Микросхемотехника / amelina_m_a_amelin_s_a_programma_shemotehnicheskogo_modeliro

Value Format (Cursor Format ɜ MC10) — ɮɨɪɦɚɬ ɜɵɜɨɞɚ ɤɨɨɪɞɢɧɚɬ ɬɨɱɟɤ ɩɨ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɣ ɨɫɢ ɜ ɪɟɠɢɦɟ Cursor Mode ɢ ɧɚ ɜɵɧɨɫɧɵɯ ɢ ɪɚɡɦɟɪɧɵɯ ɥɢɧɢɹɯ.

Auto Scale. Ʉɨɦɚɧɞɚ ɨɰɟɧɢɜɚɟɬ ɡɧɚɱɟɧɢɹ ɦɚɤɫɢɦɚɥɶɧɨɣ ɢ ɦɢɧɢɦɚɥɶ- ɧɨɣ ɜɟɥɢɱɢɧ, ɨɬɤɥɚɞɵɜɚɟɦɵɯ ɩɨ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɣ ɨɫɢ, ɢ, ɜ ɫɨɨɬɜɟɬ- ɫɬɜɢɢ ɫ ɷɬɢɦ, ɚɜɬɨɦɚɫɲɬɚɛɢɪɭɟɬ ɝɪɚɮɢɤ. Ɋɟɡɭɥɶɬɚɬ ɫɬɚɧɨɜɢɬɫɹ ɜɢɞɟɧ ɩɨɫɥɟ ɧɚɠɚɬɢɹ ɤɧɨɩɤɢ Apply (ɉɪɢɦɟɧɢɬɶ).

Log. ɉɨɫɥɟ ɩɪɨɜɟɪɤɢ ɡɧɚɱɟɧɢɣ, ɨɬɤɥɚɞɵɜɚɟɦɵɯ ɩɨ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɣ ɨɫɢ (ɨɧɢ ɞɨɥɠɧɵ ɛɵɬɶ ɩɨɥɨɠɢɬɟɥɶɧɵɦɢ), ɭɫɬɚɧɚɜɥɢɜɚɟɬ ɧɚ ɧɟɣ ɥɨ- ɝɚɪɢɮɦɢɱɟɫɤɢɣ ɦɚɫɲɬɚɛ.

Auto/Static Grids — ɱɢɫɥɨ ɥɢɧɢɣ ɤɨɨɪɞɢɧɚɬɧɨɣ ɫɟɬɤɢ ɩɨ ɫɨɨɬɜɟɬɫɬ- ɜɭɸɳɟɣ ɨɫɢ ɩɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɚɜɬɨɦɚɫɲɬɚɛɢɪɨɜɚɧɢɹ (Auto Scale) ɢɥɢ ɫɬɚɬɢɱɟɫɤɨɣ ɤɨɨɪɞɢɧɚɬɧɨɣ ɫɟɬɤɢ (Statics Grids).

Enable Scaling — ɪɚɡɪɟɲɚɟɬ ɨɩɟɪɚɰɢɢ ɦɚɫɲɬɚɛɢɪɨɜɚɧɢɹ ɢ ɩɚɧɨɪɚ- ɦɢɪɨɜɚɧɢɹ ɜɞɨɥɶ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɣ ɨɫɢ.

xStatic Grids. ɉɪɢ ɜɵɛɨɪɟ ɷɬɨɣ ɨɩɰɢɢ ɢɫɩɨɥɶɡɭɟɬɫɹ ɤɨɥɢɱɟɫɬɜɨ ɥɢɧɢɣ ɤɨɨɪ- ɞɢɧɚɬɧɨɣ ɫɟɬɤɢ N, ɭɤɚɡɚɧɧɨɟ ɜ ɩɨɡɢɰɢɢ Auto/Static Grids. ɉɪɢ ɷɬɨɦ ɲɚɝ ɤɨ- ɨɪɞɢɧɚɬɧɨɣ ɫɟɬɤɢ ɩɪɢɧɭɞɢɬɟɥɶɧɨ ɩɪɢɧɢɦɚɟɬɫɹ ɪɚɜɧɵɦ (Range High–Range Low)/N ɢ ɫɨɯɪɚɧɹɟɬɫɹ ɪɚɜɧɨɣ ɷɬɨɣ ɜɟɥɢɱɢɧɟ ɩɪɢ ɜɫɟɯ ɩɨɫɥɟɞɭɸɳɢɯ ɨɩɟɪɚ- ɰɢɹɯ ɦɚɫɲɬɚɛɢɪɨɜɚɧɢɹ ɢ ɩɚɧɨɪɚɦɢɪɨɜɚɧɢɹ.

xKeep X Scales the Same. ɍɫɬɚɧɚɜɥɢɜɚɟɬ ɨɞɢɧɚɤɨɜɵɟ ɦɚɫɲɬɚɛɵ ɩɨ ɨɫɢ ɏ, ɞɥɹ ɜɫɟɯ ɝɪɚɮɢɱɟɫɤɢɯ ɨɤɨɧ, ɢɫɩɨɥɶɡɭɸɳɢɯ ɜ ɤɚɱɟɫɬɜɟ X-ɩɟɪɟɦɟɧɧɨɣ ɨɞɧɭ ɢ ɬɭ ɠɟ ɩɟɪɟɦɟɧɧɭɸ (ɜɵɪɚɠɟɧɢɟ) ɩɪɢ ɜɫɟɯ ɩɨɫɥɟɞɭɸɳɢɯ ɨɩɟɪɚɰɢɹɯ ɦɚɫ- ɲɬɚɛɢɪɨɜɚɧɢɹ ɢ ɩɚɧɨɪɚɦɢɪɨɜɚɧɢɹ.

xSlope Calculation. ɋɩɢɫɨɤ ɫɩɨɫɨɛɨɜ ɜɵɱɢɫɥɟɧɢɹ ɩɪɨɢɡɜɨɞɧɵɯ ɜɵɪɚɠɟɧɢɣ, ɨɬɤɥɚɞɵɜɚɟɦɵɯ ɩɨ ɨɫɢ Y. Ɇɨɠɧɨ ɜɵɛɪɚɬɶ ɨɛɵɱɧɵɣ ɫɩɨɫɨɛ ɜɵɱɢɫɥɟɧɢɹ ɤɚɤ ɨɬɧɨɲɟɧɢɟ ɩɪɢɪɚɳɟɧɢɣ (Normal); ɞȻ/ɞɟɤ (dB/dec); ɞȻ/ɨɤɬɚɜɭ (dB/oct). ɉɨ-

ɫɥɟɞɧɢɟ ɞɜɚ ɫɩɨɫɨɛɚ ɢɫɩɨɥɶɡɭɸɬɫɹ ɜ ɪɟɠɢɦɟ ɦɚɥɨɫɢɝɧɚɥɶɧɨɝɨ ɱɚɫɬɨɬɧɨɝɨ ɚɧɚɥɢɡɚ AC.

xSame Y Scales for Each Plot Group. ȼ ɪɟɡɭɥɶɬɚɬɟ ɭɫɬɚɧɨɜɤɢ ɷɬɨɝɨ ɮɥɚɝɚ ɩɪɢ ɚɜɬɨɦɚɫɲɬɚɛɢɪɨɜɚɧɢɢ ɜɫɟɯ ɝɪɚɮɢɤɨɜ ɨɞɧɨɝɨ ɝɪɚɮɢɱɟɫɤɨɝɨ ɨɤɧɚ ɩɨɞ- ɛɢɪɚɟɬɫɹ ɟɞɢɧɵɣ ɦɚɫɲɬɚɛ ɩɨ ɨɫɹɦ, ɩɨɞɯɨɞɹɳɢɣ ɞɥɹ ɪɚɡɦɟɳɟɧɢɹ ɜɫɟɯ ɝɪɚɮɢɤɨɜ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɚɜɬɨɦɚɫɲɬɚɛɢɪɨɜɚɧɢɟ ɩɪɨɢɡɜɨɞɢɬɫɹ ɨɬ- ɞɟɥɶɧɨ ɞɥɹ ɤɚɠɞɨɝɨ ɝɪɚɮɢɤɚ ɨɞɧɨɝɨ ɝɪɚɮɢɱɟɫɤɨɝɨ ɨɤɧɚ.

xSave Range Edits. ɍɫɬɚɧɨɜɤɚ ɷɬɨɝɨ ɮɥɚɝɚ ɩɪɢɜɨɞɢɬ ɤ ɩɟɪɟɡɚɩɢɫɢ ɜɫɟɯ ɭɫ- ɬɚɧɨɜɨɤ ɞɥɹ ɦɚɫɲɬɚɛɨɜ ɜ ɷɬɨɣ ɡɚɤɥɚɞɤɟ ɜ ɭɫɬɚɧɨɜɤɢ ɨɤɧɚ Analysis Limits, ɞɟɥɚɹ ɢɯ ɩɨɫɬɨɹɧɧɨ ɞɟɣɫɬɜɭɸɳɢɦɢ.

xUse Common Formats. Ʉɨɩɢɪɭɟɬ ɭɫɬɚɧɨɜɤɢ ɮɨɪɦɚɬɨɜ ɩɨ ɨɫɹɦ ɞɥɹ ɜɵ- ɛɪɚɧɧɨɣ ɤɪɢɜɨɣ ɜ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɟ ɭɫɬɚɧɨɜɤɢ ɜɫɟɯ ɞɪɭɝɢɯ ɤɪɢɜɵɯ, ɜɵɜɨ- ɞɢɦɵɯ ɧɚ ɝɪɚɮɢɤɢ.

xCommon Y Scale. ɍɫɬɚɧɚɜɥɢɜɚɟɬ ɦɚɫɲɬɚɛ ɩɨ ɨɫɢ Y ɞɥɹ ɜɫɟɯ ɝɪɚɮɢɤɨɜ ɜɵɛɪɚɧɧɨɝɨ ɝɪɚɮɢɱɟɫɤɨɝɨ ɨɤɧɚ ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɡɚɞɚɧɧɵɦɢ ɧɚ ɩɚɧɟɥɢ Y ɡɧɚɱɟɧɢɹɦɢ.

xSmith Chart Scale Factor. ȼ ɷɬɨɦ ɩɨɥɟ ɩɪɨɢɡɜɨɞɢɬɫɹ ɭɫɬɚɧɨɜɤɚ ɦɚɫɲɬɚɛ- ɧɨɝɨ ɦɧɨɠɢɬɟɥɹ ɫɨɩɪɨɬɢɜɥɟɧɢɹ ɞɥɹ ɞɢɚɝɪɚɦɦɵ ɋɦɢɬɚ (ɨɛɵɱɧɨ ɜɵɛɢɪɚɟɬ- ɫɹ ɟɞɢɧɢɰɚ).

Colors, Fonts, and Lines

Ɂɚɤɥɚɞɤɚ ɞɥɹ ɢɡɦɟɧɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɨɬɨɛɪɚɠɟɧɢɹ ɪɚɡɥɢɱɧɵɯ ɨɛɴɟɤɬɨɜ ɝɪɚɮɢɱɟɫɤɨɝɨ ɨɤɧɚ (ɫɦ. ɪɢɫ. 8.3.).

xObjects. ɋɩɢɫɨɤ ɨɛɴɟɤɬɨɜ, ɩɚɪɚɦɟɬɪɵ ɨɬɨɛɪɚɠɟɧɢɹ ɤɨɬɨɪɵɯ ɦɨɠɧɨ ɧɚ- ɫɬɪɚɢɜɚɬɶ ɜ ɷɬɨɦ ɨɤɧɟ:

Baseline Color — ɰɜɟɬ ɧɭɥɟɜɨɣ ɥɢɧɢɢ ɤɨɨɪɞɢɧɚɬɧɨɣ ɫɟɬɤɢ.

Data Point Labels — ɰɜɟɬ ɲɪɢɮɬɚ (Foreground) ɢ ɡɚɞɧɟɝɨ ɮɨɧɚ (background) ɦɟɬɨɤ ɝɪɚɮɢɤɨɜ, ɭɫɬɚɧɚɜɥɢɜɚɟɦɵɯ ɜ ɪɟɠɢɦɟ Label Time (Frequency, Input Sweep) Points. Ɍɚɤɠɟ ɞɥɹ ɧɢɯ ɭɫɬɚɧɚɜɥɢɜɚɟɬɫɹ ɝɚɪ-

ɧɢɬɭɪɚ (Font), ɪɚɡɦɟɪ (Size) ɢ ɧɚɱɟɪɬɚɧɢɟ ɲɪɢɮɬɚ (Font style). ɉɪɢ ɠɟɥɚɧɢɢ ɦɨɠɧɨ ɢɡ ɫɩɢɫɤɚ <Select Style> ɜɵɛɪɚɬɶ ɧɟɨɛɯɨɞɢɦɵɣ ɫɬɢɥɶ ɜɵɜɨɞɚ X,Y ɤɨɨɪɞɢɧɚɬ ɬɨɱɟɤ.

General Text — ɬɟɤɫɬ, ɢɫɩɨɥɶɡɭɟɦɵɣ ɞɥɹ ɨɬɨɛɪɚɠɟɧɢɹ ɦɚɫɲɬɚɛɨɜ ɩɨ ɨɫɹɦ, ɡɚɝɨɥɨɜɤɨɜ, ɤɭɪɫɨɪɧɵɯ ɬɚɛɥɢɰ, ɢɦɟɧ ɤɪɢɜɵɯ. Ⱦɥɹ ɧɟɝɨ ɭɫɬɚɧɚɜ- ɥɢɜɚɟɬɫɹ ɰɜɟɬ (General Text), ɪɚɡɦɟɪ (Size), ɝɚɪɧɢɬɭɪɚ (Font) ɢ ɧɚɱɟɪ- ɬɚɧɢɟ ɲɪɢɮɬɚ (Font style). ɉɪɢ ɠɟɥɚɧɢɢ ɦɨɠɧɨ ɢɡ ɫɩɢɫɤɚ <Select Style> ɜɵɛɪɚɬɶ ɧɟɨɛɯɨɞɢɦɵɣ ɫɬɢɥɶ ɬɟɤɫɬɚ.

Graph Background — ɰɜɟɬ ɡɚɞɧɟɝɨ ɮɨɧɚ ɨɤɨɧ ɜɵɜɨɞɚ ɤɪɢɜɵɯ.

Grid — ɥɢɧɢɢ ɤɨɨɪɞɢɧɚɬɧɨɣ ɫɟɬɤɢ. Ⱦɥɹ ɧɢɯ ɭɫɬɚɧɚɜɥɢɜɚɟɬɫɹ ɰɜɟɬ

(Grid), ɬɨɥɳɢɧɚ (Width) ɢ ɬɢɩ ɥɢɧɢɣ (Pattern).

Select — ɰɜɟɬ ɜɵɛɪɚɧɧɨɝɨ ɞɜɨɣɧɵɦ ɤɥɢɤɨɦ ɦɵɲɢ ɨɛɴɟɤɬɚ.

Select Box — ɰɜɟɬ ɩɪɹɦɨɭɝɨɥɶɧɢɤɚ, ɨɛɪɚɡɭɟɦɨɝɨ ɛɭɤɫɢɪɨɜɤɨɣ ɥɟɜɨɣ

ɤɥɚɜɢɲɢ ɦɵɲɢ ɜ ɪɟɠɢɦɟ Select ɞɥɹ ɜɵɞɟɥɟɧɢɹ ɝɪɭɩɩɵ ɨɛɴɟɤɬɨɜ. Select Color Primary — ɜɵɛɨɪ ɰɜɟɬɚ ɝɪɚɮɢɤɚ ɜɚɪɢɚɧɬɚ ɦɧɨɝɨɜɚɪɢɚɧɬ- ɧɨɝɨ ɚɧɚɥɢɡɚ ɤ ɤɨɬɨɪɨɦɭ ɨɫɭɳɟɫɬɜɥɹɟɬɫɹ ɩɟɪɟɯɨɞ ɩɪɢ ɧɚɠɚɬɢɢ ɤɧɨɩ- ɤɢ Left ɞɢɚɥɨɝɨɜɨɝɨ ɨɤɧɚ Go To Branch.

Select Color Secondary — ɜɵɛɨɪ ɰɜɟɬɚ ɝɪɚɮɢɤɚ ɜɚɪɢɚɧɬɚ ɦɧɨɝɨɜɚɪɢ-

ɚɧɬɧɨɝɨ ɚɧɚɥɢɡɚ ɤ ɤɨɬɨɪɨɦɭ ɨɫɭɳɟɫɬɜɥɹɟɬɫɹ ɩɟɪɟɯɨɞ ɩɪɢ ɧɚɠɚɬɢɢ ɤɧɨɩɤɢ Right ɞɢɚɥɨɝɨɜɨɝɨ ɨɤɧɚ Go To Branch.

Tracker — ɤɨɨɪɞɢɧɚɬɵ ɩɟɪɟɫɟɱɟɧɢɹ ɷɥɟɤɬɪɨɧɧɵɯ ɤɭɪɫɨɪɨɜ ɫ ɝɪɚɮɢ- ɤɨɦ. Ⱦɥɹ ɧɢɯ ɭɫɬɚɧɚɜɥɢɜɚɸɬɫɹ ɰɜɟɬ ɲɪɢɮɬɚ (Foreground) ɢ ɮɨɧɚ (background), ɝɚɪɧɢɬɭɪɚ (Font), ɪɚɡɦɟɪ (Size) ɢ ɧɚɱɟɪɬɚɧɢɟ ɲɪɢɮɬɚ (Font style). ɉɪɢ ɠɟɥɚɧɢɢ ɦɨɠɧɨ ɢɡ ɫɩɢɫɤɚ <Select Style> ɜɵɛɪɚɬɶ ɧɟ- ɨɛɯɨɞɢɦɵɣ ɫɬɢɥɶ ɜɵɜɨɞɚ ɤɨɨɪɞɢɧɚɬ.

Window Background — ɰɜɟɬ ɡɚɞɧɟɝɨ ɮɨɧɚ ɨɤɧɚ ɪɟɡɭɥɶɬɚɬɨɜ ɚɧɚɥɢɡɚ. Plot All — ɭɫɬɚɧɚɜɥɢɜɚɟɬɫɹ ɰɜɟɬ (Curve Line), ɬɨɥɳɢɧɭ (Width), ɬɢɩ ɥɢɧɢɢ (Pattern), ɬɢɩ ɜɵɜɨɞɚ ɪɚɫɱɟɬɧɵɯ ɬɨɱɟɤ (points), ɫɬɢɥɶ ɜɵɜɨɞɚ ɝɪɚɮɢɤɨɜ (Style), ɚ ɬɚɤɠɟ ɰɜɟɬ ɜɵɪɚɠɟɧɢɣ ɝɪɚɮɢɤɨɜ ɢ ɱɢɫɟɥ ɜ ɫɬɪɨɤɟ ɤɭɪɫɨɪɧɨɣ ɬɚɛɥɢɰɵ, ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɣ ɜɵɪɚɠɟɧɢɸ Y (Curve Text), ɨɞ- ɧɨɜɪɟɦɟɧɧɨ ɞɥɹ ɜɫɟɯ ɝɪɚɮɢɤɨɜ.

ɋɩɢɫɨɤ ɨɬɨɛɪɚɠɚɟɦɵɯ ɝɪɚɮɢɤɨɜ. ɉɨɡɜɨɥɹɟɬ ɭɫɬɚɧɚɜɥɢɜɚɬɶ ɰɜɟɬ

(Curve Line), ɬɨɥɳɢɧɭ (Width), ɬɢɩ ɥɢɧɢɢ (Pattern), ɬɢɩ ɜɵɜɨɞɚ ɪɚɫ-

ɱɟɬɧɵɯ ɬɨɱɟɤ (points), ɫɬɢɥɶ ɜɵɜɨɞɚ ɝɪɚɮɢɤɨɜ (Style), ɚ ɬɚɤɠɟ ɰɜɟɬ ɜɵɪɚɠɟɧɢɹ ɝɪɚɮɢɤɚ ɢ ɱɢɫɟɥ ɜ ɫɬɪɨɤɟ ɤɭɪɫɨɪɧɨɣ ɬɚɛɥɢɰɵ, ɫɨɨɬɜɟɬɫɬ- ɜɭɸɳɟɣ ɜɵɛɪɚɧɧɨɦɭ Y-ɜɵɪɚɠɟɧɢɸ (Curve Text), ɩɨ ɨɬɞɟɥɶɧɨɫɬɢ ɞɥɹ ɤɚɠɞɨɝɨ ɝɪɚɮɢɤɚ.

ɉɟɪɟɱɢɫɥɟɧɧɵɟ ɜɨɡɦɨɠɧɨɫɬɢ ɭɩɪɚɜɥɟɧɢɹ ɜɵɜɨɞɨɦ ɝɪɚɮɢɤɨɜ ɢ ɤɨɨɪɞɢɧɚɬ ɤɭɪɫɨɪɨɜ ɞɨɩɨɥɧɟɧɵ ɜ MC10 ɭɩɪɚɜɥɟɧɢɟɦ ɮɨɪɦɚɬɨɦ ɱɢɫɥɨɜɵɯ ɞɚɧɧɵɯ ɧɚ ɪɚɡɦɟɪɧɵɯ ɥɢɧɢɹɯ ɢ ɜ ɪɟɡɭɥɶɬɚɬɚɯ ɜɵɱɢɫɥɟɧɢɣ ɫ ɩɨɦɨɳɶɸ ɮɨɪɦɭɥɶɧɨɝɨ ɬɟɤ-

ɫɬɚ: Plot Tag, Formula Text.

Ɉɬɧɨɫɢɬɟɥɶɧɨ ɫɬɢɥɟɣ ɜɵɜɨɞɚ ɝɪɚɮɢɤɨɜ (Style) ɫɥɟɞɭɟɬ ɨɬɦɟɬɢɬɶ, ɱɬɨ ɨɧɢ ɦɨɝɭɬ ɛɵɬɶ ɫɥɟɞɭɸɳɢɦɢ:

–Normal. ɇɚɢɛɨɥɟɟ ɱɚɫɬɨɬ ɢɫɩɨɥɶɡɭɟɦɵɣ ɫɬɢɥɶ ɨɬɨɛɪɚɠɟɧɢɹ, ɡɚɤɥɸ-

ɱɚɸɳɢɣɫɹ ɜ ɩɪɨɪɢɫɨɜɤɟ ɩɪɹɦɨɣ ɥɢɧɢɢ ɦɟɠɞɭ ɪɚɫɱɟɬɧɵɦɢ ɬɨɱɤɚɦɢ (ɥɢɧɟɣɧɚɹ ɢɧɬɟɪɩɨɥɹɰɢɹ).

–Popsicle. ȼɵɜɨɞ ɪɚɫɱɟɬɧɵɯ ɬɨɱɟɤ ɫ ɩɟɪɩɟɧɞɢɤɭɥɹɪɨɦ ɫɨɟɞɢɧɹɸɳɢɦ ɢɯ ɫ ɨɫɶɸ X. Ɍɚɤɨɣ ɫɩɨɫɨɛ ɨɛɵɱɧɨ ɢɫɩɨɥɶɡɭɟɬɫɹ ɞɥɹ ɨɬɨɛɪɚɠɟɧɢɹ ɫɩɟɤɬɪɨɜ ɢ ɮɭɧɤɰɢɣ ɩɪɨɢɡɜɨɞɧɵɯ ɨɬ ɧɢɯ (ɫɦ. ɪɢɫ. 7.12).

x Rainbow. ɍɫɬɚɧɨɜɤɚ ɷɬɨɣ ɨɩɰɢɢ ɩɪɢɫɜɚɢɜɚɟɬ ɤɚɠɞɨɣ ɤɪɢɜɨɣ ɦɧɨɝɨɜɚɪɢ- ɚɧɬɧɨɝɨ ɚɧɚɥɢɡɚ ɪɚɡɧɵɣ ɰɜɟɬ.

x Sample. ɉɨɤɚɡɵɜɚɟɬ ɨɛɪɚɡɟɰ ɬɟɤɭɳɟɝɨ ɢɡɦɟɧɟɧɢɹ ɫɜɨɣɫɬɜ ɨɛɴɟɤɬɨɜ.

SCOPE

ɍɫɬɚɧɨɜɤɢ ɷɬɨɣ ɡɚɤɥɚɞɤɢ ɞɭɛɥɢɪɭɸɬ ɨɞɧɨɢɦɟɧɧɵɟ ɤɨɦɚɧɞɵ ɦɟɧɸ Scope. ȼɟɪɫɢɹ MC10 ɨɬɥɢɱɚɟɬɫɹ ɬɟɦ, ɱɬɨ ɜ ɷɬɨɣ ɡɚɤɥɚɞɤɟ ɩɨɹɜɢɥɢɫɶ ɞɨɩɨɥɧɢ-

ɬɟɥɶɧɵɟ ɤɨɦɚɧɞɵ: Align Cursors, Keep Cursors on Same Branch, ɩɨɡɜɨɥɹɸɳɢɟ ɭɫɬɚɧɚɜɥɢɜɚɬɶ ɫɜɹɡɚɧɧɨɫɬɶ ɤɭɪɫɨɪɨɜ ɢ ɢɯ ɩɪɢɜɹɡɤɭ ɤ ɨɞɧɨɦɭ ɜɚɪɢɚɧɬɭ ɚɧɚɥɢ- ɡɚ ɞɥɹ ɬɟɤɭɳɟɣ ɫɯɟɦɵ.

FFT

Ɂɚɤɥɚɞɤɚ, ɭɩɪɚɜɥɹɸɳɚɹ ɩɚɪɚɦɟɬɪɚɦɢ ɜɵɱɢɫɥɟɧɢɹ ɮɭɧɤɰɢɣ ɫɩɟɤɬɪɚɥɶɧɨ- ɝɨ ɚɧɚɥɢɡɚ ɧɚ ɨɫɧɨɜɟ ɩɪɹɦɨɝɨ ɢ ɨɛɪɚɬɧɨɝɨ ɛɵɫɬɪɨɝɨ ɩɪɟɨɛɪɚɡɨɜɚɧɢɹ Ɏɭɪɶɟ. Ⱦɥɹ ɪɟɠɢɦɚ ɚɧɚɥɢɡɚ ɩɟɪɟɯɨɞɧɵɯ ɩɪɨɰɟɫɫɨɜ Transient:

xUpper Time Limit. ɍɤɚɡɵɜɚɟɬ ɤɨɧɟɱɧɨɟ ɡɧɚɱɟɧɢɟ ɜɪɟɦɟɧɧɨɝɨ ɢɧɬɟɪɜɚɥɚ, ɢɫɩɨɥɶɡɭɟɦɨɝɨ ɩɪɢ ɜɵɱɢɫɥɟɧɢɢ ɮɭɧɤɰɢɣ ɧɚ ɨɫɧɨɜɟ ɛɵɫɬɪɨɝɨ ɩɪɟɨɛɪɚɡɨɜɚ- ɧɢɹ Ɏɭɪɶɟ (FFT). ɉɨ ɭɦɨɥɱɚɧɢɸ ɭɫɬɚɧɚɜɥɢɜɚɟɬɫɹ TMAX.

xLower Time Limit. ɍɤɚɡɵɜɚɟɬ ɧɚɱɚɥɶɧɨɟ ɡɧɚɱɟɧɢɟ ɜɪɟɦɟɧɧɨɝɨ ɢɧɬɟɪɜɚɥɚ, ɢɫɩɨɥɶɡɭɟɦɨɝɨ ɩɪɢ ɜɵɱɢɫɥɟɧɢɢ FFT ɮɭɧɤɰɢɣ. ɉɨ ɭɦɨɥɱɚɧɢɸ ɭɫɬɚɧɚɜɥɢɜɚ- ɟɬɫɹ TMIN. ȼ Micro-Cap 10 ɩɨ ɭɦɨɥɱɚɧɢɸ ɭɫɬɚɧɚɜɥɢɜɚɟɬɫɹ TSTART.

Ɉɛɵɱɧɨ ɩɨɥɧɨɟ ɜɪɟɦɹ ɪɚɫɱɟɬɚ ɩɟɪɟɯɨɞɧɵɯ ɩɪɨɰɟɫɫɨɜ ɭɫɬɚɧɚɜɥɢɜɚɟɬɫɹ

ɨɬ 2 ɞɨ 10 ɩɟɪɢɨɞɨɜ ɝɚɪɦɨɧɢɱɟɫɤɨɝɨ ɜɨɡɞɟɣɫɬɜɢɹ ɧɚ ɜɯɨɞɟ ɞɥɹ ɭɫɬɚɧɨɜɥɟɧɢɹ ɩɟɪɟɯɨɞɧɵɯ ɩɪɨɰɟɫɫɨɜ. ɢɧɬɟɪɜɚɥ ɜɪɟɦɟɧɢ, ɧɚ ɤɨɬɨɪɨɦ ɩɪɨɢɡɜɨɞɢɬɫɹ ɫɩɟɤ- ɬɪɚɥɶɧɵɣ ɚɧɚɥɢɡ [Lower Time Limit, Upper Time Limit], ɨɛɵɱɧɨ ɭɫɬɚɧɚɜɥɢɜɚɟɬ- ɫɹ ɪɚɜɧɵɦ 1 ɩɟɪɢɨɞɭ ɜ ɤɨɧɰɟ ɩɨɥɧɨɝɨ ɢɧɬɟɪɜɚɥɚ ɪɚɫɱɟɬɚ.

xFrequency Step (MC10) – ɲɚɝ ɢɡɦɟɧɟɧɢɹ ɱɚɫɬɨɬɵ ɗɬɨ ɜɟɥɢɱɢɧɚ, ɨɛɪɚɬɧɚɹ ɪɚɡɧɨɫɬɢ ɜɟɪɯɧɟɝɨ ɢ ɧɢɠɧɟɝɨ ɩɪɟɞɟɥɨɜ Ɏɭɪɶɟ-ɚɧɚɥɢɡɚ:

Frequency Step=1/(Upper Time Limit – Lower Time Limit)

Ɉɧɚ ɪɚɜɧɚ ɱɚɫɬɨɬɟ ɩɟɪɜɨɣ ɝɚɪɦɨɧɢɤɢ ɢɫɫɥɟɞɭɟɦɨɝɨ ɫɢɝɧɚɥɚ.

xNumber of Points. Ʉɨɥɢɱɟɫɬɜɨ ɬɨɱɟɤ ɞɚɧɧɵɯ (ɜ ɬɨɦ ɱɢɫɥɟ ɢ ɢɧɬɟɪɩɨɥɢɪɨ- ɜɚɧɧɵɯ), ɢɫɩɨɥɶɡɭɟɦɵɯ ɩɪɢ ɜɵɱɢɫɥɟɧɢɢ ɤɨɷɮɮɢɰɢɟɧɬɨɜ ɛɵɫɬɪɨɝɨ ɩɪɟɨɛ- ɪɚɡɨɜɚɧɢɹ Ɏɭɪɶɟ. Ɍɢɩɢɱɧɵɟ ɡɧɚɱɟɧɢɹ — 1024, 2048, ɢɥɢ 4096.

xAuto Scaling. ɍɩɪɚɜɥɹɟɬ ɚɜɬɨɦɚɫɲɬɚɛɢɪɨɜɚɧɢɟɦ ɝɪɚɮɢɤɨɜ FFT ɮɭɧɤɰɢɣ. ȼɤɥɸɱɚɟɬ ɜ ɫɟɛɹ ɫɥɟɞɭɸɳɢɟ ɨɩɰɢɢ.

414 ɉɪɨɝɪ ɦɦ ɫɯɟɦɨɬɟɯɧɢɱɟɫɤɨɝɨ ɦɨɞɟɥɢɪɨɜ ɧɢɹ Micro-Cap. ȼɟɪɫɢɢ 9, 10

Include DC Harmonic. ȼɤɥɸɱɟɧɢɟ ɷɬɨɣ ɨɩɰɢɢ ɩɪɨɢɡɜɨɞɢɬ ɩɪɢ ɚɜɬɨ- ɦɚɫɲɬɚɛɢɪɨɜɚɧɢɢ ɭɱɟɬ ɜɟɥɢɱɢɧɵ ɩɨɫɬɨɹɧɧɨɣ ɫɨɫɬɚɜɥɹɸɳɟɣ (0-ɨɣ ɝɚɪɦɨɧɢɤɢ). Ɉɛɵɱɧɨ ɨɧɚ ɜɵɤɥɸɱɟɧɚ.

Auto Scale First .... Harmonics. ɉɨɥɟ ɭɤɚɡɵɜɚɟɬ ɤɨɥɢɱɟɫɬɜɨ ɝɚɪɦɨɧɢɤ, ɧɚɱɢɧɚɹ ɫ 1-ɨɣ, ɤɨɬɨɪɵɟ ɭɱɢɬɵɜɚɸɬɫɹ ɩɪɢ ɚɜɬɨɦɚɫɲɬɚɛɢɪɨɜɚɧɢɢ ɝɪɚɮɢɤɨɜ ɮɭɧɤɰɢɣ ɫɩɟɤɬɪɚɥɶɧɨɝɨ ɚɧɚɥɢɡɚ.

Header

Ɂɚɤɥɚɞɤɚ ɭɩɪɚɜɥɹɟɬ ɡɚɝɨɥɨɜɤɚɦɢ ɜ ɮɚɣɥɟ ɱɢɫɥɟɧɧɨɝɨ ɜɵɜɨɞɚ <ɢɦɹ ɫɯɟ- ɦɵ>.*no, ɜ ɜɵɯɨɞɧɨɦ ɮɚɣɥɟ ɪɟɞɚɤɬɨɪɚ ɧɚɱɚɥɶɧɵɯ ɭɫɥɨɜɢɣ <ɢɦɹ ɫɯɟɦɵ>.svv, ɜ ɜɵɯɨɞɧɨɦ ɮɚɣɥɟ ɫɬɚɬɢɫɬɢɤɢ ɚɧɚɥɢɡɚ Monte Carlo <ɢɦɹ ɫɯɟɦɵ>.*mc.

Numeric Output

ɇɚ ɷɬɨɣ ɡɚɤɥɚɞɤɟ ɭɤɚɡɵɜɚɟɬɫɹ, ɤɚɤɢɟ ɪɚɡɞɟɥɵ ɛɭɞɭɬ ɜɤɥɸɱɟɧɵ ɜ ɬɟɤɫɬɨ- ɜɵɣ ɮɚɣɥ ɱɢɫɥɨɜɨɝɨ ɜɵɜɨɞɚ, ɚ ɤɚɤɢɟ ɧɟɬ. Ɉɩɰɢɢ ɷɬɨɣ ɡɚɤɥɚɞɤɢ ɩɨɞɪɨɛɧɨ ɛɵ- ɥɢ ɪɚɫɫɦɨɬɪɟɧɵ ɩɪɢ ɨɩɢɫɚɧɢɢ ɱɢɫɥɨɜɨɝɨ ɜɵɜɨɞɚ ɜ ɪɟɠɢɦɟ ɚɧɚɥɢɡɚ ɩɟɪɟɯɨɞ- ɧɵɯ ɩɪɨɰɟɫɫɨɜ (ɫɦ. ɩɭɧɤɬ 6.1.6).

Save Curves

Ɂɚɤɥɚɞɤɚ ɩɨɡɜɨɥɹɟɬ ɫɨɯɪɚɧɢɬɶ ɧɚ ɞɢɫɤ ɨɞɧɭ ɢɥɢ ɧɟɫɤɨɥɶɤɨ ɤɪɢɜɵɯ ɜ ɮɨɪɦɟ ɬɚɛɥɢɱɧɨɝɨ ɬɟɤɫɬɨɜɨɝɨ ɮɚɣɥɚ ɫ ɪɚɫɲɢɪɟɧɢɟɦ .usr (ɢɥɢ .csv) ɞɥɹ ɩɨ-

ɫɥɟɞɭɸɳɟɝɨ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɜ ɤɚɱɟɫɬɜɟ ɩɨɥɶɡɨɜɚɬɟɥɶɫɤɢɯ ɢɫɬɨɱɧɢɤɨɜ ɫɢɝɧɚɥɚ

User Sources.

Curves. ɋɩɢɫɨɤ ɩɨɫɬɪɨɟɧɧɵɯ ɝɪɚɮɢɤɨɜ, ɢɡ ɤɨɬɨɪɵɯ ɜɵɛɢɪɚɟɬɫɹ ɨɞɧɨ ɢɥɢ ɧɟɫɤɨɥɶɤɨ ɜɵɪɚɠɟɧɢɣ ɞɥɹ ɩɪɢɦɟɧɟɧɢɹ ɤ ɧɟɦɭ ɨɩɰɢɣ ɡɚɤɥɚɞɤɢ.

xTemperature. ȿɫɥɢ ɚɧɚɥɢɡ ɜɵɩɨɥɧɹɥɫɹ ɩɪɢ ɧɟɫɤɨɥɶɤɢɯ ɬɟɦɩɟɪɚɬɭɪɚɯ, ɜɵ- ɛɢɪɚɟɬɫɹ ɨɞɧɚ ɧɟɨɛɯɨɞɢɦɚɹ.

xɉɟɪɟɦɟɧɧɚɹ ɦɧɨɝɨɜɚɪɢɚɧɬɧɨɝɨ ɚɧɚɥɢɡɚ (Run, *.Value). ȿɫɥɢ ɢɦɟɥ ɦɟɫɬɨ ɦɧɨɝɨɜɚɪɢɚɧɬɧɵɣ ɚɧɚɥɢɡ, ɬɨ ɢɡ ɪɚɫɤɪɵɜɚɸɳɢɯɫɹ ɫɩɢɫɤɨɜ ɜɵɛɢɪɚɟɬɫɹ ɧɟ- ɨɛɯɨɞɢɦɚɹ ɪɟɚɥɢɡɚɰɢɹ.

xSave Curve(s). ȼ ɷɬɨɦ ɩɨɥɟ ɭɫɬɚɧɚɜɥɢɜɚɟɬɫɹ ɡɚɜɢɫɢɦɨɫɬɶ, ɷɤɜɢɜɚɥɟɧɬɧɚɹ ɜɵɛɪɚɧɧɨɣ ɤɪɢɜɨɣ. Ɇɨɠɧɨ ɩɟɪɟɢɦɟɧɨɜɚɬɶ ɡɚɜɢɫɢɦɨɫɬɶ, ɧɨ ɩɪɢ ɷɬɨɦ ɧɟɨɛ-

ɯɨɞɢɦɨ ɭɱɟɫɬɶ ɢɦɹ ɧɨɜɨɣ ɡɚɜɢɫɢɦɨɫɬɢ ɩɪɢ ɩɨɫɥɟɞɭɸɳɟɦ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɟɺ ɜ ɤɚɱɟɫɬɜɟ ɢɫɬɨɱɧɢɤɚ User Source.

xNumber of Points. ɉɨɡɜɨɥɹɟɬ ɭɫɬɚɧɨɜɢɬɶ ɤɨɥɢɱɟɫɬɜɨ ɬɨɱɟɤ ɢɧɬɟɪɩɨɥɢɪɨ- ɜɚɧɧɨɣ ɡɚɜɢɫɢɦɨɫɬɢ, ɟɫɥɢ ɫɛɪɨɫɢɬɶ ɮɥɚɠɨɤ Save Actual Data Points.

xSave Actual Data Points. ȿɫɥɢ ɷɬɨɬ ɮɥɚɠɨɤ ɫɛɪɨɲɟɧ, ɤɪɢɜɚɹ ɫɨɯɪɚɧɹɟɬɫɹ ɜ ɮɚɣɥ, ɢɫɩɨɥɶɡɭɹ ɡɧɚɱɟɧɢɟ ɤɨɥɢɱɟɫɬɜɚ ɬɨɱɟɤ, ɡɚɞɚɧɧɨɟ ɜ ɩɨɥɟ Number of Points. Ɍɨɱɤɢ ɞɚɧɧɵɯ ɞɥɹ ɷɬɨɝɨ ɫɥɭɱɚɹ ɷɤɜɢɞɢɫɬɚɧɬɧɵ ɜɞɨɥɶ ɨɫɢ ɧɟɡɚɜɢɫɢ- ɦɨɣ ɩɟɪɟɦɟɧɧɨɣ ɢ ɩɨɥɭɱɚɸɬɫɹ ɩɭɬɟɦ ɢɧɬɟɪɩɨɥɹɰɢɢ ɪɚɫɱɟɬɧɵɯ ɬɨɱɟɤ. ȿɫɥɢ ɠɟ ɮɥɚɝ ɭɫɬɚɧɨɜɥɟɧ, ɬɨ ɜ ɮɚɣɥ ɛɭɞɭɬ ɫɨɯɪɚɧɹɬɶɫɹ ɪɚɫɫɱɢɬɚɧɧɵɟ ɬɨɱɤɢ ɞɚɧɧɵɯ, ɤɨɬɨɪɵɟ ɦɨɝɭɬ ɨɬɫɬɨɹɬɶ ɞɪɭɝ ɨɬ ɞɪɭɝɚ ɧɚ ɪɚɡɥɢɱɧɵɣ ɢɧɬɟɪɜɚɥ.

xIn File. ȼ ɷɬɨɦ ɩɨɥɟ ɭɤɚɡɵɜɚɟɬɫɹ ɢɦɹ ɮɚɣɥɚ, ɜ ɤɨɬɨɪɵɣ ɡɚɩɢɫɵɜɚɟɬɫɹ ɬɚɛ- ɥɢɰɚ ɡɧɚɱɟɧɢɣ ɤɪɢɜɨɣ. Ⱦɥɹ ɜɵɛɨɪɚ ɦɟɫɬɚ ɜ ɢɟɪɚɪɯɢɱɟɫɤɨɣ ɫɬɪɭɤɬɭɪɟ ɩɚɩɨɤ ɦɨɠɧɨ ɜɨɫɩɨɥɶɡɨɜɚɬɶɫɹ ɤɧɨɩɤɨɣ Browse.

xBrowse. ɉɨɡɜɨɥɹɟɬ ɜɵɛɪɚɬɶ ɞɢɪɟɤɬɨɪɢɸ, ɤɭɞɚ ɛɭɞɟɬ ɫɨɯɪɚɧɟɧ ɮɚɣɥ ɱɢɫ- ɥɟɧɧɵɯ ɡɧɚɱɟɧɢɣ ɜɵɛɪɚɧɧɨɝɨ ɝɪɚɮɢɤɚ.

xSave. Ʉɪɢɜɚɹ ɫɨɯɪɚɧɹɟɬɫɹ ɜ ɮɚɣɥɟ ɫ ɡɚɞɚɧɧɵɦ ɢɦɟɧɟɦ ɢ ɞɢɪɟɤɬɨɪɢɟɣ. Ɉɬɦɟɬɢɦ, ɱɬɨ ɟɫɥɢ ɤɪɢɜɵɟ ɫɨɯɪɚɧɹɸɬɫɹ ɜ ɭɠɟ ɫɭɳɟɫɬɜɭɸɳɢɣ ɮɚɣɥ, ɨɧɢ ɞɨɛɚɜɥɹɸɬɫɹ, ɧɟ ɭɧɢɱɬɨɠɚɹ ɩɪɟɞɵɞɭɳɭɸ ɢɧɮɨɪɦɚɰɢɸ. ȿɫɥɢ ɠɟ ɜ ɮɚɣɥɟ ɫɨɞɟɪɠɚɥɚɫɶ ɭɠɟ ɤɪɢɜɚɹ ɫ ɬɚɤɢɦ ɠɟ ɢɦɟɧɟɦ, ɬɨ ɨɧɚ ɩɟɪɟɡɚɩɢɫɵɜɚɟɬɫɹ.

xDelete. Ʉɨɦɚɧɞɚ ɭɞɚɥɹɟɬ ɤɪɢɜɵɟ ɫ ɭɤɚɡɚɧɧɵɦɢ ɢɦɟɧɚɦɢ ɢɡ ɮɚɣɥɚ.

WAV (ɬɨɥɶɤɨ ɜ MC10). ɗɬɚ ɩɚɧɟɥɶ ɡɚɤɥɚɞɤɢ ɫɥɭɠɢɬ ɞɥɹ ɭɩɪɚɜɥɟɧɢɹ ɫɨ- ɯɪɚɧɟɧɢɟɦ ɜɵɛɪɚɧɧɨɣ ɤɪɢɜɨɣ ɜ ɮɨɪɦɚɬɟ WAV-ɮɚɣɥɚ (ɫɦ. ɪɢɫ. 6.1, ɜ).

xSample Rate — ɜɵɛɨɪ ɱɚɫɬɨɬɵ ɞɢɫɤɪɟɬɢɡɚɰɢɢ.

xNumber of Bits — ɪɚɡɪɹɞɧɨɫɬɶ ɰɢɮɪɨɜɨɝɨ ɩɪɟɞɫɬɚɜɥɟɧɢɹ ɫɢɝɧɚɥɚ ɜ ɛɢɬɚɯ.

xRange — ɦɚɤɫɢɦɭɦ ɲɤɚɥɵ ɞɥɹ ɜɵɛɪɚɧɧɨɝɨ ɫɢɝɧɚɥɚ.

xPlay — ɜɨɫɩɪɨɢɡɜɟɞɟɧɢɟ ɜɵɛɪɚɧɧɨɝɨ ɫɢɝɧɚɥɚ ɱɟɪɟɡ ɝɪɨɦɤɨɝɨɜɨɪɢɬɟɥɢ.

xStop — ɩɪɟɪɜɚɬɶ ɜɨɫɩɪɨɢɡɜɟɞɟɧɢɟ ɜɵɛɪɚɧɧɨɝɨ ɫɢɝɧɚɥɚ.

xAuto Range — ɚɜɬɨɦɚɬɢɱɟɫɤɚɹ ɭɫɬɚɧɨɜɤɚ ɦɚɤɫɢɦɭɦɚ ɲɤɚɥɵ ɞɥɹ ɜɵɛɪɚɧɧɨ- ɝɨ ɫɢɝɧɚɥɚ.

Tool Bar

Ɂɚɤɥɚɞɤɚ ɩɨɡɜɨɥɹɟɬ ɭɫɬɚɧɨɜɢɬɶ ɤɧɨɩɤɢ ɥɨɤɚɥɶɧɵɯ ɩɚɧɟɥɟɣ ɢɧɫɬɪɭɦɟɧɬɨɜ ɞɥɹ ɫɯɟɦɵ ɞɥɹ ɪɚɡɧɵɯ ɪɟɠɢɦɨɜ ɪɚɛɨɬɵ (ɪɟɞɚɤɬɢɪɨɜɚɧɢɹ, ɚɧɚɥɢɡɚ, ɨɛɪɚɛɨɬɤɢ ɝɪɚɮɢɤɨɜ). ȼɵɛɨɪ ɨɫɭɳɟɫɬɜɥɹɟɬɫɹ ɭɫɬɚɧɨɜɤɨɣ ɮɥɚɠɤɨɜ ɜɨɡɥɟ ɠɟɥɚɟɦɨɣ ɩɢɤ-

ɬɨɝɪɚɦɦɵ ɢ ɭɫɬɚɧɨɜɤɨɣ ɨɩɰɢɢ ɠɟɥɚɟɦɨɝɨ ɦɟɫɬɨɩɨɥɨɠɟɧɢɹ ɫɯɟɦɧɨɣ ɩɚɧɟɥɢ ɢɧɫɬɪɭɦɟɧɬɨɜ.

ɇɚ ɡɚɤɥɚɞɤɚɯ Colors, Fonts, and Lines; Scope; FFT; Numeric Output ɢɦɟɸɬ-

ɫɹ ɤɧɨɩɤɢ Default ɢ Set Default:

xDefault. ɉɪɢɫɜɚɢɜɚɟɬ ɭɫɬɚɧɨɜɤɚɦ ɬɟɤɭɳɟɣ ɫɯɟɦɵ ɭɫɬɚɧɨɜɤɢ, ɫɞɟɥɚɧɧɵɟ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɤɨɦɚɧɞɵ Options>Default Properties For New Circuit

(Alt+F10).

xSet Default. ɉɪɢɫɜɚɢɜɚɟɬ ɭɦɨɥɱɚɬɟɥɶɧɵɦ ɭɫɬɚɧɨɜɤɚɦ ɞɥɹ ɧɨɜɨɣ ɫɯɟɦɵ

(Default Properties For New Circuit) ɡɧɚɱɟɧɢɹ ɭɫɬɚɧɨɜɨɤ ɬɟɤɭɳɟɣ ɫɯɟɦɵ.

8.4. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɮɭɧɤɰɢɣ Performance

Micro-Cap ɢɦɟɟɬ ɝɪɭɩɩɭ ɫɩɟɰɢɚɥɶɧɵɯ ɮɭɧɤɰɢɣ Performance, ɤɨɬɨɪɵɟ ɩɪɟɞɧɚɡɧɚɱɟɧɵ ɞɥɹ ɨɛɪɚɛɨɬɤɢ ɪɟɡɭɥɶɬɚɬɨɜ ɦɨɞɟɥɢɪɨɜɚɧɢɹ. ɗɬɢ ɮɭɧɤɰɢɢ ɩɨ-

ɡɜɨɥɹɸɬ ɧɚ ɨɫɧɨɜɚɧɢɢ ɚɧɚɥɢɡɚ ɩɨɥɭɱɟɧɧɨɝɨ ɝɪɚɮɢɤɚ ɡɚɜɢɫɢɦɨɫɬɢ ɜɵɱɢɫɥɢɬɶ ɧɟɤɨɬɨɪɵɟ ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ ɷɬɨɣ ɡɚɜɢɫɢɦɨɫɬɢ. ɋ ɢɯ ɩɨɦɨɳɶɸ ɦɨɠɧɨ ɢɡɦɟɪɹɬɶ ɬɚɤɢɟ ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ ɤɚɤ, ɜɪɟɦɹ ɧɚɪɚɫɬɚɧɢɹ ɢ ɫɩɚɞɚ ɢɦɩɭɥɶɫɚ, ɞɥɢɬɟɥɶɧɨɫɬɶ ɢɦɩɭɥɶɫɚ, ɱɚɫɬɨɬɚ, ɩɟɪɢɨɞ ɢ ɦɧɨɝɨɟ ɞɪɭɝɨɟ. ɇɚɩɪɢɦɟɪ, ɮɭɧɤɰɢɹ Rise_Time ɩɨɡɜɨɥɹɟɬ ɜɵɱɢɫɥɢɬɶ, ɜ ɬɟɱɟɧɢɟ ɤɚɤɨɝɨ ɜɪɟɦɟɧɢ ɩɪɨɢɡɨɲɥɨ ɧɚɪɚɫɬɚɧɢɟ ɫɢɝ- ɧɚɥɚ ɨɬ ɨɞɧɨɝɨ ɭɪɨɜɧɹ ɞɨ ɞɪɭɝɨɝɨ. Ɍ.ɟ. ɟɟ ɦɨɠɧɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɞɥɹ ɜɵɱɢɫɥɟ- ɧɢɹ ɞɥɢɬɟɥɶɧɨɫɬɢ ɮɪɨɧɬɨɜ ɢɦɩɭɥɶɫɨɜ.

Ʉɪɨɦɟ ɬɨɝɨ, ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɮɭɧɤɰɢɣ Performance ɩɪɢ ɨɛɪɚɛɨɬɤɟ ɪɟɡɭɥɶ- ɬɚɬɨɜ ɫɟɪɢɣ ɪɚɫɱɟɬɨɜ ɩɨɡɜɨɥɹɟɬ ɫɬɪɨɢɬɶ ɨɩɨɫɪɟɞɨɜɚɧɧɵɟ ɡɚɜɢɫɢɦɨɫɬɢ. ɇɚ- ɩɪɢɦɟɪ, ɩɪɢ ɩɨɦɨɳɢ ɷɬɢɯ ɮɭɧɤɰɢɣ ɦɨɠɧɨ ɩɨɫɬɪɨɢɬɶ ɝɪɚɮɢɤ ɡɚɜɢɫɢɦɨɫɬɢ ɞɥɢ- ɬɟɥɶɧɨɫɬɢ ɮɪɨɧɬɚ ɢɦɩɭɥɶɫɚ ɨɬ ɫɨɩɪɨɬɢɜɥɟɧɢɹ ɪɟɡɢɫɬɨɪɚ ɜ ɰɟɩɢ ɛɚɡɵ ɬɪɚɧɡɢ-

ɫɬɨɪɧɨɝɨ ɤɥɸɱɚ ɢɥɢ ɡɚɜɢɫɢɦɨɫɬɶ ɚɦɩɥɢɬɭɞɵ ɩɭɥɶɫɚɰɢɣ ɧɚ ɜɵɯɨɞɟ ɮɢɥɶɬɪɚ ɜɵɩɪɹɦɢɬɟɥɹ ɨɬ ɟɦɤɨɫɬɢ ɷɬɨɝɨ ɮɢɥɶɬɪɚ.

ɇɟɩɨɫɪɟɞɫɬɜɟɧɧɨɟ ɩɨɫɬɪɨɟɧɢɟ ɬɚɤɢɯ ɡɚɜɢɫɢɦɨɫɬɟɣ ɜ ɪɟɠɢɦɟ ɚɧɚɥɢɡɚ ɩɟ- ɪɟɯɨɞɧɵɯ ɩɪɨɰɟɫɫɨɜ ɧɟɜɨɡɦɨɠɧɨ. ɇɨ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɦɧɨɝɨɜɚɪɢɚɧɬɧɨɝɨ ɚɧɚ- ɥɢɡɚ ɢ ɮɭɧɤɰɢɣ Performance ɩɨɡɜɨɥɹɟɬ ɪɟɲɢɬɶ ɬɚɤɭɸ ɡɚɞɚɱɭ.

Ɏɭɧɤɰɢɢ ɪɚɡɞɟɥɚ Performance ɦɨɝɭɬ ɢɫɩɨɥɶɡɨɜɚɬɶɫɹ ɧɟɫɤɨɥɶɤɢɦɢ ɫɩɨɫɨ- ɛɚɦɢ.

8.4.1.Ɉɛɪɚɛɨɬɤɚ ɪɟɡɭɥɶɬɚɬɨɜ ɦɨɞɟɥɢɪɨɜɚɧɢɹ

ɜɪɟɠɢɦɟ Go to Performance

Ɉɤɧɨ ɪɟɠɢɦɚ Go to Performance ɜɵɡɵɜɚɟɬɫɹ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɣ ɤɨɦɚɧɞɨɣ

ɢɡ ɦɟɧɸ Scope ɢɥɢ ɩɢɤɬɨɝɪɚɦɦɨɣ ɩɨɫɥɟ ɩɨɫɬɪɨɟɧɢɹ ɝɪɚɮɢɤɨɜ ɡɚɜɢɫɢɦɨ- ɫɬɟɣ (ɪɢɫ. 8.5.). ɇɟɨɛɯɨɞɢɦɚɹ ɞɥɹ ɪɚɫɱɟɬɚ ɮɭɧɤɰɢɹ ɜɵɛɢɪɚɟɬɫɹ ɜ ɫɩɢɫɤɟ Function (ɧɚɡɧɚɱɟɧɢɟ ɮɭɧɤɰɢɣ ɪɚɫɫɦɨɬɪɟɧɨ ɧɢɠɟ). ɉɪɢ ɷɬɨɦ ɜ ɨɤɧɟ ɩɨɹɜɥɹɟɬ- ɫɹ ɝɪɚɮɢɱɟɫɤɚɹ ɩɨɞɫɤɚɡɤɚ ɧɚɡɧɚɱɟɧɢɹ ɜɵɛɪɚɧɧɨɣ ɮɭɧɤɰɢɢ, ɚ ɜ ɧɢɠɧɟɣ ɫɬɪɨɤɟ ɨɤɧɚ — ɚɥɝɨɪɢɬɦ ɟɟ ɜɵɱɢɫɥɟɧɢɹ (ɪɢɫ. 8.5).

Ɋɢɫ. 8.5. ȼɵɩɨɥɧɟɧɢɟ ɢɡɦɟɪɟɧɢɣ ɫ ɩɨɦɨɳɶɸ ɞɢɚɥɨɝɨɜɨɝɨ ɨɤɧɚ Go To Performance

ȼɵɛɪɚɧɧɚɹ ɮɭɧɤɰɢɹ ɩɪɢɦɟɧɹɟɬɫɹ ɤ ɡɚɜɢɫɢɦɨɫɬɢ, ɤɨɬɨɪɭɸ ɡɚɞɚɸɬ ɜ ɫɩɢ-

ɫɤɟ Expression.

ȼ ɩɨɥɟ Boolean ɡɚɞɚɟɬɫɹ ɥɨɝɢɱɟɫɤɨɟ ɜɵɪɚɠɟɧɢɟ, ɩɪɢ ɢɫɬɢɧɧɨɫɬɢ ɤɨɬɨɪɨɝɨ ɛɭɞɟɬ ɜɵɱɢɫɥɹɬɶɫɹ ɜɵɛɪɚɧɧɚɹ ɮɭɧɤɰɢɹ. Ɉɛɵɱɧɨ ɜɵɱɢɫɥɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɡɚ- ɜɢɫɢɦɨɫɬɟɣ ɩɪɨɢɡɜɨɞɹɬ ɩɨɫɥɟ ɨɤɨɧɱɚɧɢɹ ɩɟɪɟɯɨɞɧɵɯ ɩɪɨɰɟɫɫɨɜ. ɂɫɩɨɥɶɡɨ- ɜɚɧɢɟ ɷɬɨɝɨ ɩɨɥɹ ɩɨɡɜɨɥɹɟɬ ɢɫɤɥɸɱɚɬɶ ɢɡ ɪɚɫɫɦɨɬɪɟɧɢɹ ɧɚɱɚɥɶɧɵɣ ɷɬɚɩ ɪɚɫ- ɱɟɬɨɜ, ɡɚɞɚɜ, ɧɚɩɪɢɦɟɪ, T>100ms. ȿɫɥɢ ɜ ɷɬɨɦ ɩɨɥɟ ɡɚɞɚɧɚ ɟɞɢɧɢɰɚ, ɬɨ ɮɭɧɤɰɢɹ ɜɵɱɢɫɥɹɟɬɫɹ ɜɫɟɝɞɚ.

ɉɨɥɟ N — ɰɟɥɨɟ ɱɢɫɥɨ, ɭɤɚɡɵɜɚɸɳɟɟ ɤɚɤɨɟ ɩɨ ɩɨɪɹɞɤɭ ɢɡɦɟɪɟɧɢɟ ɞɟɥɚ- ɟɬɫɹ ɜ ɞɚɧɧɵɣ ɦɨɦɟɧɬ. ɇɚɩɪɢɦɟɪ, ɧɟɨɛɯɨɞɢɦɨ ɢɡɦɟɪɢɬɶ ɞɥɢɬɟɥɶɧɨɫɬɶ ɮɪɨɧ- ɬɚ ɧɟɫɤɨɥɶɤɢɯ ɢɞɭɳɢɯ ɩɨɞɪɹɞ ɢɦɩɭɥɶɫɨɜ. Ɍɨɝɞɚ N=1 ɫɨɨɬɜɟɬɫɬɜɭɟɬ ɩɟɪɜɨɦɭ ɢɦɩɭɥɶɫɭ ɫɥɟɜɚ. ȼɟɥɢɱɢɧɚ N ɜ ɪɟɠɢɦɟ Cursor Mode ɭɜɟɥɢɱɢɜɚɟɬɫɹ ɧɚ 1 ɩɪɢ ɤɚɠɞɨɦ ɧɚɠɚɬɢɢ ɧɚ ɤɧɨɩɤɭ Go To.

Ɉɫɬɚɥɶɧɵɟ ɩɨɥɹ ɡɚɞɚɸɬ ɩɚɪɚɦɟɬɪɵ, ɯɚɪɚɤɬɟɪɧɵɟ ɞɥɹ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɤɨɧɤɪɟɬɧɨɣ ɮɭɧɤɰɢɢ. ɋɨɨɬɜɟɬɫɬɜɟɧɧɨ, ɨɧɢ ɦɟɧɹɸɬɫɹ ɩɪɢ ɜɵɛɨɪɟ ɩɨɡɢɰɢɢ ɪɚɫ- ɤɪɵɜɚɸɳɟɝɨɫɹ ɫɩɢɫɤɚ Function. ɇɚɡɧɚɱɟɧɢɟ ɷɬɢɯ ɩɚɪɚɦɟɬɪɨɜ ɞɥɹ ɪɚɡɥɢɱɧɵɯ ɮɭɧɤɰɢɣ Performance ɪɚɫɫɦɨɬɪɟɧɨ ɧɢɠɟ.

ȼ ɩɪɢɦɟɪɟ ɪɢɫ. 8.5 (ɫɯɟɦɚ Choke.cir ɢɡ ɤɚɬɚɥɨɝɚ Analysis\Scope) ɢɫɩɨɥɶ- ɡɭɸɬɫɹ ɫɥɟɞɭɸɳɢɟ ɩɚɪɚɦɟɬɪɵ:

ɉɨɥɟ ɏ Low ɡɚɞɚɟɬ ɧɢɠɧɟɟ ɝɪɚɧɢɱɧɨɟ ɡɧɚɱɟɧɢɟ ɧɟɡɚɜɢɫɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ 11 ɦɫ, ɢɫɩɨɥɶɡɭɟɦɨɟ ɮɭɧɤɰɢɟɣ Y_range.

ɉɨɥɟ ɏ High — ɜɟɪɯɧɟɟ ɝɪɚɧɢɱɧɨɟ ɡɧɚɱɟɧɢɟ ɧɟɡɚɜɢɫɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ 25 ɦɫ, ɢɫɩɨɥɶɡɭɟɦɨɟ ɮɭɧɤɰɢɟɣ Y_range.

ȼɵɱɢɫɥɟɧɢɟ ɮɭɧɤɰɢɢ ɩɪɨɢɡɜɨɞɢɬɫɹ ɩɨɫɥɟ ɧɚɠɚɬɢɹ ɤɧɨɩɤɢ Go To, ɚ ɪɟ- ɡɭɥɶɬɚɬ ɜɵɱɢɫɥɟɧɢɣ ɜɵɜɨɞɢɬɫɹ ɧɟɩɨɫɪɟɞɫɬɜɟɧɧɨ ɜ ɩɨɥɟ ɞɢɚɥɨɝɨɜɨɝɨ ɨɤɧɚ. ȼ ɩɪɢɜɟɞɟɧɧɨɦ ɧɚ ɪɢɫ. 8.5. ɩɪɢɦɟɪɟ ɷɬɨɬ ɪɟɡɭɥɶɬɚɬ (ɦɚɤɫɢɦɚɥɶɧɵɣ ɩɟɪɟɩɚɞ ɧɚɩɪɹɠɟɧɢɹ ɜ ɭɡɥɟ ɜ ɞɢɚɩɚɡɨɧɟ ɜɪɟɦɟɧɢ ɨɬ 11 ɦɫ ɞɨ 25 ɦɫ) — Y_Range=70.5591. Ʉɪɨɦɟ ɬɨɝɨ, ɜ ɪɚɫɫɦɨɬɪɟɧɧɨɦ ɩɪɢɦɟɪɟ ɩɨɫɥɟ ɧɚɠɚɬɢɹ ɤɧɨɩɤɢ Go To ɥɟɜɵɣ ɢ ɩɪɚɜɵɣ ɤɭɪɫɨɪ ɭɫɬɚɧɨɜɢɥɢɫɶ ɜ ɩɨɡɢɰɢɢ, ɫɨɨɬɜɟɬɫɬɜɭɸ- ɳɢɟ ɜɵɩɨɥɧɹɟɦɨɦɭ ɢɡɦɟɪɟɧɢɸ — ɜ ɜɵɫɲɭɸ ɢ ɧɢɡɲɭɸ ɬɨɱɤɭ ɝɪɚɮɢɤɚ ɜ ɪɚɫ- ɫɦɚɬɪɢɜɚɟɦɨɦ ɜɪɟɦɟɧɧɨɦ ɞɢɚɩɚɡɨɧɟ (X low, X high).

8.4.2. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɮɭɧɤɰɢɣ Performance ɢ ɩɨɫɬɪɨɟɧɢɟ ɢɯ ɝɪɚɮɢɤɨɜ

ɇɢɠɟ ɛɭɞɭɬ ɩɟɪɟɱɢɫɥɟɧɵ ɜɫɟ ɜɨɡɦɨɠɧɵɟ ɫɥɭɱɚɢ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɮɭɧɤɰɢɣ ɪɚɡɞɟɥɚ Performance.

ɉɨɫɬɪɨɟɧɢɟ ɝɪɚɮɢɤɨɜ ɮɭɧɤɰɢɣ Performance ɜɨɡɦɨɠɧɨ ɬɨɥɶɤɨ ɩɨɫɥɟ ɩɪɨ- ɜɟɞɟɧɢɟ ɦɧɨɝɨɜɚɪɢɚɧɬɧɨɝɨ ɚɧɚɥɢɡɚ (Stepping ɢɥɢ Monte Carlo). Ⱦɥɹ ɦɧɨɝɨ- ɜɚɪɢɚɧɬɧɨɝɨ ɚɧɚɥɢɡɚ Stepping ɦɨɠɧɨ ɩɨɫɬɪɨɢɬɶ ɞɜɭɦɟɪɧɵɟ (ɪɢɫ. 8.6 ɢ ɫɯɟɦ- ɧɵɣ ɮɚɣɥ Perf1.cir) ɢ ɬɪɟɯɦɟɪɧɵɟ ɝɪɚɮɢɤɢ ɮɭɧɤɰɢɣ Performance (ɫɯɟɦɧɵɣ ɮɚɣɥ Perf1_02.cir) ɜ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɤɨɥɢɱɟɫɬɜɚ ɜɥɨɠɟɧɢɣ ɜɚɪɶɢɪɭɟɦɵɯ ɩɚ- ɪɚɦɟɬɪɨɜ.

ɉɪɟɞɩɨɥɨɠɢɦ, ɱɬɨ ɚɧɚɥɢɡ ɩɟɪɟɯɨɞɧɵɯ ɩɪɨɰɟɫɫɨɜ ɜ ɤɨɥɟɛɚɬɟɥɶɧɨɦ ɤɨɧɬɭ- ɪɟ (ɪɢɫ. 8.6) ɩɪɨɜɨɞɢɥɫɹ ɩɪɢ ɢɡɦɟɧɟɧɢɢ ɟɦɤɨɫɬɢ ɤɨɧɞɟɧɫɚɬɨɪɚ C1 ɨɬ 100ɩɎ ɞɨ 4600ɩɎ ɫ ɲɚɝɨɦ 500ɩɎ. Ɇɧɨɝɨɜɚɪɢɚɧɬɧɵɣ ɚɧɚɥɢɡ ɨɪɝɚɧɢɡɨɜɚɧ ɫ ɩɨɦɨɳɶɸ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɯ ɭɫɬɚɧɨɜɨɤ ɨɤɧɚ Stepping.

1.Ⱦɥɹ ɩɨɫɬɪɨɟɧɢɹ ɝɪɚɮɢɤɨɜ ɦɨɠɟɬ ɛɵɬɶ ɢɫɩɨɥɶɡɨɜɚɧɚ ɤɨɦɚɧɞɚ ɦɟɧɸ ɫɨ- ɨɬɜɟɬɫɬɜɭɸɳɟɝɨ ɚɧɚɥɢɡɚ Add Performance Window, ɚ ɡɚɬɟɦ ɜ ɪɚɡɞɟɥɟ Plot ɨɤɧɚ Properties (F10) ɢɡ ɫɩɢɫɤɚ, ɜɵɡɵɜɚɟɦɨɝɨ ɧɚɠɚɬɢɟɦ ɤɧɨɩɤɢ GET, ɜɵɛɢɪɚ- ɟɬɫɹ ɧɟɨɛɯɨɞɢɦɚɹ ɮɭɧɤɰɢɹ (ɪɢɫ. 8.6). ȼ ɪɚɫɫɦɚɬɪɢɜɚɟɦɨɦ ɩɪɢɦɟɪɟ ɫɬɪɨɢɬɫɹ ɡɚɜɢɫɢɦɨɫɬɶ ɜɪɟɦɟɧɢ ɩɟɪɟɞɧɟɝɨ ɮɪɨɧɬɚ (Rise_Time) ɨɬ ɟɦɤɨɫɬɢ C1.

2.ȼ ɪɟɠɢɦɟ ɫɬɚɬɢɫɬɢɱɟɫɤɨɝɨ ɚɧɚɥɢɡɚ Ɇɨɧɬɟ-Ʉɚɪɥɨ ɫɬɪɨɢɬɫɹ ɝɢɫɬɨɝɪɚɦɦɚ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɮɭɧɤɰɢɢ Performance ɩɨ ɢɧɬɟɪɜɚɥɚɦ, ɧɚɝɥɹɞɧɨ ɩɨɤɚɡɵɜɚɸ-

ɳɚɹ ɟɟ ɩɨɜɟɞɟɧɢɟ ɩɪɢ ɫɬɚɬɢɫɬɢɱɟɫɤɨɦ ɪɚɡɛɪɨɫɟ ɩɚɪɚɦɟɬɪɨɜ ɤɨɦɩɨɧɟɧɬɨɜ ɫɯɟɦɵ. ɉɪɢ ɷɬɨɦ ɢɫɩɨɥɶɡɭɟɬɫɹ ɤɨɦɚɧɞɚ Monte Carlo>Histograms>Add Histogram, ɚ ɜ ɪɚɡɞɟɥɟ Plot ɨɤɧɚ Properties (F10) ɢɡ ɫɩɢɫɤɚ, ɜɵɡɵɜɚɟɦɨɝɨ ɧɚ-

ɠɚɬɢɟɦ ɤɧɨɩɤɢ GET, ɜɵɛɢɪɚɟɬɫɹ ɧɟɨɛɯɨɞɢɦɚɹ ɮɭɧɤɰɢɹ (ɫɦ. ɪɢɫ. 7.7). Ʉɪɨɦɟ ɬɨɝɨ, ɮɭɧɤɰɢɹ Performance ɢɫɩɨɥɶɡɭɟɬɫɹ ɞɥɹ ɜɵɹɜɥɟɧɢɹ ɜɚɪɢɚɧɬɚ ɨɬɤɚɡɚ ɫɯɟɦɵ ɩɪɢ ɫɬɚɬɢɫɬɢɱɟɫɤɨɦ ɚɧɚɥɢɡɟ ɜ ɫɬɪɨɤɟ Report When ɨɤɧɚ Monte Carlo

Options (ɫɦ. ɪɢɫ. 7.5).

Ɋɢɫ. 8.6. Ɂɚɞɚɧɢɟ ɮɭɧɤɰɢɣ Performance ɜ ɨɤɧɟ Performance Window

3.ɉɪɢ ɩɪɨɜɟɞɟɧɢɢ ɨɩɬɢɦɢɡɚɰɢɢ ɢɡ ɫɩɢɫɤɚ, ɜɵɡɵɜɚɟɦɨɝɨ ɜ ɨɤɧɟ Optimize ɧɚɠɚɬɢɟɦ ɤɧɨɩɤɢ Get (ɫɦ. ɪɢɫ. 7.8, 7.10), ɜɵɛɢɪɚɟɬɫɹ ɨɩɬɢɦɢɡɢɪɭɟɦɚɹ ɮɭɧɤ- ɰɢɹ ɢɡ ɝɪɭɩɩɵ Performance.

4.ɂ, ɧɚɤɨɧɟɰ, ɜ ɩɨɫɥɟɞɧɢɯ ɜɟɪɫɢɹɯ ɩɪɨɝɪɚɦɦɵ (MC9, MC10) ɮɭɧɤɰɢɢ Performance ɢɫɩɨɥɶɡɭɸɬɫɹ ɜ ɞɢɧɚɦɢɱɟɫɤɢɯ ɪɚɡɦɟɪɧɵɯ ɥɢɧɢɹɯ (performance tag),

ɜɵɡɵɜɚɟɦɵɯ ɤɨɦɚɧɞɨɣ Options>Mode>Performance Tag ɢɥɢ . Ⱦɢɧɚɦɢɱɟ-

ɫɤɚɹ ɪɚɡɦɟɪɧɚɹ ɥɢɧɢɹ ɨɫɭɳɟɫɬɜɥɹɟɬ ɧɨɜɨɟ ɢɡɦɟɪɟɧɢɟ ɞɥɹ ɤɚɠɞɨɝɨ ɧɨɜɨɝɨ ɡɚɩɭɫɤɚ ɚɧɚɥɢɡɚ (ɜ ɬɨɦ ɱɢɫɥɟ ɢ ɩɪɢ ɜɚɪɢɚɰɢɢ ɩɚɪɚɦɟɬɪɨɜ, ɬɟɦɩɟɪɚɬɭɪɵ, ɫɬɚ- ɬɢɫɬɢɱɟɫɤɨɦ ɚɧɚɥɢɡɟ Monte Carlo) ɢ ɩɪɢ ɜɵɛɨɪɟ ɜɚɪɢɚɧɬɚ ɩɨɤɚɡɵɜɚɟɬ ɫɨɨɬ- ɜɟɬɫɬɜɭɸɳɢɟ ɪɟɡɭɥɶɬɚɬɵ ɢɡɦɟɪɟɧɢɹ.

Ⱦɨɩɭɫɬɢɦ ɜ ɪɚɫɫɦɚɬɪɢɜɚɟɦɨɦ ɫɯɟɦɧɨɦ ɩɪɢɦɟɪɟ (ɪɢɫ. 8.6) ɫɬɚɜɢɬɫɹ ɡɚɞɚ- ɱɚ ɢɡɦɟɪɟɧɢɹ ɩɟɪɟɞɧɟɝɨ ɮɪɨɧɬɚ ɢɦɩɭɥɶɫɚ (ɜɪɟɦɟɧɢ ɧɚɪɚɫɬɚɧɢɹ ɨɬ 0.1 ɞɨ 0.9 ɭɫɬɚɧɨɜɢɜɲɟɝɨɫɹ ɡɧɚɱɟɧɢɹ). Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɧɟɨɛɯɨɞɢɦɨ ɢɡɦɟɪɢɬɶ ɜɪɟɦɹ ɧɚ- ɪɚɫɬɚɧɢɹ ɧɚɩɪɹɠɟɧɢɹ V(out) ɨɬ 0.5 ȼ ɞɨ 4.5 ȼ. ɋɧɚɱɚɥɚ ɜɵɛɢɪɚɟɬɫɹ ɪɟɠɢɦ

ɧɚɧɟɫɟɧɢɹ ɞɢɧɚɦɢɱɟɫɤɨɣ ɪɚɡɦɟɪɧɨɣ ɥɢɧɢɢ . Ɂɚɬɟɦ, ɩɨɫɥɟ ɤɥɢɤɚ ɦɵɲɶɸ ɧɚ ɩɨɥɟ ɝɪɚɮɢɤɚ ɜ ɨɬɤɪɵɜɲɟɦɫɹ ɞɢɚɥɨɝɨɜɨɦ ɨɤɧɟ ɜɵɛɢɪɚɟɬɫɹ ɧɭɠɧɚɹ ɮɭɧɤ-

ɰɢɹ Performance, ɜ ɞɚɧɧɨɦ ɫɥɭɱɚɟ Rise_Time(V(OUT),1,1,0.5,4.5) (ɪɢɫ. 8.7).

Ɍɚɤɠɟ ɜ ɷɬɨɦ ɞɢɚɥɨɝɨɜɨɦ ɨɤɧɟ ɡɚɞɚɟɬɫɹ ɝɪɚɮɢɱɟɫɤɨɟ ɢ ɲɪɢɮɬɨɜɨɟ ɨɮɨɪɦɥɟ- ɧɢɟ ɪɚɡɦɟɪɧɨɣ ɥɢɧɢɢ.

ɉɪɢ ɜɵɛɨɪɟ ɜ ɪɟɠɢɦɟ Cursor Mode (F8) ɝɪɚɮɢɤɚ ɜɚɪɢɚɧɬɚ ɚɧɚɥɢɡɚ (ɧɚ-

ɩɪɢɦɟɪ ɩɟɪɟɦɟɳɟɧɢɟɦ ɫɬɪɟɥɤɚɦɢ pn ɧɚ ɞɨɩɨɥɧɢɬɟɥɶɧɨɣ ɤɥɚɜɢɚɬɭɪɟ), ɪɚɡ-

ɦɟɪɧɚɹ ɥɢɧɢɹ ɛɭɞɟɬ ɢɡɦɟɧɹɬɶ ɫɜɨɟ ɩɨɥɨɠɟɧɢɟ ɢ ɩɨɤɚɡɚɧɢɹ ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɩɚɪɚɦɟɬɪɚɦɢ ɫɢɝɧɚɥɚ ɜɵɛɪɚɧɧɨɝɨ ɜɚɪɢɚɧɬɚ. Ɍɚɤ ɜ ɪɚɫɫɦɚɬɪɢɜɚɟɦɨɦ ɩɪɢɦɟɪɟ (ɪɢɫ. 8.7) ɜɵɛɪɚɧ ɜɚɪɢɚɧɬ ɚɧɚɥɢɡɚ ɩɪɢ ɋ1=4.6n, ɞɢɧɚɦɢɱɟɫɤɚɹ ɪɚɡɦɟɪɧɚɹ ɥɢ- ɧɢɹ Performance-ɮɭɧɤɰɢɢ ɩɨɤɚɡɵɜɚɟɬ ɞɥɢɬɟɥɶɧɨɫɬɶ ɮɪɨɧɬɚ ɜɵɯɨɞɧɨɝɨ ɫɢɝɧɚ- ɥɚ ɞɥɹ ɷɬɨɝɨ ɜɚɪɢɚɧɬɚ.

Ɋɢɫ. 8.7. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɞɢɧɚɦɢɱɟɫɤɢɯ ɪɚɡɦɟɪɧɵɯ ɥɢɧɢɣ ɞɥɹ Performance-ɮɭɧɤɰɢɣ

ȼ ɫɯɟɦɧɨɦ ɮɚɣɥɟ Perf_Tag.cir ɤɚɬɚɥɨɝɚ Analysis\Performance ɞɢɧɚɦɢɱɟ-

ɫɤɢɟ ɬɷɝɢ ɢɫɩɨɥɶɡɭɸɬɫɹ ɞɥɹ ɢɡɦɟɪɟɧɢɹ ɜɪɟɦɟɧɢ ɜɤɥɸɱɟɧɢɹ ɢ ɡɚɞɟɪɠɤɢ ɜɵ- ɤɥɸɱɟɧɢɹ (ɜɪɟɦɟɧɢ ɪɚɫɫɚɫɵɜɚɧɢɹ) ɤɥɸɱɚ ɧɚ ɛɢɩɨɥɹɪɧɨɦ ɬɪɚɧɡɢɫɬɨɪɟ ɩɪɢ ɢɡɦɟɧɟɧɢɢ ɩɚɪɚɦɟɬɪɨɜ ɫɯɟɦɵ.

8.4.3. Ɏɭɧɤɰɢɢ Performance

ɇɢɠɟ ɛɭɞɟɬ ɩɪɢɜɟɞɟɧ ɩɟɪɟɱɟɧɶ ɮɭɧɤɰɢɣ Performance ɫ ɚɪɝɭɦɟɧɬɚɦɢ ɢ ɪɟɡɭɥɶɬɚɬ ɢɯ ɜɵɩɨɥɧɟɧɢɹ. ɉɪɢ ɷɬɨɦ ɩɪɢɧɹɬɵ ɫɥɟɞɭɸɳɢɟ ɨɛɨɡɧɚɱɟɧɢɹ.

Y_Expr — ɜɵɪɚɠɟɧɢɟ ɞɥɹ ɩɟɪɟɦɟɧɧɨɣ, ɨɬɤɥɚɞɵɜɚɟɦɨɣ ɩɨ ɨɫɢ ɨɪɞɢɧɚɬ, ɞɥɹ ɤɨɬɨɪɨɝɨ ɧɟɨɛɯɨɞɢɦɨ ɜɵɩɨɥɧɢɬɶ ɮɭɧɤɰɢɸ ɝɪɭɩɩɵ Performance.

Boolean_Expr — ɥɨɝɢɱɟɫɤɨɟ ɜɵɪɚɠɟɧɢɟ, ɩɪɢ ɢɫɬɢɧɧɨɫɬɢ ɤɨɬɨɪɨɝɨ ɛɭɞɟɬ ɜɵɱɢɫɥɹɬɶɫɹ ɜɵɛɪɚɧɧɚɹ ɮɭɧɤɰɢɹ. Ɉɛɵɱɧɨ ɜɵɱɢɫɥɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɡɚɜɢɫɢɦɨ- ɫɬɟɣ ɩɪɨɢɡɜɨɞɹɬ ɩɨɫɥɟ ɨɤɨɧɱɚɧɢɹ ɩɟɪɟɯɨɞɧɵɯ ɩɪɨɰɟɫɫɨɜ. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɷɬɨ- ɝɨ ɩɨɥɹ ɩɨɡɜɨɥɹɟɬ ɢɫɤɥɸɱɢɬɶ ɢɡ ɪɚɫɫɦɨɬɪɟɧɢɹ ɧɚɱɚɥɶɧɵɣ ɷɬɚɩ ɪɚɫɱɟɬɨɜ, ɡɚ- ɞɚɜ, ɧɚɩɪɢɦɟɪ, «T>100ns». ȿɫɥɢ ɜ ɷɬɨɦ ɩɨɥɟ ɡɚɞɚɧɚ 1, ɬɨ ɮɭɧɤɰɢɹ ɜɵɱɢɫɥɹ- ɟɬɫɹ ɜɫɟɝɞɚ.

N — ɰɟɥɨɟ ɱɢɫɥɨ, ɭɤɚɡɵɜɚɸɳɟɟ ɤɚɤɨɟ ɩɨ ɩɨɪɹɞɤɭ ɢɡɦɟɪɟɧɢɟ ɞɟɥɚɟɬɫɹ ɜ ɧɚɫɬɨɹɳɢɣ ɦɨɦɟɧɬ. ɇɚɩɪɢɦɟɪ, ɟɫɥɢ ɧɟɨɛɯɨɞɢɦɨ ɢɡɦɟɪɢɬɶ ɞɥɢɬɟɥɶɧɨɫɬɶ ɮɪɨɧɬɚ ɧɟɫɤɨɥɶɤɢɯ ɢɞɭɳɢɯ ɩɨɞɪɹɞ ɢɦɩɭɥɶɫɨɜ, ɬɨ N=1 ɫɨɨɬɜɟɬɫɬɜɭɟɬ ɩɟɪɜɨɦɭ ɢɦɩɭɥɶɫɭ ɫɥɟɜɚ. ȼɟɥɢɱɢɧɚ N ɜ ɪɟɠɢɦɟ Cursor Mode ɭɜɟɥɢɱɢɜɚɟɬɫɹ ɧɚ 1 ɩɪɢ ɤɚɠɞɨɦ ɧɚɠɚɬɢɢ ɧɚ ɤɧɨɩɤɢ GO TO, Left, Right.

Low — ɧɢɠɧɟɟ ɝɪɚɧɢɱɧɨɟ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ, ɢɫɩɨɥɶɡɭɟɦɨɟ ɫɨɨɬɜɟɬ- ɫɬɜɭɸɳɢɦɢ ɮɭɧɤɰɢɹɦɢ.

High — ɜɟɪɯɧɟɟ ɝɪɚɧɢɱɧɨɟ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ, ɢɫɩɨɥɶɡɭɟɦɨɟ ɫɨɨɬɜɟɬ- ɫɬɜɭɸɳɢɦɢ ɮɭɧɤɰɢɹɦɢ.

Level — ɭɪɨɜɟɧɶ ɡɧɚɱɟɧɢɹ ɩɟɪɟɦɟɧɧɨɣ, ɢɫɩɨɥɶɡɭɟɦɵɣ ɩɪɢ ɜɵɱɢɫɥɟɧɢɢ ɪɚɡɥɢɱɧɵɯ ɩɚɪɚɦɟɬɪɨɜ ɫɢɝɧɚɥɨɜ.

ɋɩɢɫɨɤ ɮɭɧɤɰɢɣ Performance (ɜ ɚɥɮɚɜɢɬɧɨɦ ɩɨɪɹɞɤɟ)

Average(Y_expr,Boolean_expr,XMin,XMax) — ɧɚɯɨɞɢɬ ɫɪɟɞɧɟɟ ɡɧɚɱɟɧɢɟ ɮɭɧɤɰɢɢ Y_expr ɧɚ ɢɧɬɟɪɜɚɥɟ ɢɡɦɟɧɟɧɢɹ ɧɟɡɚɜɢɫɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ ɨɬ Xmin ɞɨ Xmax (ɩɨ ɭɦɨɥɱɚɧɢɸ ɭɫɬɚɧɚɜɥɢɜɚɟɬɫɹ ɢɧɬɟɪɜɚɥ ɨɬ Tmin (Tstart ɜ MC10) ɞɨ

Tmax).

Fall_Time(Y_expr,Boolean_expr,N,low,high) — ɞɥɢɬɟɥɶɧɨɫɬɶ ɭɛɵɜɚɧɢɹ ɜɞɨɥɶ ɨɫɢ X ɩɟɪɟɦɟɧɧɨɣ Y ɨɬ ɭɤɚɡɚɧɧɨɝɨ ɜɟɪɯɧɟɝɨ (High) ɞɨ ɭɤɚɡɚɧɧɨɝɨ ɧɢɠ- ɧɟɝɨ (Low) ɭɪɨɜɧɟɣ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɡɚɞɚɧɧɨɝɨ ɥɨɝɢɱɟɫɤɨɝɨ ɜɵɪɚɠɟɧɢɹ Boo-

lean_expr.

Frequency(Y_expr,Boolean_expr,N) — ɞɨɩɨɥɧɟɧɢɟ ɮɭɧɤɰɢɢ Period. Ɋɚ-

ɛɨɬɚɟɬ ɬɚɤɠɟ, ɤɚɤ ɢ ɮɭɧɤɰɢɹ Period, ɧɨ ɜɵɱɢɫɥɹɟɬ ɡɧɚɱɟɧɢɟ 1/Period. Gain_Margin (ɬɨɥɶɤɨ ɜ MC10) — ɜɵɱɢɫɥɹɟɬ ɡɚɩɚɫ ɩɨ ɚɦɩɥɢɬɭɞɟ ɜ ɱɚɫɬɨɬ-

ɧɨɦ ɚɧɚɥɢɡɟ. Ɂɚɪɚɧɟɟ ɞɨɥɠɧɵ ɛɵɬɶ ɩɨɫɬɪɨɟɧɵ ɝɪɚɮɢɤɢ ɑɏ dB(expr) ɢ Ɏɑɏ PHASE(expr). Ɇɨɠɟɬ ɵɬɶ ɩɪɢɦɟɧɟɧɚ ɬɨɥɶɤɨ ɤ ɪɟɡɭɥɶɬɚɬɚɦ AC-ɚɧɚɥɢɡɚ.

High_X(Y_expr,Boolean_expr) — ɨɩɪɟɞɟɥɹɟɬ ɤɨɨɪɞɢɧɚɬɭ X ɬɨɱɤɢ ɝɥɨ- ɛɚɥɶɧɨɝɨ ɦɚɤɫɢɦɭɦɚ ɮɭɧɤɰɢɢ Y_expr. ȼ ɪɟɠɢɦɟ Cursor Mode ɜ ɧɚɣɞɟɧɧɭɸ ɬɨɱɤɭ ɞɨɩɨɥɧɢɬɟɥɶɧɨ ɩɨɦɟɳɚɟɬɫɹ ɜɵɛɪɚɧɧɵɣ ɥɟɜɵɣ (ɢɥɢ ɩɪɚɜɵɣ) ɤɭɪɫɨɪ ɢ ɜɵɱɢɫɥɹɟɬɫɹ ɟɟ ɤɨɨɪɞɢɧɚɬɚ ɩɨ ɨɫɢ X.

High_Y(Y_expr,Boolean_expr) — ɨɩɪɟɞɟɥɹɟɬ ɤɨɨɪɞɢɧɚɬɭ Y ɬɨɱɤɢ ɝɥɨ- ɛɚɥɶɧɨɝɨ ɦɚɤɫɢɦɭɦɚ ɮɭɧɤɰɢɢ Y_expr. ȼ ɪɟɠɢɦɟ Cursor Mode ɞɨɩɨɥɧɢɬɟɥɶɧɨ ɩɨɦɟɳɚɟɬɫɹ ɜɵɛɪɚɧɧɵɣ ɥɟɜɵɣ (ɢɥɢ ɩɪɚɜɵɣ) ɤɭɪɫɨɪ ɜ ɧɚɣɞɟɧɧɭɸ ɬɨɱɤɭ ɢ ɜɵ- ɱɢɫɥɹɟɬɫɹ ɟɟ ɤɨɨɪɞɢɧɚɬɚ ɩɨ ɨɫɢ Y.

Low_X(Y_expr,Boolean_expr) — ɨɩɪɟɞɟɥɹɟɬ ɤɨɨɪɞɢɧɚɬɭ X ɬɨɱɤɢ ɝɥɨ- ɛɚɥɶɧɨɝɨ ɦɢɧɢɦɭɦɚ ɮɭɧɤɰɢɢ Y_expr. ȼ ɪɟɠɢɦɟ Cursor Mode ɞɨɩɨɥɧɢɬɟɥɶɧɨ ɩɨɦɟɳɚɟɬɫɹ ɜɵɛɪɚɧɧɵɣ ɥɟɜɵɣ (ɢɥɢ ɩɪɚɜɵɣ) ɤɭɪɫɨɪ ɜ ɧɚɣɞɟɧɧɭɸ ɬɨɱɤɭ ɢ ɜɵ- ɱɢɫɥɹɟɬɫɹ ɟɟ ɤɨɨɪɞɢɧɚɬɚ ɩɨ ɨɫɢ X.

Low_Y(Y_expr,Boolean_expr) — ɨɩɪɟɞɟɥɹɟɬ ɤɨɨɪɞɢɧɚɬɭ Y ɬɨɱɤɢ ɝɥɨ- ɛɚɥɶɧɨɝɨ ɦɢɧɢɦɭɦɚ ɮɭɧɤɰɢɢ Y_expr. ȼ ɪɟɠɢɦɟ Cursor Mode ɞɨɩɨɥɧɢɬɟɥɶɧɨ ɩɨɦɟɳɚɟɬɫɹ ɜɵɛɪɚɧɧɵɣ ɥɟɜɵɣ (ɢɥɢ ɩɪɚɜɵɣ) ɤɭɪɫɨɪ ɜ ɧɚɣɞɟɧɧɭɸ ɬɨɱɤɭ ɢ ɜɵ- ɱɢɫɥɹɟɬɫɹ ɟɟ ɤɨɨɪɞɢɧɚɬɚ ɩɨ ɨɫɢ Y.

Peak_Valley(Y_expr,Boolean_expr,N) — ɜɨɡɜɪɚɳɚɟɬ ɪɚɡɧɨɫɬɶ ɤɨɨɪɞɢɧɚɬ

Y 2-ɯ ɫɨɫɟɞɧɢɯ ɬɨɱɟɤ ɥɨɤɚɥɶɧɨɝɨ ɦɚɤɫɢɦɭɦɚ ɢ ɦɢɧɢɦɭɦɚ ɜɵɛɪɚɧɧɨɣ ɩɟɪɟ- ɦɟɧɧɨɣ Y_expr. ȼ ɪɟɠɢɦɟ Cursor Mode ɞɨɩɨɥɧɢɬɟɥɶɧɨ ɩɨɦɟɳɚɸɬɫɹ ɥɟɜɵɣ ɢ ɩɪɚɜɵɣ ɤɭɪɫɨɪɵ ɜ ɨɱɟɪɟɞɧɵɟ ɧɚɣɞɟɧɧɵɟ 2 ɬɨɱɤɢ ɦɚɤɫɢɦɭɦɚ ɢ ɦɢɧɢɦɭɦɚ. Ɇɨɠɟɬ ɢɫɩɨɥɶɡɨɜɚɬɶɫɹ ɞɥɹ ɢɡɦɟɪɟɧɢɹ ɪɚɡɦɚɯɚ ɪɚɡɧɨɨɛɪɚɡɧɵɯ ɩɭɥɶɫɚɰɢɣ, ɜɵɛɪɨɫɨɜ ɢ ɭɞɜɨɟɧɧɵɯ ɚɦɩɥɢɬɭɞ ɩɟɪɢɨɞɢɱɟɫɤɢɯ ɫɢɝɧɚɥɨɜ.

Peak_X(Y_expr,Boolean_expr,N) — ɮɭɧɤɰɢɹ ɜɵɱɢɫɥɹɟɬ ɤɨɨɪɞɢɧɚɬɭ X

ɨɱɟɪɟɞɧɨɝɨ ɥɨɤɚɥɶɧɨɝɨ ɦɚɤɫɢɦɭɦɚ (PEAK) ɜɵɛɪɚɧɧɨɣ ɩɟɪɟɦɟɧɧɨɣ Y_expr. Ʌɨɤɚɥɶɧɵɣ ɦɚɤɫɢɦɭɦ — ɷɬɨ ɬɨɱɤɚ, ɡɧɚɱɟɧɢɟ ɮɭɧɤɰɢɢ Y ɜ ɤɨɬɨɪɨɣ ɛɨɥɶɲɟ ɱɟɦ ɜ ɫɨɫɟɞɧɢɯ ɬɨɱɤɚɯ ɫ ɨɛɟɢɯ ɫɬɨɪɨɧ. ȼ ɪɟɠɢɦɟ Cursor Mode ɩɪɢ ɷɬɨɦ ɞɨɩɨɥɧɢ- ɬɟɥɶɧɨ ɩɨɦɟɳɚɟɬɫɹ ɥɟɜɵɣ ɢɥɢ ɩɪɚɜɵɣ ɤɭɪɫɨɪ ɜ ɨɱɟɪɟɞɧɨɣ ɥɨɤɚɥɶɧɵɣ ɦɚɤɫɢ- ɦɭɦ.

Peak_Y(Y_expr,Boolean_expr,N) — ɮɭɧɤɰɢɹ ɚɧɚɥɨɝɢɱɧɚ ɮɭɧɤɰɢɢ

Peak_X, ɧɨ ɜɵɱɢɫɥɹɟɬ ɡɧɚɱɟɧɢɟ ɤɨɨɪɞɢɧɚɬɵ Y ɬɨɱɤɢ ɥɨɤɚɥɶɧɨɝɨ ɦɚɤɫɢɦɭɦɚ. Ɏɭɧɤɰɢɹ ɦɨɠɟɬ ɢɫɩɨɥɶɡɨɜɚɬɶɫɹ ɞɥɹ ɢɡɦɟɪɟɧɢɹ ɡɧɚɱɟɧɢɣ ɜɵɛɪɨɫɨɜ ɩɪɢ ɚɧɚ-