Version 4
SHEET 1 1848 680
WIRE 0 224 0 160
WIRE 32 160 0 160
WIRE 832 176 816 176
WIRE 912 176 832 176
WIRE 1040 176 992 176
WIRE 1040 176 1040 48
WIRE 1056 176 1040 176
WIRE 1056 272 1056 224
WIRE 1056 288 1056 272
WIRE 1088 48 1040 48
WIRE 1104 160 1104 128
WIRE 1104 272 1056 272
WIRE 1104 272 1104 240
WIRE 1232 48 1152 48
WIRE 1232 128 1104 128
WIRE 1232 128 1232 48
WIRE 1312 128 1232 128
WIRE 1344 128 1312 128
FLAG 0 304 0
FLAG 32 160 rnd
FLAG 1056 288 0
FLAG 832 176 rnd
FLAG 1312 128 out
SYMBOL bv 0 208 R0
WINDOW 123 24 132 Left 0
SYMATTR InstName B1
SYMATTR Value V=SQRT(-2*LN(1E-4+rand(time*1.07*ft)))*SIN(2*PI*rand(time*0.93*ft))
SYMBOL res 1008 192 M270
WINDOW 0 32 56 VTop 0
WINDOW 3 0 56 VBottom 0
SYMATTR InstName R1
SYMATTR Value 1
SYMBOL e2 1104 144 R0
SYMATTR InstName E1
SYMATTR Value 100MEG
SYMBOL cap 1152 32 R90
WINDOW 0 0 32 VBottom 0
WINDOW 3 32 32 VTop 0
SYMATTR InstName C1
SYMATTR Value 1
TEXT 174 162 Left 0 !.tran 0 1000u 0 .5n
TEXT -24 -160 Left 0 ;From LTSPICE-group:\n"This is the Box-Mueller algorithm for Gaussian Noise. You need two\nindependent random variables. The factors 1.07 and 0.93 is an attempt\nto do this. You will need to alter these, and the time step to get\nwhat you want. You also may need to scale to get the correct rms\nvalue in your noise bandwidth.\nIt's not perfect."\n \nFactors .999997 and .50001 correct for the math. problem at LN(0).\nOriginal formula is:\nV=SQRT(-2*LN(white(time*1.07G)+0.5))*COS(2*PI*(white(time*0.93G+0.5)))
TEXT 208 232 Left 0 !.params ft=1e9
TEXT -32 392 Left 0 ;better formula (more gauss-like distribution):\nV=SQRT(-2*LN(1E-4+rand(time*1.07*ft)))*SIN(2*PI*rand(time*0.93*ft))\nthe 1E-4 factor can be changed to adjust for the peak value!
TEXT 504 232 Left 0 !.ic v(out)=0
TEXT 864 -104 Left 0 ;Note that the output noise is integrated thermal noise, see Ex. 8.14 on pages\n245-246. The PSD of this noise has a 1/f^2 shape. It's useful to look at the\ntime domain view of this type of noise to see how it varies (note the low frequency\nshape).