1. Genetic Algorithm for Parameter Optimization Skyler Weaver Objective: to implement a Genetic Algorithm into myspice to allow parameter optimization of multiple parameters and multiple goal specifications.

2. Introduction Optimization allows you to set circuit outputs and solve for parameters Circuit outputs are “goals” Many optimization routines require a good initial guess –Many local minima –Slow gradient F(x1,x2,x3,…) Genetic Algorithm doesn’t have this problem!!!

3. Choose variables Create population of randomized parameters Determine fitness of individuals and rank them Solution? “mate” fittest individuals to create new population Mutate new population Solution found The concept

4. The approach 1011 0100 10111001 0111 1010 Bit array represents “DNA” : RRRR CCCC LLLL FFMF MFMF FMMM 1001 0110 1010 } crossover 0000 0100 0001 1001 0010 1011 } mutation Get values for R, C, L and run simulation father mother

5. Does it work? Successful sexual crossover Positive mutation occurred Vdd supply 0 1 0 Vsig in div 0 1 R1 supply div ~ 1 1000 R2 div 0 ~ 1 1000 R3 in out ~ 1 1000 L1 supply out ~ 1e-6 1e-3 C1 out 0 ~ 1e-12 1e-9 ~ DC div 0.5 ~ DC Vdd -0.05 ~ AC out 0.5 1e6 ~ AC out 0.4 2e6 DC Goals: dc[3] = 0.500000 (0.500000) 100.0% dc[5] = -0.050000 (-0.050000) 100.0% AC Goals: ac[4] = 0.500000 (0.500037) @ 1000000.0 Hz ac[4] = 0.400000 (0.399643) @ 2000000.0 Hz R1 supply div 10.645582 R2 div 0 9.999998 R3 in out 164.898438 Vdd supply 0 1.000000 0.000000 Vsig in div 0.000000 1.000000 C1 out 0 625.375000pF L1 supply out 81.607788uH