# Optimal doping profile optimization

Jump to: Source code Text output Plots Library index

    % Boyd, Kim, Vandenberghe, and Hassibi, "A tutorial on geometric programming"
    % Joshi, Boyd, and Dutton, "Optimal doping profiles via geometric programming"
    % Written for CVX by Almir Mutapcic 02/08/06
    % (a figure is generated)
    %
    % Determines the optimal doping profile that minimizes base transit
    % time in a (homojunction) bipolar junction transistor.
    % This problem can be posed as a GP:
    %
    %   minimize   tau_B
    %       s.t.   Nmin <= v <= Nmax
    %              y_(i+1) + v_i^const1 <= y_i
    %              w_(i+1) + v_i^const2 <= w_i, etc...
    %
    % where variables are v_i, y_i, and w_i.

    % discretization size
    M = 50;
    % M = 1000; % takes a few minutes to process constraints

    % problem constants
    g1 = 0.42;
    g2 = 0.69;
    Nmax = 5*10^18;
    Nmin = 5*10^16;
    Nref = 10^17;
    Dn0 = 20.72;
    ni0 = 1.4*(10^10);
    WB = 10^(-5);
    C =  WB^2/((M^2)*(Nref^g1)*Dn0);

    % exponent powers
    pwi = g2 -1;
    pwj = 1+g1-g2;

    % optimization variables
    cvx_begin gp
      variables v(M) y(M) w(M)

      % objective function is the base transmit time
      tau_B = C*w(1);

      minimize( tau_B )
      subject to
        % problem constraints
        v >= Nmin;
        v <= Nmax;

        for i = 1:M-1
          if( mod(i,100) == 0 ), fprintf(1,'progress counter: %d\n',i), end;
          y(i+1) + v(i)^pwj <= y(i);
          w(i+1) + y(i)*v(i)^pwi <= w(i);
        end

        y(M) == v(M)^pwj;
        w(M) == y(M)*v(M)^pwi;
    cvx_end

    % plot the basic optimal doping profile
    figure, clf
    nbw = 0:1/M:1-1/M;
    semilogy(nbw,v,'LineWidth',2);
    axis([0 1 1e16 1e19]);
    xlabel('base');
    ylabel('doping');
    text(0,Nmin,'Nmin ', 'HorizontalAlignment','right');
    text(0,Nmax,'Nmax ', 'HorizontalAlignment','right');
    disp('Optimal doping profile is plotted.')

    Successive approximation method to be employed.
       For improved efficiency, sedumi is solving the dual problem.
       sedumi will be called several times to refine the solution.
       Original size: 785 variables, 343 equality constraints
       196 exponentials add 1568 variables, 980 equality constraints
    -----------------------------------------------------------------
     Cones  |             Errors              |
    Mov/Act | Centering  Exp cone   Poly cone | Status
    --------+---------------------------------+---------
    196/196 | 5.752e+00  2.045e+00  5.884e-07 | Solved
    194/194 | 3.395e+00  8.756e-01  2.249e-06 | Solved
    171/192 | 4.056e-01  1.160e-02  4.145e-06 | Solved
     46/ 60 | 3.782e-01  1.025e-02  1.776e-06 | Solved
      1/  1 | 2.711e-02  3.039e-05  0.000e+00 | Solved
      0/  0 | 0.000e+00  0.000e+00  0.000e+00 | Solved
    -----------------------------------------------------------------
    Status: Solved
    Optimal value (cvx_optval): +1.57873e-12
    Optimal doping profile is plotted.