Микросхемотехника / amelina_m_a_amelin_s_a_programma_shemotehnicheskogo_modeliro

Ʉɚɪɥɨ. ɗɬɨ ɩɨɡɜɨɥɢɬ ɪɚɫɫɦɨɬɪɟɬɶ ɤɚɠɞɵɣ ɫɥɭɱɚɣ ɨɬɤɚɡɚ ɜ ɨɬɞɟɥɶɧɨɫɬɢ, ɩɪɨ- ɚɧɚɥɢɡɢɪɨɜɚɬɶ ɩɚɪɚɦɟɬɪɵ, ɩɪɢ ɤɨɬɨɪɵɯ ɷɬɨɬ ɨɬɤɚɡ ɩɪɨɢɡɨɲɟɥ, ɢ ɜɧɟɫɬɢ ɧɟɨɛ- ɯɨɞɢɦɵɟ ɤɨɪɪɟɤɬɢɜɵ ɜ ɢɫɯɨɞɧɭɸ ɫɯɟɦɭ (ɫɦ. ɪɢɫ. 7.5 ɢ ɩɪɢɦɟɪ Carlo_02.cir ɢɡ ɤɚɬɚɥɨɝɚ Analysis\Monte Carlo).

Seed: ɍɩɪɚɜɥɹɟɬ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɹɦɢ ɝɟɧɟɪɢɪɭɟɦɵɯ ɫɥɭɱɚɣɧɵɯ ɱɢɫɟɥ, ɩɭɬɟɦ ɭɫɬɚɧɨɜɤɢ ɧɚɱɚɥɶɧɨɝɨ ɡɧɚɱɟɧɢɹ ɝɟɧɟɪɚɬɨɪɨɜ ɫɥɭɱɚɣɧɵɯ ɱɢɫɟɥ SEED.

ȿɫɥɢ SEED 1, ɬɨ ɝɟɧɟɪɢɪɭɸɬɫɹ ɩɨɜɬɨɪɹɸɳɢɟɫɹ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ ɫɥɭɱɚɣ- ɧɵɯ ɱɢɫɟɥ. ȿɫɥɢ SEED ɨɬɫɭɬɫɬɜɭɟɬ ɢɥɢ SEED<1, ɬɨ ɝɟɧɟɪɢɪɭɸɬɫɹ ɧɟɩɨɜɬɨ- ɪɹɸɳɢɟɫɹ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ.

Tolerance (Ctrl+Shift+T). Ɉɬɤɪɵɬɢɟ ɞɢɚɥɨɝɨɜɨɝɨ ɨɤɧɚ Tolerance (ɪɢɫ. 7.6).

Ɋɢɫ. 7.6. Ⱦɢɚɥɨɝɨɜɨɟ ɨɤɧɨ Tolerance

Ⱦɨɩɭɫɤɢ ɧɨɦɢɧɚɥɨɜ ɤɨɦɩɨɧɟɧɬɨɜ (Tolerances) ɨɛɵɱɧɨ ɨɩɪɟɞɟɥɹɸɬɫɹ ɩɪɢ ɫɨɡɞɚɧɢɢ ɦɨɞɟɥɶɧɨɣ ɞɢɪɟɤɬɢɜɵ. Ɉɞɧɚɤɨ ɦɨɞɟɥɢ ɫɨɜɪɟɦɟɧɧɵɯ MOSFET- ɢ BJT-ɬɪɚɧɡɢɫɬɨɪɨɜ, ɢɦɟɸɬ ɫɬɨɥɶ ɛɨɥɶɲɨɟ ɤɨɥɢɱɟɫɬɜɨ ɩɚɪɚɦɟɬɪɨɜ, ɱɬɨ ɩɪɚɤɬɢ- ɱɟɫɤɢ ɧɟɜɨɡɦɨɠɧɨ ɜɜɟɫɬɢ ɞɨɩɭɫɤɢ ɞɥɹ ɜɫɟɝɨ ɢɯ ɪɹɞɚ, ɞɚɠɟ ɟɫɥɢ ɨɧɢ ɢɡɜɟɫɬɧɵ. Ɋɚɫɫɦɚɬɪɢɜɚɟɦɨɟ ɞɢɚɥɨɝɨɜɨɟ ɨɤɧɨ, ɜɵɡɵɜɚɟɦɨɟ ɬɚɤɠɟ ɢɡ ɞɢɚɥɨɝɨɜɨɝɨ ɨɤɧɚ

Monte Carlo>Options ɢ ɢɡ ɩɭɧɤɬɚ ɦɟɧɸ Edit>Change>Tolerances, ɩɨɡɜɨɥɹɟɬ ɨɩɪɟɞɟɥɢɬɶ ɞɨɩɭɫɤɢ ɞɥɹ ɜɫɟɯ ɩɚɪɚɦɟɬɪɨɜ ɨɞɧɨɣ ɤɨɦɚɧɞɨɣ. Ɇɨɠɧɨ ɢɫɩɨɥɶɡɨ- ɜɚɬɶ ɞɨɩɭɫɤɢ ɬɢɩɚ LOT ɢ DEV ɞɥɹ ɥɸɛɨɝɨ ɤɨɦɩɨɧɟɧɬɚ, ɢɦɟɸɳɟɝɨ ɦɨɞɟɥɶ (ɨɩ- ɪɟɞɟɥɟɧɚ ɦɨɞɟɥɶɧɚɹ ɞɢɪɟɤɬɢɜɚ), ɚ, ɫɥɟɞɨɜɚɬɟɥɶɧɨ ɢ ɦɨɞɟɥɶɧɵɟ ɩɚɪɚɦɟɬɪɵ. Ɇɨɠɧɨ ɬɚɤɠɟ ɫɨɯɪɚɧɹɬɶ ɧɚɛɨɪ ɭɫɬɚɧɨɜɨɤ ɞɥɹ ɪɚɡɛɪɨɫɚ ɩɚɪɚɦɟɬɪɨɜ (ɞɨɩɭɫɤɨɜ) ɞɥɹ ɩɨɫɥɟɞɭɸɳɟɝɨ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɞɥɹ ɞɪɭɝɢɯ ɦɨɞɟɥɟɣ ɬɨɝɨ ɠɟ ɬɢɩɚ. Ⱦɢɚɥɨ- ɝɨɜɨɟ ɨɤɧɨ ɢɦɟɟɬ ɫɥɟɞɭɸɳɢɟ ɩɨɥɹ:

xTypes: ɋɩɢɫɨɤ ɬɢɩɨɜ ɤɨɦɩɨɧɟɧɬɨɜ, ɢɦɟɸɳɢɯɫɹ ɜ ɬɟɤɭɳɟɣ ɫɯɟɦɟ. Ɇɨɠɧɨ ɜɵɛɪɚɬɶ ɨɞɢɧ ɢɥɢ ɧɟɫɤɨɥɶɤɨ ɬɢɩɨɜ ɞɥɹ ɞɚɥɶɧɟɣɲɟɝɨ ɨɩɪɟɞɟɥɟɧɢɹ ɞɨɩɭɫ- ɤɨɜ, ɯɨɬɹ ɨɛɵɱɧɨ ɞɨɩɭɫɤɢ ɢɫɩɨɥɶɡɭɸɬɫɹ ɞɥɹ ɤɨɦɩɨɧɟɧɬɨɜ ɨɞɧɨɝɨ ɬɢɩɚ.

xModels: ɋɩɢɫɨɤ ɜɫɟɯ ɦɨɞɟɥɟɣ ɞɥɹ ɜɵɛɪɚɧɧɨɝɨ ɬɢɩɚ (ɬɢɩɨɜ) ɤɨɦɩɨɧɟɧɬɨɜ. Ɇɨɠɧɨ ɜɵɛɪɚɬɶ ɨɞɧɭ (ɪɢɫ. 7.6) ɢɥɢ ɧɟɫɤɨɥɶɤɨ ɦɨɞɟɥɟɣ ɞɥɹ ɩɨɫɥɟɞɭɸɳɟɝɨ ɡɚɞɚɧɢɹ ɞɨɩɭɫɤɚ ɩɚɪɚɦɟɬɪɨɜ.

xParameters: ɉɨɤɚɡɵɜɚɟɬ ɫɩɢɫɨɤ ɩɚɪɚɦɟɬɪɨɜ ɞɥɹ ɜɫɟɯ ɜɵɛɪɚɧɧɵɯ ɦɨɞɟɥɟɣ. Ɂɞɟɫɶ ɬɚɤɠɟ ɞɥɹ ɡɚɞɚɧɢɹ ɞɨɩɭɫɤɚ ɦɨɠɧɨ ɜɵɛɪɚɬɶ ɨɞɢɧ (ɪɢɫ. 7.6) ɢɥɢ ɝɪɭɩ- ɩɭ ɩɚɪɚɦɟɬɪɨɜ.

xLOT: ɍɩɪɚɜɥɟɧɢɟ ɨɩɰɢɹɦɢ ɞɥɹ ɞɨɩɭɫɤɚ ɬɢɩɚ LOT:

Add/Change. Ⱦɨɛɚɜɥɹɟɬ ɢɥɢ ɢɡɦɟɧɹɟɬ ɞɨɩɭɫɤ ɬɢɩɚ LOT ɞɥɹ ɜɵɛɪɚɧ- ɧɨɝɨ ɦɨɞɟɥɶɧɨɝɨ ɩɚɪɚɦɟɬɪɚ(ɨɜ) ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɜɟɥɢɱɢɧɨɣ ɭɤɚɡɚɧ- ɧɨɣ ɜ ɩɨɥɟ Tolerance, ɧɨ ɬɨɥɶɤɨ ɩɨɫɥɟ ɧɚɠɚɬɢɹ ɩɚɧɟɥɢ Apply.

Leave. ȼɵɛɨɪ ɷɬɨɣ ɨɩɰɢɢ ɨɫɬɚɜɥɹɟɬ ɩɚɪɚɦɟɬɪɵ ɞɨɩɭɫɤɚ LOT ɧɟɢɡ- ɦɟɧɧɵɦɢ.

Remove. ɍɫɬɚɧɨɜɤɚ ɷɬɨɣ ɨɩɰɢɢ ɩɪɢɜɨɞɢɬ ɤ ɭɞɚɥɟɧɢɸ ɞɨɩɭɫɤɨɜ ɬɢɩɚ LOT ɞɥɹ ɜɵɛɪɚɧɧɵɯ ɦɨɞɟɥɶɧɵɯ ɩɚɪɚɦɟɬɪɨɜ.

Lot#. ɉɨɥɟ ɩɨɡɜɨɥɹɟɬ ɡɚɞɚɬɶ ɧɨɦɟɪ ɝɟɧɟɪɚɬɨɪɚ ɫɥɭɱɚɣɧɨɣ ɩɨɫɥɟɞɨ- ɜɚɬɟɥɶɧɨɫɬɢ lot# ɞɥɹ ɞɨɩɭɫɤɚ ɬɢɩɚ LOT.

Distribution. ɉɨɡɜɨɥɹɟɬ ɜɵɛɪɚɬɶ ɬɢɩ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɞɥɹ ɞɨɩɭɫɤɚ ɬɢɩɚ

LOT.

Tolerance. ɉɨɡɜɨɥɹɟɬ ɡɚɞɚɬɶ ɜɟɥɢɱɢɧɭ ɞɨɩɭɫɤɚ. Ɇɨɠɧɨ ɜɜɟɫɬɢ ɚɛɫɨ- ɥɸɬɧɨɟ ɡɧɚɱɟɧɢɟ ɢɥɢ ɨɬɧɨɫɢɬɟɥɶɧɨɟ — ɜ % ɨɬ ɧɨɦɢɧɚɥɶɧɨɝɨ ɡɧɚɱɟ- ɧɢɹ.

xDEV: ɉɚɧɟɥɶ ɭɩɪɚɜɥɟɧɢɹ ɨɩɰɢɹɦɢ ɞɨɩɭɫɤɚ ɬɢɩɚ DEV. ɇɚɛɨɪ ɨɩɰɢɣ ɬɨɬ ɠɟ, ɱɬɨ ɢ ɞɥɹ ɩɚɧɟɥɢ LOT.

xTolerance Sets: Ⱦɚɧɧɚɹ ɩɚɧɟɥɶ ɩɨɡɜɨɥɹɟɬ ɩɪɢɫɜɨɢɬɶ ɢɦɹ ɬɟɤɭɳɟɣ ɭɫɬɚɧɨɜ- ɤɟ ɞɨɩɭɫɤɨɜ ɞɥɹ ɩɨɫɥɟɞɭɸɳɟɝɨ ɩɪɢɦɟɧɟɧɢɹ ɤ ɧɚɛɨɪɭ ɦɨɞɟɥɶɧɵɯ ɩɚɪɚɦɟɬ- ɪɨɜ ɞɪɭɝɨɝɨ ɤɨɦɩɨɧɟɧɬɚ, ɢɦɟɸɳɟɝɨ ɚɧɚɥɨɝɢɱɧɭɸ ɦɨɞɟɥɶ. Ⱦɥɹ ɨɩɪɟɞɟɥɟɧɢɹ ɢɦɟɧɢ ɫɥɟɞɭɟɬ ɧɚɠɚɬɶ ɧɚ ɤɧɨɩɤɭ Save ɢ ɜɜɟɫɬɢ ɢɦɹ, ɬɚɤɨɟ, ɤɚɤ ɧɚɩɪɢɦɟɪ, MOSFET_5%, Caps_10%, Res_10%. Ⱦɥɹ ɩɪɢɦɟɧɟɧɢɹ ɭɫɬɚɧɨɜɤɢ ɞɨɩɭɫɤɨɜ ɤ ɜɵɛɪɚɧɧɵɦ ɩɚɪɚɦɟɬɪɚɦ, ɫɥɟɞɭɟɬ ɜɵɛɪɚɬɶ ɢɦɹ ɧɭɠɧɨɝɨ ɧɚɛɨɪɚ ɢɡ ɫɩɢɫɤɚ ɢ ɧɚɠɚɬɶ ɧɚ ɤɧɨɩɤɭ Apply ɩɚɧɟɥɢ Tolerance Sets. Ⱦɥɹ ɭɞɚɥɟɧɢɹ ɧɚɛɨɪɚ ɭɫɬɚ- ɧɨɜɨɤ ɞɨɩɭɫɤɨɜ, ɫɥɟɞɭɟɬ ɜɵɛɪɚɬɶ ɢɦɹ ɭɞɚɥɹɟɦɨɝɨ ɧɚɛɨɪɚ ɭɫɬɚɧɨɜɨɤ ɞɨɩɭɫ- ɤɨɜ, ɚ ɡɚɬɟɦ ɧɚɠɚɬɶ ɧɚ ɤɧɨɩɤɭ Delete.

ɉɪɢɦɟɱɚɧɢɟ. Ⱦɨɩɭɫɤɢ ɧɟ ɦɨɝɭɬ ɛɵɬɶ ɭɫɬɚɧɨɜɥɟɧɵ ɞɥɹ ɩɚɪɚɦɟɬɪɨɜ, ɤɨɬɨɪɵɟ ɧɟ ɨɩɪɟ- ɞɟɥɟɧɵ ɜ ɦɨɞɟɥɶɧɵɯ ɞɢɪɟɤɬɢɜɚɯ ɢɥɢ ɢɦɟɸɬ ɡɧɚɱɟɧɢɹ, ɩɪɢɧɹɬɵɟ ɩɨ ɭɦɨɥɱɚɧɢɸ.

ɇɚɩɪɢɦɟɪ ɩɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɧɢɠɟɩɪɢɜɟɞɟɧɧɨɣ ɦɨɞɟɥɶɧɨɣ ɞɢɪɟɤɬɢɜɵ:

.MODEL MM1 NMOS (Level=1)

ɜ ɬɪɚɧɡɢɫɬɨɪɟ MM1 ɧɟɥɶɡɹ ɢɫɩɨɥɶɡɨɜɚɬɶ ɞɨɩɭɫɤɢ, ɫɤɚɠɟɦ, ɞɥɹ ɩɚɪɚɦɟɬɪɚ GAMMA, ɩɨɫɤɨɥɶɤɭ ɟɝɨ ɡɧɚɱɟɧɢɟ ɧɟ ɨɩɪɟɞɟɥɟɧɨ ɜ ɦɨɞɟɥɶɧɨɣ ɞɢɪɟɤɬɢɜɟ. Ɇɨɠɧɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɞɨɩɭɫɤɢ ɞɥɹ GAMMA, ɟɫɥɢ ɟɝɨ ɜɟɥɢɱɢɧɚ ɨɩɪɟɞɟɥɟɧɚ ɜ ɦɨɞɟɥɶɧɨɣ ɞɢɪɟɤɬɢɜɟ, ɤɚɤ ɧɚɩɪɢɦɟɪ ɫɞɟɥɚɧɨ ɧɢɠɟ:

.MODEL MM1 NMOS (Level=1 GAMMA=.65)

7.2.3. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɮɭɧɤɰɢɣ Performance ɢ ɩɨɫɬɪɨɟɧɢɟ ɝɢɫɬɨɝɪɚɦɦ

ɉɪɢ ɩɪɨɜɟɞɟɧɢɢ ɚɧɚɥɢɡɚ Ɇɨɧɬɟ-Ʉɚɪɥɨ Micro-Cap ɫɨɯɪɚɧɹɟɬ ɜɫɟ ɡɧɚɱɟ- ɧɢɹ X-ɜɵɪɚɠɟɧɢɣ ɢ Y-ɜɵɪɚɠɟɧɢɣ ɜ ɤɚɠɞɨɣ ɬɨɱɤɟ ɝɪɚɮɢɤɚ ɞɥɹ ɜɫɟɯ ɜɚɪɢɚɧɬɨɜ ɪɚɫɱɟɬɚ. ɋɥɟɞɨɜɚɬɟɥɶɧɨ, ɦɨɠɧɨ ɩɨɫɬɪɨɢɬɶ ɝɢɫɬɨɝɪɚɦɦɵ ɞɥɹ ɮɭɧɤɰɢɣ, ɤɨɬɨ- ɪɵɟ ɢɫɩɨɥɶɡɭɸɬ ɝɪɚɮɢɤɢ ɜɫɟɯ ɪɟɚɥɢɡɚɰɢɣ. ɇɚɩɪɢɦɟɪ, ɟɫɥɢ ɜ ɩɪɨɰɟɫɫɟ ɚɧɚɥɢ- ɡɚ ɫɬɪɨɢɥɢɫɶ ɝɪɚɮɢɤɢ ɧɚɩɪɹɠɟɧɢɹ V(3) ɨɬ ɜɪɟɦɟɧɢ, ɬɨ ɩɨɫɥɟ ɨɤɨɧɱɚɧɢɹ ɚɧɚ-

Rise_Time(V(3),1,1,2,5)+Fall_Time(V(3),1,1,2,5).

Ɂɚɩɢɫɚɧɧɵɟ ɜ ɩɨɫɥɟɞɧɟɦ ɜɵɪɚɠɟɧɢɢ ɞɜɟ ɮɭɧɤɰɢɢ ɩɪɟɞɫɬɚɜɥɹɸɬ ɫɨɛɨɣ ɮɭɧɤɰɢɢ ɪɚɡɞɟɥɚ Performance, ɤɨɬɨɪɵɟ ɨɛɪɚɛɚɬɵɜɚɸɬ ɩɚɪɚɦɟɬɪɵ ɝɪɚɮɢɤɨɜ ɧɟɫɤɨɥɶɤɢɯ ɜɚɪɢɚɧɬɨɜ ɚɧɚɥɢɡɚ (ɛɭɞɶ ɬɨ ɨɛɵɱɧɵɣ ɦɧɨɝɨɜɚɪɢɚɧɬɧɵɣ ɚɧɚɥɢɡ Stepping ɢɥɢ ɚɧɚɥɢɡ Monte Carlo). Ɏɭɧɤɰɢɹ ɪɚɡɞɟɥɚ Performance ɜɵɱɢɫɥɹɟɬ ɩɨ ɨɞɧɨɣ ɪɟɚɥɢɡɚɰɢɢ ɡɧɚɱɟɧɢɟ ɨɞɧɨɝɨ ɱɢɫɥɟɧɧɨɝɨ ɩɚɪɚɦɟɬɪɚ, ɤɨɬɨɪɵɣ ɹɜɥɹɟɬɫɹ ɜɚɠɧɨɣ ɯɚɪɚɤɬɟɪɢɫɬɢɤɨɣ ɫɯɟɦɵ (ɫɦ. ɪɢɫ. 7.5, ɮɭɧɤɰɢɹ Rise_Time — ɞɥɢɬɟɥɶ- ɧɨɫɬɶ ɧɚɪɚɫɬɚɧɢɹ). Ɉɬɞɟɥɶɧɵɟ ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ ɡɚɬɟɦ ɨɛɴɟɞɢɧɹɸɬɫɹ ɜ ɦɚɫɫɢɜ ɞɚɧɧɵɯ, ɤɨɬɨɪɵɣ ɩɨɞɜɟɪɝɚɟɬɫɹ ɫɬɚɬɢɫɬɢɱɟɫɤɨɦɭ ɚɧɚɥɢɡɭ, ɡɚɤɥɸɱɢɬɟɥɶɧɵɦ ɪɟɡɭɥɶɬɚɬɨɦ ɤɨɬɨɪɨɝɨ ɹɜɥɹɟɬɫɹ ɩɨɫɬɪɨɟɧɢɟ ɝɢɫɬɨɝɪɚɦɦɵ. ɗɬɢ ɝɢɫɬɨɝɪɚɦɦɵ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɩɨ ɢɧɬɟɪɜɚɥɚɦ ɜɚɠɧɟɣɲɢɯ ɯɚɪɚɤɬɟɪɢɫɬɢɤ ɫɯɟɦɵ (ɡɧɚɱɟɧɢɣ ɮɭɧɤɰɢɣ Performance) ɩɨɦɨɝɚɸɬ ɩɪɟɞɫɤɚɡɚɬɶ ɜɵɯɨɞ ɝɨɞɧɵɯ ɷɥɟɤɬɪɨɧɧɵɯ ɫɯɟɦ ɩɪɢ ɦɚɫɫɨɜɨɦ ɩɪɨɢɡɜɨɞɫɬɜɟ. Ɏɭɧɤɰɢɢ Performance ɩɨɞɪɨɛɧɨ ɛɭɞɭɬ ɪɚɫɫɦɨɬ- ɪɟɧɵ ɜ ɪɚɡɞɟɥɟ 8.4. Ɋɚɫɤɪɵɜɚɸɳɢɣɫɹ ɫɩɢɫɨɤ ɮɭɧɤɰɢɣ Performance, ɞɨɫɬɭɩ- ɧɵɣ ɢɡ ɨɤɧɚ ɩɨɫɬɪɨɟɧɢɹ ɝɢɫɬɨɝɪɚɦɦ ɩɪɟɞɫɬɚɜɥɟɧ ɧɚ ɪɢɫ. 7.7.

Ɋɢɫ. 7.7. Ɉɤɧɨ ɩɨɫɬɪɨɟɧɢɹ ɝɢɫɬɨɝɪɚɦɦ ɢ ɜɵɛɨɪ Performance-ɮɭɧɤɰɢɢ

ɉɨɫɥɟ ɩɪɨɜɟɞɟɧɢɹ ɚɧɚɥɢɡɚ Ɇɨɧɬɟ-Ʉɚɪɥɨ, ɢɡ ɟɝɨ ɦɟɧɸ ɫɬɚɧɨɜɢɬɫɹ ɞɨɫɬɭɩ- ɧɵɦ ɩɭɧɤɬ ɩɨɫɬɪɨɟɧɢɹ ɝɢɫɬɨɝɪɚɦɦ Monte Carlo>Histograms, ɤɨɬɨɪɵɣ ɢɦɟɟɬ ɫɥɟɞɭɸɳɢɟ ɤɨɦɚɧɞɵ:

Add Histogram — ɞɨɛɚɜɥɟɧɢɟ ɨɤɧɚ ɝɢɫɬɨɝɪɚɦɦ (ɞɨɫɬɭɩɧɨ ɩɨɫɥɟ ɩɪɨ- ɜɟɞɟɧɢɹ ɦɨɞɟɥɢɪɨɜɚɧɢɹ);

Delete Histograms — ɭɞɚɥɟɧɢɟ ɨɤɧɚ ɝɢɫɬɨɝɪɚɦɦ (ɞɨɫɬɭɩɧɨ ɩɨɫɥɟ ɩɨ- ɫɬɪɨɟɧɢɹ ɯɨɬɹ ɛɵ ɨɞɧɨɣ ɝɢɫɬɨɝɪɚɦɦɵ);

Statistics — ɫɬɚɬɢɫɬɢɱɟɫɤɚɹ ɨɛɪɚɛɨɬɤɚ ɪɟɡɭɥɶɬɚɬɨɜ (ɞɨɫɬɭɩɧɨ ɩɨɫɥɟ ɩɨɫɬɪɨɟɧɢɹ ɝɢɫɬɨɝɪɚɦɦɵ).

ɋɬɚɬɢɫɬɢɱɟɫɤɚɹ ɨɛɪɚɛɨɬɤɚ ɪɟɡɭɥɶɬɚɬɨɜ ɦɨɞɟɥɢɪɨɜɚɧɢɹ ɧɚɱɢɧɚɟɬɫɹ ɩɨ ɤɨɦɚɧɞɟ Monte Carlo>Histograms>Add Histogram. ȿɟ ɪɟɡɭɥɶɬɚɬɵ ɩɪɟɞɫɬɚɜɥɹ-

ɸɬɫɹ ɜ ɜɢɞɟ ɝɢɫɬɨɝɪɚɦɦɵ, ɩɪɢɦɟɪɧɵɣ ɜɢɞ ɤɨɬɨɪɨɣ ɩɨɤɚɡɚɧ ɧɚ ɪɢɫ. 7.7. Ⱦɜɨɣ- ɧɨɣ ɤɥɢɤ ɥɟɜɨɣ ɤɥɚɜɢɲɟɣ ɦɵɲɢ ɜ ɩɨɥɟ ɨɤɧɚ ɝɢɫɬɨɝɪɚɦɦ ɨɬɤɪɵɜɚɟɬ ɞɢɚɥɨɝɨ- ɜɨɟ ɨɤɧɨ ɡɚɞɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ Properties (ɡɚɤɥɚɞɤɚ Plot). ɗɬɨ ɠɟ ɨɤɧɨ ɨɬɤɪɵɜɚ- ɟɬɫɹ ɢ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɤɨɦɚɧɞɵ Add Histograms. ȼ ɧɟɦ ɜ ɫɬɪɨɤɟ What to Plot c ɩɨɦɨɳɶɸ ɧɚɠɚɬɢɹ ɤɥɚɜɢɲɢ GET (ɪɢɫ. 7.7) ɭɤɚɡɵɜɚɟɬɫɹ ɢɦɹ ɚɧɚɥɢɡɢɪɭɟɦɨɣ ɮɭɧɤɰɢɢ, ɚ ɜ ɫɬɪɨɤɟ Title — ɢɦɹ ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ ɰɟɩɢ (ɷɬɨ ɩɨɥɟ ɦɨɠɧɨ ɨɬɪɟ- ɞɚɤɬɢɪɨɜɚɬɶ, ɩɪɟɞɜɚɪɢɬɟɥɶɧɨ ɫɧɹɜ ɮɥɚɠɨɤ Auto).

ɇɚ ɝɪɚɮɢɤɟ ɝɢɫɬɨɝɪɚɦɦɵ ɩɨ ɝɨɪɢɡɨɧɬɚɥɶɧɨɣ ɨɫɢ ɨɬɤɥɚɞɵɜɚɸɬɫɹ ɡɧɚɱɟɧɢɹ ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ F, ɩɨ ɜɟɪɬɢɤɚɥɢ — ɩɪɨɰɟɧɬ ɩɨɩɚɞɚɧɢɣ ɮɭɧɤɰɢɢ ɜ ɢɧɬɟɪɜɚɥ ɨɬ ɨɛɳɟɝɨ ɤɨɥɢɱɟɫɬɜɚ ɜɚɪɢɚɧɬɨɜ ɚɧɚɥɢɡɚ ɜ ɜɢɞɟ ɫɬɨɥɛɢɤɨɜɨɣ ɞɢɚɝɪɚɦɦɵ. Ɍɚɤ ɧɚ ɪɢɫ. 7.7. ɩɨɤɚɡɚɧɚ ɝɢɫɬɨɝɪɚɦɦɚ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɦɚɤɫɢɦɭɦɚ ɩɟɪɟɯɨɞɧɨɣ ɯɚɪɚɤ- ɬɟɪɢɫɬɢɤɢ RLC-ɫɯɟɦɵ (ɫɯɟɦɧɵɣ ɮɚɣɥ Carlo_02.cir).

ȼ ɩɨɫɥɟɞɧɟɣ ɜɟɪɫɢɢ MC10 ɫɭɳɟɫɬɜɭɟɬ ɬɚɤɠɟ ɜɨɡɦɨɠɧɨɫɬɶ ɜɵɜɨɞɚ ɩɨ ɜɟɪ- ɬɢɤɚɥɢ (ɢɥɢ ɧɚ ɜɟɪɯɧɸɸ ɝɪɚɧɶ ɫɬɨɥɛɰɚ) ɚɛɫɨɥɸɬɧɨɝɨ ɤɨɥɢɱɟɫɬɜɚ ɩɨɩɚɞɚɧɢɣ ɜ ɡɚɞɚɧɧɵɣ ɢɧɬɟɪɜɚɥ. ɗɬɨ ɩɪɨɢɫɯɨɞɢɬ ɩɪɢ ɭɫɬɚɧɨɜɤɟ ɮɥɚɠɤɚ Bar Top= Quantity ɜ ɨɤɧɟ ɫɜɨɣɫɬɜ ɝɢɫɬɨɝɪɚɦɦɵ (F10 ɢɥɢ ɞɜɨɣɧɨɣ ɤɥɢɤ).

Ɂɧɚɱɟɧɢɹ ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ F ɜɨ ɜɫɟɯ ɪɟɚɥɢɡɚɰɢɹɯ ɜɵɜɟɞɟɧɵ ɜ ɨɤɧɟ ɜ ɩɪɚ- ɜɨɣ ɱɚɫɬɢ ɨɤɧɚ. ɇɢɠɟ ɝɢɫɬɨɝɪɚɦɦɵ ɪɚɫɩɨɥɚɝɚɟɬɫɹ ɩɚɧɟɥɶ, ɜ ɤɨɬɨɪɨɣ ɦɨɠɧɨ ɡɚɞɚɬɶ ɲɢɪɢɧɭ ɢɧɬɟɪɜɚɥɚ ɪɚɡɛɢɟɧɢɹ (Grid Spacing) ɨɛɥɚɫɬɢ ɨɩɪɟɞɟɥɟɧɢɹ ɚɧɚɥɢɡɢɪɭɟɦɨɣ ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ F ɢ ɡɧɚɱɟɧɢɹ ɟɟ ɝɪɚɧɢɰ (Range Low, Range High). Ɇɨɠɧɨ ɡɚɞɚɬɶ ɚɜɬɨɦɚɫɲɬɚɛɢɪɨɜɚɧɢɟ ɝɢɫɬɨɝɪɚɦɦɵ (ɭɫɬɚɧɨɜɥɟɧɨ ɩɨ ɭɦɨɥɱɚɧɢɸ), ɜɵɛɪɚɜ ɨɩɰɢɸ Auto.

ȼ ɧɢɠɧɟɣ ɩɚɧɟɥɢ ɨɤɧɚ ɝɢɫɬɨɝɪɚɦɦ ɩɨɦɟɳɚɟɬɫɹ ɫɥɟɞɭɸɳɚɹ ɫɬɚɬɢɫɬɢɱɟ- ɫɤɚɹ ɢɧɮɨɪɦɚɰɢɹ:

Low — ɦɢɧɢɦɚɥɶɧɨɟ ɡɧɚɱɟɧɢɟ ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ; Mean — ɟɟ ɫɪɟɞɧɟɟ ɡɧɚɱɟɧɢɟ;

High — ɦɚɤɫɢɦɚɥɶɧɨɟ ɡɧɚɱɟɧɢɟ;

Sigma — ɫɪɟɞɧɟɤɜɚɞɪɚɬɢɱɟɫɤɨɟ ɨɬɤɥɨɧɟɧɢɟ ɜɟɥɢɱɢɧɵ F ɨɬ ɫɪɟɞɧɟɝɨ ɡɧɚɱɟɧɢɹ.

Ɋɟɡɭɥɶɬɚɬɵ ɫɬɚɬɢɫɬɢɱɟɫɤɨɣ ɨɛɪɚɛɨɬɤɢ ɡɚɧɨɫɹɬɫɹ ɬɚɤɠɟ ɜ ɬɟɤɫɬɨɜɵɣ ɮɚɣɥ ɤɨɦɚɧɞɨɣ Monte Carlo>Statistics. Ɍɟɤɫɬɨɜɚɹ ɢɧɮɨɪɦɚɰɢɹ ɪɚɡɦɟɳɚɟɬɫɹ ɜ ɮɚɣ- ɥɚɯ, ɢɦɟɸɳɢɯ ɬɚɤɨɟ ɠɟ ɢɦɹ, ɤɚɤ ɢ ɫɯɟɦɚ, ɚ ɪɚɫɲɢɪɟɧɢɹ — .amc, .dmc, .tmc ɜ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɜɢɞɚ ɚɧɚɥɢɡɚ. ɉɪɢɦɟɪɵ ɫɬɚɬɢɫɬɢɱɟɫɤɨɝɨ ɚɧɚɥɢɡɚ ɫɦ. ɜ ɫɯɟɦ- ɧɵɯ ɮɚɣɥɚɯ ɩɨɞɤɚɬɚɥɨɝɚ Analysis\Monte Carlo.

7.3. ɉɚɪɚɦɟɬɪɢɱɟɫɤɚɹ ɨɩɬɢɦɢɡɚɰɢɹ

ɉɚɪɚɦɟɬɪɢɱɟɫɤɚɹ ɨɩɬɢɦɢɡɚɰɢɹ ɜɵɩɨɥɧɹɟɬɫɹ ɜ ɩɪɨɝɪɚɦɦɟ Micro-Cap ɩɪɢ ɩɪɨɜɟɞɟɧɢɢ ɥɸɛɨɝɨ ɜɢɞɚ ɚɧɚɥɢɡɚ. Ɇɟɬɨɞ ɉɚɭɷɥɥɚ ɧɚɢɛɨɥɟɟ ɩɨɞɯɨɞɢɬ ɞɥɹ ɪɟɲɟɧɢɹ ɡɚɞɚɱ ɨɩɬɢɦɢɡɚɰɢɢ ɷɥɟɤɬɪɨɧɧɵɯ ɫɯɟɦ.

ȼɷɬɨɦ ɪɟɠɢɦɟ ɨɫɨɛɟɧɧɨɫɬɢ ɩɨɫɥɟɞɧɢɯ ɜɟɪɫɢɣ Micro-Cap ɫɥɟɞɭɸɳɢɟ:

x Ɉɩɬɢɦɢɡɚɰɢɹ ɦɨɠɟɬ ɢɫɩɨɥɶɡɨɜɚɬɶɫɹ ɜ ɪɟɠɢɦɚɯ ɚɧɚɥɢɡɚ Dynamic DC ɢ Dynamic AC.

xȼ ɨɤɧɟ ɨɩɬɢɦɢɡɚɬɨɪɚ ɢɦɟɟɬɫɹ ɮɥɚɠɨɤ, ɩɨɡɜɨɥɹɸɳɢɣ ɞɢɧɚɦɢɱɟɫɤɢ ɨɬɨɛɪɚ- ɠɚɬɶ ɨɩɬɢɦɢɡɢɪɭɟɦɵɣ ɝɪɚɮɢɤ ɜ ɩɪɨɰɟɫɫɟ ɩɪɨɜɟɞɟɧɢɹ ɨɩɬɢɦɢɡɚɰɢɢ.

xɇɚɱɚɥɶɧɚɹ ɨɛɥɚɫɬɶ ɨɩɬɢɦɢɡɚɰɢɢ (ɨɛɥɚɫɬɶ ɩɨɢɫɤɚ ɷɤɫɬɪɟɦɭɦɚ) ɦɨɠɟɬ ɭɫɬɚ- ɧɚɜɥɢɜɚɬɶɫɹ ɚɜɬɨɦɚɬɢɱɟɫɤɢ, ɢɫɯɨɞɹ ɢɡ ɫɭɳɟɫɬɜɭɸɳɢɯ ɜɟɥɢɱɢɧ ɩɚɪɚɦɟɬɪɨɜ.

xȼ MC9 ɩɪɢ ɩɪɨɜɟɞɟɧɢɢ ɨɩɬɢɦɢɡɚɰɢɢ ɢɫɩɨɥɶɡɭɟɬɫɹ ɟɞɢɧɫɬɜɟɧɧɵɣ ɦɟɬɨɞ — ɦɟɬɨɞ ɉɚɭɷɥɥɚ (ɜ ɨɛɵɱɧɨɦ ɢ ɩɨɲɚɝɨɜɨɦ ɜɚɪɢɚɧɬɚɯ), ɜ ɬɨ ɜɪɟɦɹ ɤɚɤ ɜ MC10 ɩɨɹɜɢɥɢɫɶ 3 ɧɨɜɵɯ ɦɟɬɨɞɚ ɩɨɢɫɤɚ ɨɩɬɢɦɭɦɚ — ɦɟɬɨɞ Ʌɟɜɟɧɛɟɪɝɚ- Ɇɚɪɤɜɚɪɞɬɚ, ɏɭɤɚ-Ⱦɠɢɜɫɚ, ɞɢɮɮɟɪɟɧɰɢɚɥɶɧɨɣ ɷɜɨɥɸɰɢɢ.

xȼ MC10 ɜ ɨɤɧɨ ɡɚɞɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɨɩɬɢɦɢɡɚɰɢɢ ɜɜɟɞɟɧɚ ɧɨɜɚɹ ɤɨɦɚɧɞɚ ɢɦɩɨɪɬɚ ɝɪɚɮɢɤɚ ɢɡ ɮɚɣɥɚ.

7.3.1. ɉɪɢɧɰɢɩ ɪɚɛɨɬɵ ɨɩɬɢɦɢɡɚɬɨɪɚ Micro-Cap

Ɉɩɬɢɦɢɡɚɬɨɪ ɭɩɨɪɹɞɨɱɟɧɧɵɦ ɨɛɪɚɡɨɦ (ɫɨɝɥɚɫɧɨ ɚɥɝɨɪɢɬɦɭ ɩɨɢɫɤɚ ɷɤɫɬɪɟ- ɦɭɦɚ) ɦɟɧɹɟɬ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɫɯɟɦɵ ɜ ɩɪɟɞɟɥɚɯ ɨɛɥɚɫɬɟɣ, ɡɚɞɚɧɧɵɯ ɩɨɥɶɡɨɜɚɬɟɥɟɦ, ɞɥɹ ɬɨɝɨ, ɱɬɨɛɵ ɞɨɛɢɬɶɫɹ ɦɢɧɢɦɭɦɚ, ɦɚɤɫɢɦɭɦɚ, ɪɚɜɟɧɫɬɜɚ ɨɩɪɟɞɟɥɟɧɧɨɦɭ ɡɧɚɱɟɧɢɸ ɡɚɞɚɧɧɨɣ ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ ɫɯɟɦɵ. Ɉɧ ɜɵɡɵɜɚɟɬɫɹ ɢɡ ɥɸɛɨɝɨ ɪɟɠɢɦɚ ɚɧɚɥɢɡɚ (ɡɚ ɢɫɤɥɸɱɟɧɢɟɦ Sensitivity ɢ Transfer Function), ɩɨɡɜɨ- ɥɹɹ ɨɩɬɢɦɢɡɢɪɨɜɚɬɶ ɢɫɤɚɠɟɧɢɹ, ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ ɜɨ ɜɪɟɦɟɧɧɨɣ ɨɛɥɚɫɬɢ, ɦɚɥɨ- ɫɢɝɧɚɥɶɧɵɟ ɱɚɫɬɨɬɧɵɟ ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ ɢ ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ ɧɚ ɩɨɫɬɨɹɧɧɨɦ ɬɨɤɟ. ȼɫɟ ɩɚɪɚɦɟɬɪɵ ɜɵɯɨɞɧɵɯ ɝɪɚɮɢɤɨɜ, ɤɨɬɨɪɵɟ ɦɨɝɭɬ ɛɵɬɶ ɧɚɣɞɟɧɵ ɫ ɩɨɦɨɳɶɸ ɮɭɧɤɰɢɣ Performance (ɫɦ. 8.4), ɬɚɤɠɟ ɦɨɝɭɬ ɩɨɞɜɟɪɝɚɬɶɫɹ ɨɩɬɢɦɢɡɚɰɢɢ.

Ɋɚɫɫɦɨɬɪɢɦ ɪɚɛɨɬɭ ɨɩɬɢɦɢɡɚɬɨɪɚ ɧɚ ɩɪɢɦɟɪɟ ɪɟɲɟɧɢɹ ɬɢɩɢɱɧɨɣ ɡɚɞɚɱɢ ɧɚ ɩɨɢɫɤ ɷɤɫɬɪɟɦɭɦɚ: ɧɚɯɨɠɞɟɧɢɹ ɫɨɩɪɨɬɢɜɥɟɧɢɹ ɧɚɝɪɭɡɤɢ, ɩɪɢ ɤɨɬɨɪɨɦ, ɦɨɳɧɨɫɬɶ, ɩɟɪɟɞɚɜɚɟɦɚɹ ɜ ɧɟɟ ɦɚɤɫɢɦɚɥɶɧɚ ɩɪɢ ɭɫɥɨɜɢɢ ɡɚɞɚɧɧɨɝɨ ɫɨɩɪɨ- ɬɢɜɥɟɧɢɹ ɝɟɧɟɪɚɬɨɪɚ (ɪɢɫ. 7.8 ɢ ɫɯɟɦɧɵɣ ɮɚɣɥ opt1.cir ɢɡ ɤɚɬɚɥɨɝɚ

Analysis\Optimize).

ɋɯɟɦɚ ɩɟɪɟɞɚɟɬ ɦɨɳɧɨɫɬɶ ɧɚ ɚɤɬɢɜɧɭɸ ɧɚɝɪɭɡɤɭ RL (ɜ ɢɫɯɨɞɧɨɦ ɫɨɫɬɨɹ- ɧɢɢ RL=6 Ɉɦ) ɨɬ ɛɚɬɚɪɟɢ VG=1ȼ ɫ ɜɧɭɬɪɟɧɧɢɦ ɫɨɩɪɨɬɢɜɥɟɧɢɟɦ RG=75 Ɉɦ. ɂɫɩɨɥɶɡɭɟɦ ɜɫɬɪɨɟɧɧɵɣ ɨɩɬɢɦɢɡɚɬɨɪ ɞɥɹ ɧɚɯɨɠɞɟɧɢɹ ɫɨɩɪɨɬɢɜɥɟɧɢɹ ɪɟɡɢ- ɫɬɨɪɚ RL, ɩɪɢ ɤɨɬɨɪɨɦ ɦɨɳɧɨɫɬɶ, ɪɚɫɫɟɢɜɚɟɦɚɹ ɧɚ ɧɟɦ ɦɚɤɫɢɦɚɥɶɧɚ.

7.3.2. Ⱦɢɚɥɨɝɨɜɨɟ ɨɤɧɨ Optimize

ȼɵɩɨɥɧɢɦ ɜɧɚɱɚɥɟ ɚɧɚɥɢɡ ɩɟɪɟɯɨɞɧɵɯ ɩɪɨɰɟɫɫɨɜ ɜ ɫɯɟɦɟ ɧɚ ɞɨɫɬɚɬɨɱɧɨ ɤɨɪɨɬɤɨɦ ɢɧɬɟɪɜɚɥɟ ɜɪɟɦɟɧɢ (1 ɦɤɫ), ɩɪɢ ɷɬɨɦ ɜɵɜɟɞɟɦ ɧɚ ɝɪɚɮɢɤ ɡɚɜɢɫɢ- ɦɨɫɬɶ ɪɚɫɫɟɢɜɚɟɦɨɣ ɧɚ ɪɟɡɢɫɬɨɪɟ RL ɦɨɳɧɨɫɬɢ PD(RL) ɨɬ ɜɪɟɦɟɧɢ. ɉɨɫɥɟ

ɨɤɨɧɱɚɧɢɹ ɪɚɫɱɟɬɚ ɜɵɡɨɜɟɦ ɨɩɬɢɦɢɡɚɬɨɪ, ɧɚɠɚɜ CTRL+F11 ɢɥɢ . ɉɨɹɜɢɬ- ɫɹ ɞɢɚɥɨɝɨɜɨɟ ɨɤɧɨ Optimize (ɪɢɫ. 7.8).

ɋɢɧɬɚɤɫɢɫ ɡɚɞɚɧɢɹ ɩɨɢɫɤɚ ɨɩɬɢɦɚɥɶɧɨɝɨ ɪɟɲɟɧɢɹ ɫɥɟɞɭɸɳɢɣ. ɇɚɣɬɢ ɜɟ- ɥɢɱɢɧɭ ɩɚɪɚɦɟɬɪɚ, ɩɪɢ ɤɨɬɨɪɨɣ ɡɚɞɚɧɧɚɹ ɯɚɪɚɤɬɟɪɢɫɬɢɤɚ ɫɯɟɦɵ ɦɢɧɢɦɚɥɶɧɚ (ɦɚɤɫɢɦɚɥɶɧɚ ɢɥɢ ɪɚɜɧɚ ɡɚɞɚɧɧɨɦɭ ɡɧɚɱɟɧɢɸ) ɩɪɢ ɫɨɛɥɸɞɟɧɢɢ ɡɚɞɚɧɧɵɯ ɨɝɪɚ- ɧɢɱɟɧɢɣ ɜ ɜɢɞɟ ɥɨɝɢɱɟɫɤɢɯ ɜɵɪɚɠɟɧɢɣ. ɉɪɢ ɷɬɨɦ ɧɚɞɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɥɢɛɨ ɫɬɚɧɞɚɪɬɧɵɣ ɦɟɬɨɞ ɉɚɭɷɥɥɚ ɥɢɛɨ ɦɟɬɨɞ ɩɪɹɦɨɝɨ ɩɚɫɫɢɜɧɨɝɨ ɩɨɢɫɤɚ ɨɩɬɢɦɭɦɚ.

Ⱦɢɚɥɨɝɨɜɨɟ ɨɤɧɨ ɫɨɞɟɪɠɢɬ ɫɥɟɞɭɸɳɢɟ ɩɚɧɟɥɢ:

Find. ɍɤɚɡɵɜɚɸɬɫɹ ɩɚɪɚɦɟɬɪɵ ɤɨɦɩɨɧɟɧɬɨɜ ɫɯɟɦɵ, ɞɥɹ ɤɨɬɨɪɵɯ ɩɪɨɜɨ- ɞɢɬɫɹ ɨɩɬɢɦɢɡɚɰɢɹ (ɧɟ ɛɨɥɟɟ 20). ɋɨɞɟɪɠɢɬ ɫɥɟɞɭɸɳɢɟ ɩɨɥɹ.

xParameter. ɍɤɚɡɵɜɚɟɬ ɤɨɦɩɨɧɟɧɬ, ɩɚɪɚɦɟɬɪ ɤɨɬɨɪɨɝɨ ɨɩɬɢɦɢɡɢɪɭɟɬɫɹ. Ⱦɥɹ ɜɵɛɨɪɚ ɜɨɡɦɨɠɧɨɝɨ ɜɚɪɢɚɧɬɚ ɦɨɠɧɨ ɧɚɠɚɬɶ ɤɧɨɩɤɭ GET. ȼɵɛɨɪ ɜ ɷɬɨɦ ɩɨɥɟ ɚɧɚɥɨɝɢɱɟɧ ɜɵɛɨɪɭ ɜɚɪɶɢɪɭɟɦɨɝɨ ɩɚɪɚɦɟɬɪɚ ɜ ɞɢɚɥɨɝɨɜɨɦ ɨɤɧɟ Stepping.

xLow — ɧɢɠɧɢɣ ɩɪɟɞɟɥ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɚ ɤɨɦɩɨɧɟɧɬɚ.

xHigh — ɜɟɪɯɧɢɣ ɩɪɟɞɟɥ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɚ ɤɨɦɩɨɧɟɧɬɚ.

xStep — ɲɚɝ ɩɪɢɪɚɳɟɧɢɹ ɩɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɦɟɬɨɞɚ ɩɪɹɦɨɝɨ ɩɚɫɫɢɜɧɨɝɨ ɩɨɢɫɤɚ.

xCurrent — ɬɟɤɭɳɟɟ ɡɧɚɱɟɧɢɟ ɨɩɬɢɦɢɡɢɪɭɟɦɨɝɨ ɩɚɪɚɦɟɬɪɚ ɜɨ ɜɪɟɦɹ ɜɵɩɨɥ- ɧɟɧɢɹ ɩɪɨɰɟɫɫɚ ɨɩɬɢɦɢɡɚɰɢɢ.

xOptimized — ɨɩɬɢɦɚɥɶɧɨɟ ɡɧɚɱɟɧɢɟ ɩɚɪɚɦɟɬɪɚ ɩɨ ɬɟɤɭɳɢɦ ɪɟɡɭɥɶɬɚɬɚɦ ɜɵɩɨɥɧɹɟɦɨɣ ɨɩɬɢɦɢɡɚɰɢɢ.

Ɋɢɫ. 7.8. Ⱦɢɚɥɨɝɨɜɨɟ ɨɤɧɨ OPTIMIZE

That: ɍɤɚɡɵɜɚɟɬɫɹ ɰɟɥɶ ɩɪɨɜɟɞɟɧɢɹ ɨɩɬɢɦɢɡɚɰɢɢ, ɜɤɥɸɱɚɹ ɤɪɢɬɟɪɢɣ ɨɩ- ɬɢɦɢɡɚɰɢɢ (ɦɚɤɫɢɦɭɦ, ɦɢɧɢɦɭɦ, ɨɩɪɟɞɟɥɟɧɧɨɟ ɡɧɚɱɟɧɢɟ) ɢ ɰɟɥɟɜɭɸ ɮɭɧɤ- ɰɢɸ, ɜɵɛɢɪɚɟɦɭɸ ɤɚɤ ɩɪɚɜɢɥɨ ɢɡ ɫɩɢɫɤɚ ɮɭɧɤɰɢɣ PERFORMANCE. ɐɟɥɟɜɚɹ ɮɭɧɤɰɢɹ — ɷɬɨ ɮɭɧɤɰɢɹ (ɢɥɢ ɤɨɦɩɥɟɤɫ ɧɟ ɛɨɥɟɟ ɱɟɦ 20 ɮɭɧɤɰɢɣ), ɤɨɬɨɪɚɹ ɜ

ɪɟɡɭɥɶɬɚɬɟ ɩɪɨɜɟɞɟɧɢɹ ɨɩɬɢɦɢɡɚɰɢɢ ɞɨɥɠɧɚ ɩɪɢɧɹɬɶ ɡɚɞɚɧɧɨɟ ɨɩɬɢɦɚɥɶɧɨɟ ɡɧɚɱɟɧɢɟ (ɦɧɨɠɟɫɬɜɨ ɡɧɚɱɟɧɢɣ).

x(Maximizes, Minimizes, Equates) — ɩɨɥɹ ɜɵɛɨɪɚ ɤɪɢɬɟɪɢɟɜ ɨɩɬɢɦɢɡɚɰɢɢ. Ɇɨɠɧɨ ɡɚɞɚɬɶ ɞɨɫɬɢɠɟɧɢɟ ɮɭɧɤɰɢɟɣ ɦɚɤɫɢɦɚɥɶɧɨɝɨ ɢɥɢ ɦɢɧɢɦɚɥɶɧɨɝɨ ɡɧɚ- ɱɟɧɢɹ, ɡɚɞɚɧɧɨɝɨ ɡɧɚɱɟɧɢɹ (Equates). Ɏɭɧɤɰɢɹ ɢɡ ɪɚɡɞɟɥɚ PERFORMANCE ɭɤɚɡɵɜɚɟɬɫɹ ɜ ɫɥɟɞɭɸɳɟɦ ɩɨɥɟ ɫɩɪɚɜɚ, ɫ ɩɨɦɨɳɶɸ ɤɧɨɩɤɢ GET.

x– — ɭɞɚɥɹɟɬ ɤɪɢɬɟɪɢɣ ɨɩɬɢɦɢɡɚɰɢɢ ɢɡ ɬɟɤɭɳɟɣ ɫɬɪɨɤɢ.

Time (ɬɨɥɶɤɨ ɜ MC10). ɉɨɥɟ ɩɨɤɚɡɵɜɚɟɬ ɩɨɥɧɨɟ ɜɪɟɦɹ, ɡɚɬɪɚɱɟɧɧɨɟ ɧɚ ɞɚɧɧɵɣ ɫɟɚɧɫ ɨɩɬɢɦɢɡɚɰɢɢ.

RMS Error. ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɤɪɢɬɟɪɢɟɜ ɨɩɬɢɦɢɡɚɰɢɢ ɬɢɩɚ Equate, ɜ

ɷɬɨɦ ɩɨɥɟ ɜɵɜɨɞɢɬɫɹ ɫɪɟɞɧɟɤɜɚɞɪɚɬɢɱɟɫɤɨɟ ɨɬɤɥɨɧɟɧɢɟ ɰɟɥɟɜɨɣ ɮɭɧɤɰɢɢ (ɤɨɦɩɥɟɤɫɚ ɮɭɧɤɰɢɣ) ɨɬ ɡɚɞɚɧɧɵɯ ɡɧɚɱɟɧɢɣ — ɤɨɪɟɧɶ ɤɜɚɞɪɚɬɧɵɣ ɢɡ ɫɭɦɦɵ ɤɜɚɞɪɚɬɨɜ ɨɬɤɥɨɧɟɧɢɣ ɬɟɤɭɳɢɯ ɡɧɚɱɟɧɢɣ ɮɭɧɤɰɢɣ ɨɬ ɡɚɞɚɧɧɵɯ ɡɧɚɱɟɧɢɣ.

Percent Error (ɬɨɥɶɤɨ ɜ MC10). ɉɪɢ ɨɩɬɢɦɢɡɚɰɢɢ ɧɚ ɫɨɜɩɚɞɟɧɢɟ (equate) ɩɨɤɚɡɵɜɚɟɬ ɫɪɟɞɧɸɸ ɨɬɧɨɫɢɬɟɥɶɧɭɸ ɨɲɢɛɤɭ ɜ %.

Constraints (ɬɨɥɶɤɨ ɜ MC9). ɋɨɞɟɪɠɢɬ 4 ɩɨɥɹ ɞɥɹ ɭɤɚɡɚɧɢɹ ɨɝɪɚɧɢɱɟɧɢɣ ɜ ɜɢɞɟ ɥɨɝɢɱɟɫɤɢɯ ɜɵɪɚɠɟɧɢɣ.

ɉɪɢɦɟɪɵ: PD(R1)<=100m V(OUT)>=1.2

VCE(Q1)*IC(Q1)<=200m

ȼ ɜɟɪɫɢɢ MC10 ɭɫɬɚɧɨɜɤɚ ɨɝɪɚɧɢɱɟɧɢɣ ɩɪɨɢɫɯɨɞɢɬ ɜ ɞɢɚɥɨɝɟ, ɜɵɡɵɜɚɟ- ɦɨɦ ɩɨɫɥɟ ɧɚɠɚɬɢɹ ɤɨɦɚɧɞɧɨɣ ɤɧɨɩɤɢ Constraints.

Ʉɨɦɚɧɞɧɵɟ ɤɧɨɩɤɢ

Optimize — ɡɚɩɭɫɤɚɟɬ ɩɪɨɰɟɫɫ ɨɩɬɢɦɢɡɚɰɢɢ. Stop — ɨɫɬɚɧɚɜɥɢɜɚɟɬ ɩɪɨɰɟɫɫ ɨɩɬɢɦɢɡɚɰɢɢ.

Apply — ɦɨɞɢɮɢɰɢɪɭɟɬ ɫɯɟɦɭ ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɧɚɣɞɟɧɧɵɦɢ ɨɩɬɢɦɚɥɶ- ɧɵɦɢ ɡɧɚɱɟɧɢɹɦɢ ɩɚɪɚɦɟɬɪɨɜ.

Format — ɩɨɡɜɨɥɹɟɬ ɜɵɛɪɚɬɶ ɱɢɫɥɨɜɨɣ ɮɨɪɦɚɬ ɢɧɞɢɰɢɪɭɟɦɵɯ ɡɧɚɱɟɧɢɣ Settings — ɩɨɡɜɨɥɹɟɬ ɜɵɛɪɚɬɶ ɫɥɟɞɭɸɳɢɟ ɭɫɬɚɧɨɜɤɢ ɞɥɹ ɨɩɬɢɦɢɡɚɬɨɪɚ:

xMaximum Relative Per-iteration Error. Ɉɩɬɢɦɢɡɚɰɢɢ ɡɚɜɟɪɲɚɟɬɫɹ, ɟɫɥɢ ɨɬɧɨ-

ɫɢɬɟɥɶɧɚɹ ɪɚɡɧɨɫɬɶ ɫɪɟɞɧɟɤɜɚɞɪɚɬɢɱɟɫɤɢɯ ɨɲɢɛɨɤ ɰɟɥɟɜɵɯ ɮɭɧɤɰɢɣ ɧɚ ɫɨɫɟɞɧɢɯ ɢɬɟɪɚɰɢɹɯ ɫɬɚɧɨɜɢɬɫɹ ɦɟɧɶɲɟ ɷɬɨɝɨ ɡɧɚɱɟɧɢɹ. Ɍɢɩɢɱɧɨɟ ɡɧɚɱɟ- ɧɢɟ ɧɚɯɨɞɢɬɫɹ ɜ ɞɢɚɩɚɡɨɧɟ ɨɬ 1E-6 ɞɨ 1E-3.

xMaximum Percentage Error. Ɉɩɬɢɦɢɡɚɰɢɹ ɡɚɜɟɪɲɚɟɬɫɹ, ɟɫɥɢ ɫɪɟɞɧɟɤɜɚɞɪɚ- ɬɢɱɟɫɤɚɹ ɨɲɢɛɤɚ ɰɟɥɟɜɨɣ ɮɭɧɤɰɢɢ, ɜɵɪɚɠɟɧɧɚɹ ɜ ɩɪɨɰɟɧɬɚɯ, ɫɬɚɧɨɜɢɬɫɹ ɦɟɧɶɲɟ ɷɬɨɣ ɜɟɥɢɱɢɧɵ. Ɍɢɩɢɱɧɨɟ ɡɧɚɱɟɧɢɟ ɨɬ 0,1 ɞɨ 5.

xInitial Range Factor. Ɂɧɚɱɟɧɢɹ ɷɬɨɝɨ ɩɨɥɹ ɢɫɩɨɥɶɡɭɸɬɫɹ ɞɥɹ ɫɨɡɞɚɧɢɹ ɨɛ- ɥɚɫɬɢ ɩɨɢɫɤɚ ɷɤɫɬɪɟɦɭɦɚ (ɡɧɚɱɟɧɢɣ Low ɢ High) ɞɥɹ ɨɩɬɢɦɢɡɢɪɭɟɦɵɯ ɩɚ- ɪɚɦɟɬɪɨɜ. Ɂɧɚɱɟɧɢɟ ɷɬɨɝɨ ɩɨɥɹ ɦɨɠɟɬ ɛɵɬɶ ɢɡɦɟɧɟɧɨ ɩɨɥɶɡɨɜɚɬɟɥɟɦ. ȼɟ- ɥɢɱɢɧɵ Low ɢ High ɨɩɪɟɞɟɥɹɸɬɫɹ ɩɨ ɮɨɪɦɭɥɚɦ:

ȼ ɜɟɪɫɢɢ MC10 ɤɨɦɚɧɞɚ Settings ɩɨɡɜɨɥɹɟɬ ɜɵɛɪɚɬɶ ɢɫɩɨɥɶɡɭɟɦɵɟ ɦɟ- ɬɨɞɵ ɨɩɬɢɦɢɡɚɰɢɢ (ɫɦ. ɪɢɫ. 6.15) ɢ ɩɪɨɢɡɜɟɫɬɢ ɭɫɬɚɧɨɜɤɭ ɩɚɪɚɦɟɬɪɨɜ ɞɥɹ ɤɚ- ɠɞɨɝɨ ɢɡ ɧɢɯ. ɉɚɪɚɦɟɬɪɵ ɩɨɞɨɛɧɵ ɩɚɪɚɦɟɬɪɚɦ, ɩɟɪɟɱɢɫɥɟɧɧɵɦ ɜɵɲɟ ɞɥɹ ɦɟɬɨɞɚ ɉɚɭɷɥɥɚ.

Constraints (MC10). ȼɵɡɵɜɚɟɬ ɨɤɧɨ ɫ ɱɟɬɵɪɶɦɹ ɫɜɨɛɨɞɧɵɦɢ ɩɨɥɹɦɢ ɞɥɹ ɭɫɬɚɧɨɜɤɢ ɜ ɧɢɯ ɨɝɪɚɧɢɱɟɧɢɣ ɜ ɜɢɞɟ ɥɨɝɢɱɟɫɤɢɯ ɜɵɪɚɠɟɧɢɣ.

ɉɨɫɥɟ ɷɬɨɝɨ ɧɚɠɚɬɢɟɦ ɧɚ ɩɢɤɬɨɝɪɚɦɦɭ ɢɥɢ ɤɨɦɛɢɧɚɰɢɟɣ ɤɥɚɜɢɲ Ctrl+F11 ɨɬɤɪɵɜɚɸɬ ɞɢɚɥɨɝɨɜɨɟ ɨɤɧɨ, ɩɨɤɚɡɚɧɧɨɟ ɧɚ ɪɢɫ. 7.10 ɫɥɟɜɚ.

Ɋɢɫ. 7.10. ɉɪɢɦɟɪ ɨɩɬɢɦɢɡɚɰɢɢ ɫɯɟɦɵ ɤɨɥɟɛɚɬɟɥɶɧɨɝɨ ɤɨɧɬɭɪɚ ɧɚ ɡɚɞɚɧɧɭɸ ɑɏ

ȼ ɩɪɢɜɟɞɟɧɧɨɦ ɩɪɢɦɟɪɟ ɬɪɟɛɭɟɬɫɹ ɢɡɦɟɧɟɧɢɟɦ L1, C1, R1 ɞɨɛɢɬɶɫɹ ɬɚɤɨ- ɝɨ ɜɢɞɚ ɑɏ, ɱɬɨɛɵ ɨɧɚ ɦɚɤɫɢɦɚɥɶɧɨ ɛɥɢɡɤɨ ɩɪɨɯɨɞɢɥɚ ɜɨɡɥɟ 6 ɡɚɞɚɧɧɵɯ ɬɨ-

ɱɟɤ (ɱɚɫɬɨɬɚ, ɚɦɩɥɢɬɭɞɚ ɜ ɞȻ): (2E6, 2.188), (4E6, 10.449), (6E6, -1.696), (8E6, -9.103), (10E6, -13.939), (20E6, -27.134). ɗɬɨ ɨɡɧɚɱɚɟɬ, ɱɬɨ ɤɨɪɟɧɶ ɤɜɚɞ-

ɪɚɬɧɵɣ ɢɡ ɫɭɦɦɵ ɤɜɚɞɪɚɬɨɜ ɨɬɤɥɨɧɟɧɢɣ ɩɨɥɭɱɟɧɧɨɣ ɑɏ ɨɬ ɡɚɞɚɧɧɵɯ ɡɧɚɱɟ- ɧɢɣ ɜ ɡɚɞɚɧɧɵɯ ɬɨɱɤɚɯ ɞɨɥɠɟɧ ɛɵɬɶ ɦɢɧɢɦɚɥɶɧɵɦ. ȼ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɡɚɞɚɧ- ɧɨɣ ɰɟɥɟɜɨɣ ɮɭɧɤɰɢɟɣ ɡɚɩɨɥɧɹɸɬɫɹ ɩɨɥɹ ɝɪɭɩɩɵ THAT ɜ ɨɤɧɟ Optimize (ɫɦ.

ɪɢɫ. 7.10). ɇɚɩɪɢɦɟɪ, Y_Level(DB(V(OUT)),1,1,2e+006) ɨɛɨɡɧɚɱɚɟɬ ɡɧɚɱɟɧɢɟ ɤɪɢɜɨɣ ɨɩɬɢɦɢɡɢɪɭɟɦɨɣ ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ ( ɑɏ ɜ ɞȻ) ɩɪɢ ɡɧɚɱɟɧɢɢ ɧɟɡɚɜɢɫɢ- ɦɨɣ ɩɟɪɟɦɟɧɧɨɣ (ɱɚɫɬɨɬɵ), ɪɚɜɧɨɣ 2E6 Hz. ȼɵɛɨɪ Equates ɨɡɧɚɱɚɟɬ, ɱɬɨ ɨɩ- ɬɢɦɢɡɚɰɢɹ ɢɞɟɬ ɞɥɹ ɧɚɢɥɭɱɲɟɝɨ ɭɞɨɜɥɟɬɜɨɪɟɧɢɹ ɭɫɥɨɜɢɸ ɪɚɜɟɧɫɬɜɚ ɤɨɷɮ- ɮɢɰɢɟɧɬɚ ɩɟɪɟɞɚɱɢ ɜ ɞȻ ɡɚɞɚɧɧɨɦɭ ɡɧɚɱɟɧɢɸ 2.188. ɉɨɞɨɛɧɵɦ ɨɛɪɚɡɨɦ ɡɚ- ɩɨɥɧɹɸɬɫɹ ɜɫɟ 6 ɩɨɥɟɣ ɰɟɥɟɜɨɣ ɮɭɧɤɰɢɢ ɫɨɝɥɚɫɧɨ ɡɚɞɚɧɧɵɦ ɡɧɚɱɟɧɢɹɦ ɩɟ- ɪɟɞɚɱɢ ɜ ɞȻ ɧɚ ɪɚɡɥɢɱɧɵɯ ɱɚɫɬɨɬɚɯ. ɐɟɥɟɜɨɣ ɮɭɧɤɰɢɟɣ ɩɪɢ ɜɵɛɨɪɟ ɭɤɚɡɚɧ- ɧɵɯ ɤɪɢɬɟɪɢɟɜ ɨɩɬɢɦɢɡɚɰɢɢ ɛɭɞɟɬ ɫɪɟɞɧɟɤɜɚɞɪɚɬɢɱɟɫɤɨɟ ɨɬɤɥɨɧɟɧɢɟ ɡɧɚɱɟ- ɧɢɣ ɮɭɧɤɰɢɣ ɜ 6 ɬɨɱɤɚɯ ɧɚ ɱɚɫɬɨɬɧɨɣ ɨɫɢ ɨɬ ɡɚɞɚɧɧɵɯ ɡɧɚɱɟɧɢɣ ɤɨɷɮɮɢɰɢɟɧ- ɬɨɜ ɩɟɪɟɞɚɱɢ. ɗɬɚ ɜɟɥɢɱɢɧɚ ɜ ɩɪɨɰɟɫɫɟ ɩɪɨɜɟɞɟɧɢɹ ɨɩɬɢɦɢɡɚɰɢɢ ɛɭɞɟɬ ɜɵ- ɜɨɞɢɬɶɫɹ ɜ ɩɨɥɟ RMS Error. Ⱦɥɹ ɪɚɫɫɦɚɬɪɢɜɚɟɦɨɝɨ ɩɪɢɦɟɪɚ: