP}
C {res.sym} 260 -160 0 1 {name=R7 m=1 value=100k}
C {lab_pin.sym} 260 -110 0 0 {name=p15 lab=VSS}
C {lab_pin.sym} 500 -1070 0 0 {name=p20 lab=VSS}
C {lab_pin.sym} 540 -1110 0 0 {name=p21 lab=IN}
C {vcvs.sym} 610 -1090 0 0 {name=E0 value='-gain*0.99'}
C {lab_pin.sym} 700 -1120 0 1 {name=p23 lab=REFM}
C {lab_pin.sym} 240 -250 0 0 {name=p8 lab=INX}
C {lab_pin.sym} 870 -1050 0 0 {name=p126 lab=VSS}
C {lab_pin.sym} 950 -1200 0 1 {name=p127 lab=IN}
C {capa.sym} 550 -800 0 0 {name=C5 m=1 value="100n ic=0"}
C {lab_pin.sym} 550 -290 0 0 {name=p11 lab=VSS}
C {capa.sym} 550 -340 0 0 {name=C6 m=1 value="100n ic=0"}
C {lab_pin.sym} 350 -200 0 0 {name=p28 lab=VSS}
C {lab_pin.sym} 350 -660 0 0 {name=p1 lab=VSS}
C {res.sym} 550 -460 0 1 {name=R2 m=1 value='100k'}
C {res.sym} 550 -400 0 1 {name=R3 m=1 value="'100k/(gain-2)'"}
C {vsource.sym} 870 -1140 0 0 {name=V3 
xvalue="dc 0 pulse -.1 .1 1m .1u .1u 10.1u 20u" 
value="dc 0 sin 0 1 frequ 1m"
}
C {res.sym} 240 -810 0 1 {name=R4 m=1 value=100k}
C {lab_pin.sym} 240 -860 0 0 {name=p18 lab=VPP}
C {res.sym} 260 -620 0 1 {name=R5 m=1 value=100k}
C {lab_pin.sym} 260 -570 0 0 {name=p10 lab=VSS}
C {res.sym} 400 -950 0 1 {name=R8 m=1 value=100k}
C {capa.sym} 170 -220 1 0 {name=C4 m=1 value="100n ic=0"}
C {lab_pin.sym} 140 -220 0 0 {name=p0 lab=IN}
C {capa.sym} 150 -680 1 0 {name=C1 m=1 value="100n ic=0"}
C {lab_pin.sym} 120 -680 0 0 {name=p17 lab=VSS}
C {lab_pin.sym} 240 -710 0 0 {name=p22 lab=VSSX}
C {res.sym} 920 -1200 1 1 {name=R10 m=1 value=2}
C {lab_pin.sym} 400 -1000 0 0 {name=p24 lab=VPP}
C {res.sym} 400 -490 0 1 {name=R13 m=1 value=100k}
C {lab_pin.sym} 400 -540 0 0 {name=p16 lab=VPP}
C {vsource.sym} 870 -1080 0 0 {name=Vin value=0 xvalue="pwl 0 .1 1m .1 1.01m 0"
}
C {lab_pin.sym} 350 -270 0 0 {name=p19 lab=FB}
C {lab_pin.sym} 350 -730 0 0 {name=p25 lab=FBN}
C {title.sym} 160 -30 0 0 {name=l2 author="Stefan Schippers"}
C {lab_pin.sym} 870 -1200 0 0 {name=p27 lab=IN_INT}
C {ammeter.sym} 210 -1220 3 0 {name=vcurrvpp  net_name=true       current=0.54}
C {ammeter.sym} 210 -1060 3 0 {name=vcurrvnn  net_name=true       current=-0.4526}
C {ammeter.sym} 210 -1140 3 0 {name=vcurrvss  net_name=true       current=-0.08742}
C {launcher.sym} 780 -120 0 0 {name=h2
descr="Ctrl-Click
Clear all probes" 
tclcommand="
    xschem push_undo
    xschem set no_undo 1
    xschem set no_draw 1
 
    set lastinst [xschem get instances]
    for \{ set i 0 \} \{ $i < $lastinst \} \{incr i \} \{
      set type [xschem getprop instance $i cell::type]
      if \{ [regexp \{(^|/)probe$\} $type ] \} \{
        xschem setprop $i voltage fast
      \}
      if \{ [regexp \{current_probe$\} $type ] \} \{
        xschem setprop $i current fast
      \}
      if \{ [regexp \{differential_probe$\} $type ] \} \{
        xschem setprop $i voltage fast
      \}
    \}
    xschem set no_undo 0
    xschem set no_draw 0
    xschem redraw
"
}
C {ngspice_probe.sym} 350 -810 0 1 {name=p55}
C {ngspice_probe.sym} 150 -1140 0 1 {name=p34}
C {capa.sym} 270 -1180 0 0 {name=C2 m=1 value="100u"}
C {ngspice_probe.sym} 70 -1220 0 1 {name=p35}
C {ngspice_probe.sym} 70 -1060 0 1 {name=p36}
C {ngspice_probe.sym} 310 -1140 0 0 {name=p37}
C {ngspice_probe.sym} 840 -700 0 1 {name=p29}
C {ngspice_probe.sym} 810 -240 0 1 {name=p38}
C {ngspice_probe.sym} 300 -680 0 1 {name=p33}
C {ngspice_probe.sym} 300 -220 0 1 {name=p30}
C {ngspice_probe.sym} 350 -320 0 1 {name=p39}
C {ngspice_get_expr.sym} 535 -475 0 1 {name=r18 
node="[ngspice::get_current \{r2[i]\}]"
descr = current
 }
C {ngspice_get_expr.sym} 575 -915 0 0 {name=r1 
node="[ngspice::get_current \{r19[i]\}]"
descr = current
 }
C {ngspice_get_expr.sym} 820 -510 2 0 {name=r29 
node="[format %.4g [expr ([ngspice::get_voltage outm] - [ngspice::get_voltage outp]) * [ngspice::get_current \{r1[i]\}]]] W"
descr = power
}
C {launcher.sym} 780 -190 0 0 {name=h3
descr="Load file into gaw" 
comment="
  This launcher gets raw filename from current schematic using 'xschem get schname'
  and stripping off path and suffix.  It then loads raw file into gaw.
  This allow to use it in any schematic without changes.
"
tclcommand="
set rawfile [file tail [file rootname [xschem get schname]]].raw
gaw_cmd \\"tabledel $rawfile
load $netlist_dir/$rawfile
table_set $rawfile\\"
unset rawfile"
}
C {spice_probe.sym} 710 -860 0 0 {name=p40 analysis=tran voltage=-0.1364}
C {spice_probe.sym} 710 -400 0 0 {name=p41 analysis=tran voltage=-0.1364}
C {spice_probe.sym} 660 -1200 0 0 {name=p42 analysis=tran voltage=0.0000e+00}
C {spice_probe.sym} 670 -1120 0 0 {name=p43 analysis=tran voltage=0.0000e+00}
C {spice_probe.sym} 950 -1200 0 0 {name=p44 analysis=tran voltage=0.0000e+00}
C {launcher.sym} 1000 -270 0 0 {name=h1
descr=Backannotate
tclcommand="ngspice::annotate"}
C {ngspice_get_expr.sym} 130 -1010 0 0 {name=r19 
node="[ngspice::get_current \{r9[i]\}]"
descr = current
}
C {launcher.sym} 1000 -310 0 0 {name=h4
descr="View Raw file" 
tclcommand="textwindow $netlist_dir/[file tail [file rootname [ xschem get schname 0 ] ] ].raw"
}
C {spice_probe.sym} 300 -1220 0 0 {name=p45 analysis=tran voltage=49.84}
C {spice_probe.sym} 300 -1060 0 0 {name=p46 analysis=tran voltage=-49.86}
C {launcher.sym} 1145 -1165 0 0 {name=h5 
descr="Select arrow and 
Ctrl-Left-Click to load/unload waveforms" 
tclcommand="
xschem raw_read $netlist_dir/[file tail [file rootname [xschem get current_name]]].raw
"
}
C {launcher.sym} 1450 -30 0 0 {name=h6
descr="Graph Manual page"
url="https://xschem.sourceforge.io/stefan/xschem_man/graphs.html"}