М.А. Амелина - Конспект лекций по курсу - Компьютерный анализ и синтез электронных устройств, страница 28
Описание файла
PDF-файл из архива "М.А. Амелина - Конспект лекций по курсу - Компьютерный анализ и синтез электронных устройств", который расположен в категории "". Всё это находится в предмете "схемотехника" из 5 семестр, которые можно найти в файловом архиве НГТУ. Не смотря на прямую связь этого архива с НГТУ, его также можно найти и в других разделах. .
Просмотр PDF-файла онлайн
Текст 28 страницы из PDF
ɬɚɛɥ. 4.2).ɉɟɪɟɯɨɞ ɤ ɭɤɚɡɚɧɧɨɣ ɪɟɚɥɢɡɚɰɢɢ ɦɧɨɝɨɜɚɪɢɚɧɬɧɨɝɨ ɚɧɚɥɢɡɚ.ɇɚɧɟɫɟɧɢɟ ɧɚ ɝɪɚɮɢɤ ɡɧɚɱɟɧɢɣ ɤɨɨɪɞɢɧɚɬ ɥɟɜɨɝɨ ɤɭɪɫɨɪɚɇɚɧɟɫɟɧɢɟ ɧɚ ɝɪɚɮɢɤ ɡɧɚɱɟɧɢɣ ɤɨɨɪɞɢɧɚɬ ɩɪɚɜɨɝɨ ɤɭɪɫɨɪɚ129D:\Ɉɩɢɫɚɧɢɟ MC8\MC8_V1_2.DOCTag Horizontal(Shift+Ctrl+H)Tag Vertical(Shift+Ctrl+V)Align CursorsKeep Cursorson Same BranchSame Y ScalesThumb Nail Plotɇɚɧɟɫɟɧɢɟ ɧɚ ɝɪɚɮɢɤ ɪɚɡɦɟɪɧɵɯ ɥɢɧɢɣ ɦɟɠɞɭ ɬɨɱɤɚɦɢ ɝɪɚɮɢɤɚ, ɨɬɦɟɱɟɧɧɵɦɢɥɟɜɵɦ ɢ ɩɪɚɜɵɦ ɤɭɪɫɨɪɨɦ ɢ ɩɪɨɫɬɚɧɨɜɤɚ ɪɚɫɫɬɨɹɧɢɹ ɦɟɠɞɭ ɧɢɦɢ ɩɨ ɝɨɪɢɡɨɧɬɚɥɢɇɚɧɟɫɟɧɢɟ ɧɚ ɝɪɚɮɢɤ ɪɚɡɦɟɪɧɵɯ ɥɢɧɢɣ ɦɟɠɞɭ ɬɨɱɤɚɦɢ ɝɪɚɮɢɤɚ, ɨɬɦɟɱɟɧɧɵɦɢɥɟɜɵɦ ɢ ɩɪɚɜɵɦ ɤɭɪɫɨɪɨɦ ɢ ɩɪɨɫɬɚɧɨɜɤɚ ɪɚɫɫɬɨɹɧɢɹ ɦɟɠɞɭ ɧɢɦɢ ɩɨ ɜɟɪɬɢɤɚɥɢ.ɋɢɧɯɪɨɧɧɨɟ ɩɟɪɟɦɟɳɟɧɢɟ ɤɭɪɫɨɪɚ ɢ ɫɱɢɬɵɜɚɧɢɟ ɤɨɨɪɞɢɧɚɬ ɜɫɟɯ ɝɪɚɮɢɤɨɜ,ɪɚɫɩɨɥɨɠɟɧɧɵɯ ɜɨ ɜɫɟɯ ɝɪɚɮɢɱɟɫɤɢɯ ɨɤɧɚɯɉɨɞɞɟɪɠɚɧɢɟ ɩɟɪɟɦɟɳɟɧɢɹ ɥɟɜɨɝɨ ɢ ɩɪɚɜɨɝɨ ɤɭɪɫɨɪɨɜ ɩɨ ɨɞɧɨɦɭ ɢ ɬɨɦɭ ɠɟɝɪɚɮɢɤɭ ɪɟɡɭɥɶɬɚɬɨɜ ɦɧɨɝɨɜɚɪɢɚɧɬɧɨɝɨ ɚɧɚɥɢɡɚɉɟɪɟɫɬɪɨɟɧɢɟ ɜɫɟɯ ɝɪɚɮɢɤɨɜ ɬɚɤ, ɱɬɨɛɵ ɨɧɢ ɢɦɟɥɢ ɨɛɳɭɸ ɨɫɶ Y (ɢɫɩɨɥɶɡɭɟɬɫɹ ɜɬɨɦ ɫɥɭɱɚɟ, ɟɫɥɢ ɝɪɚɮɢɤɢ ɫɬɪɨɹɬɫɹ ɜ ɨɞɧɨɦ ɨɤɧɟ, ɧɨ ɜ ɪɚɡɧɵɯ ɦɚɫɲɬɚɛɚɯ)ɂɡɨɛɪɚɠɟɧɢɟ ɬɟɤɭɳɢɯ ɝɪɚɮɢɤɨɜ ɜ ɨɬɞɟɥɶɧɨɦ ɨɤɧɟ ɜ ɦɟɥɤɨɦ ɦɚɫɲɬɚɛɟ4.10.5.ɏɮɨɥɱɣɣ ɫɛɢɟɠɦɛ PERFORMANCEMicroCAP ɢɦɟɟɬ ɝɪɭɩɩɭ ɫɩɟɰɢɚɥɶɧɵɯ ɮɭɧɤɰɢɣ PERFORMANCE, ɤɨɬɨɪɵɟ ɢɫɩɨɥɶɡɭɸɬɫɹɞɥɹ ɭɤɚɡɚɧɢɹ ɢ ɢɡɦɟɪɟɧɢɹ ɧɟɤɨɬɨɪɵɯ ɯɚɪɚɤɬɟɪɢɫɬɢɤ ɩɨɫɬɪɨɟɧɧɵɯ ɝɪɚɮɢɤɨɜ.
ɗɬɢ ɮɭɧɤɰɢɢ ɦɨɝɭɬɜɵɡɵɜɚɬɶɫɹ ɫ ɩɨɦɨɳɶɸ ɩɚɧɟɥɢ ɢɧɫɬɪɭɦɟɧɬɨɜ ɝɪɚɮɢɱɟɫɤɨɝɨ ɨɤɧɚ ɩɨɫɥɟ ɩɨɫɬɪɨɟɧɢɹ ɝɪɚɮɢɤɨɜ. ɂɡ ɨɤɧɚ ɨɩɬɢɦɢɡɚɰɢɢ OPTIMIZE ɢ ɚɧɚɥɢɡɚɫɨɨɬɜɟɬɫɬɜɭɸɳɟɝɨ ɚɧɚɥɢɡɚ ɧɚɠɚɬɢɟɦ ɤɧɨɩɤɢMonte Carlo — PROPERTIES ɝɪɭɩɩɚ ɮɭɧɤɰɢɣ PERFORMANCE ɜɵɡɵɜɚɟɬɫɹ ɧɚɠɚɬɢɟɦ ɤɥɚɜɢɲɢGET.
ɇɢɠɟ ɜ ɬɚɛɥɢɰɟ ɩɪɢɜɟɞɟɧ ɫɩɢɫɨɤ ɷɬɢɯ ɮɭɧɤɰɢɣ ɫ ɢɯ 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 ɜɵɩɨɥɧɟɧɢɢ ɤɨɬɨɪɨɝɨ ɛɭɞɟɬ ɜɵɱɢɫɥɹɬɶɫɹɮɭɧɤɰɢɹ ɝɪɭɩɩɵ PERFORMANCE. Ɉɛɵɱɧɨ ɜɵɱɢɫɥɟɧɢɹ ɩɪɨɢɡɜɨɞɹɬ ɩɨɫɥɟ ɭɫɬɚɧɨɜɥɟɧɢɹ ɛɵɫɬɪɵɯ ɩɟɪɟɯɨɞɧɵɯ ɩɪɨɰɟɫɫɨɜ, ɱɬɨ ɡɚɫɬɚɜɥɹɟɬ ɢɫɤɥɸɱɚɬɶ ɧɚɱɚɥɶɧɵɣ ɷɬɚɩ ɢɡ ɪɚɫɫɦɨɬɪɟɧɢɹ. ɉɨɷɬɨɦɭ ɞɚɧɧɨɟ ɜɵɪɚɠɟɧɢɟ ɨɛɵɱɧɨ ɢɦɟɟɬ ɜɢɞ ɫɥɟɞɭɸɳɟɝɨ ɬɢɩɚ «T>100 ns».N — ɰɟɥɨɟ ɱɢɫɥɨ, ɭɤɚɡɵɜɚɸɳɟɟ ɤɚɤɨɟ ɩɨ ɩɨɪɹɞɤɭ ɢɡɦɟɪɟɧɢɟ ɞɟɥɚɟɬɫɹ.
ɇɚɩɪɢɦɟɪ ɧɟɨɛɯɨɞɢɦɨ ɢɡɦɟɪɢɬɶ ɞɥɢɬɟɥɶɧɨɫɬɶ ɮɪɨɧɬɚ ɧɟɫɤɨɥɶɤɢɯ ɢɞɭɳɢɯ ɩɨɞɪɹɞ ɢɦɩɭɥɶɫɨɜ. 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 ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɦɢ ɮɭɧɤɰɢɹɦɢ.High — ɜɟɪɯɧɟɟ ɝɪɚɧɢɱɧɨɟ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ, ɢɫɩɨɥɶɡɭɟɦɨɟ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɦɢ ɮɭɧɤɰɢɹɦɢ.Level — ɭɪɨɜɟɧɶ ɡɧɚɱɟɧɢɹ ɩɟɪɟɦɟɧɧɨɣ, ɢɫɩɨɥɶɡɭɟɦɵɣ ɩɪɢ ɜɵɱɢɫɥɟɧɢɢ ɪɚɡɥɢɱɧɵɯ ɩɚɪɚɦɟɬɪɨɜ ɫɢɝɧɚɥɨɜ.ɏɮɨɥɱɣɣɫɛɢɟɠɦɛPERFORMANCERise_Time(Y_expr,Boolean_expr,N,low,high) — ɞɥɢɬɟɥɶɧɨɫɬɶ ɜɨɡɪɚɫɬɚɧɢɹ ɜɞɨɥɶ ɨɫɢ Xɩɟɪɟɦɟɧɧɨɣ Y ɨɬ ɭɤɚɡɚɧɧɨɝɨ ɧɢɠɧɟɝɨ (Low) ɞɨ ɭɤɚɡɚɧɧɨɝɨ ɜɟɪɯɧɟɝɨ (High) ɭɪɨɜɧɟɣ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɡɚɞɚɧɧɨɝɨ ɥɨɝɢɱɟɫɤɨɝɨ ɜɵɪɚɠɟɧɢɹ Boolean_expr.Fall_Time(Y_expr,Boolean_expr,N,low,high) — ɞɥɢɬɟɥɶɧɨɫɬɶ ɭɛɵɜɚɧɢɹ ɜɞɨɥɶ ɨɫɢ X ɩɟɪɟɦɟɧɧɨɣ Y ɨɬ ɭɤɚɡɚɧɧɨɝɨ ɜɟɪɯɧɟɝɨ (High) ɞɨ ɭɤɚɡɚɧɧɨɝɨ ɧɢɠɧɟɝɨ (Low) ɭɪɨɜɧɟɣ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɡɚɞɚɧɧɨɝɨ ɥɨɝɢɱɟɫɤɨɝɨ ɜɵɪɚɠɟɧɢɹ Boolean_expr.ȼ ɪɟɠɢɦɟ Cursor Mode ɤɭɪɫɨɪɵ ɝɪɚɮɢɤɨɜ ɩɨɦɟɳɚɸɬɫɹ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨ ɜ ɞɜɟ ɜɵɛɪɚɧɧɵɟ130Ɇ.Ⱥ.
Ⱥɦɟɥɢɧɚ03.03.2006ɫɬɪ. 131 ɢɡ 135ɬɨɱɤɢ ɢ ɜɨɡɜɪɚɳɚɟɬɫɹ ɪɚɡɧɨɫɬɶ ɤɨɨɪɞɢɧɚɬ X ɞɥɹ ɷɬɢɯ ɬɨɱɟɤ. Ɏɭɧɤɰɢɢ Rise_Time ɢ Fall_Timeɦɨɠɧɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɞɥɹ ɢɡɦɟɪɟɧɢɹ ɜɪɟɦɟɧɢ ɧɚɪɚɫɬɚɧɢɹ ɢ ɫɩɚɞɚ ɢɦɩɭɥɶɫɧɵɯ ɫɢɝɧɚɥɨɜ.Peak_X(Y_expr,Boolean_expr,N) — ɗɬɚ ɮɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɤɨɨɪɞɢɧɚɬɭ X ɨɱɟɪɟɞɧɨɝɨɥɨɤɚɥɶɧɨɝɨ 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 ɞɨɩɨɥɧɢɬɟɥɶɧɨ ɩɨɦɟɳɚɟɬɫɹ ɥɟɜɵɣ ɢɥɢ ɩɪɚɜɵɣ ɤɭɪɫɨɪ ɜ ɨɱɟɪɟɞɧɭɸ ɬɨɱɤɭɥɨɤɚɥɶɧɨɝɨ ɦɚɤɫɢɦɭɦɚ.Peak_Y(Y_expr,Boolean_expr,N) — ɮɭɧɤɰɢɹ ɚɧɚɥɨɝɢɱɧɚ ɮɭɧɤɰɢɢ Peak_X, ɧɨ ɜɨɡɜɪɚɳɚɟɬɡɧɚɱɟɧɢɟ ɤɨɨɪɞɢɧɚɬɵ Y ɬɨɱɤɢ ɥɨɤɚɥɶɧɨɝɨ ɦɚɤɫɢɦɭɦɚ.
Ɏɭɧɤɰɢɹ ɦɨɠɟɬ ɢɫɩɨɥɶɡɨɜɚɬɶɫɹ ɞɥɹ ɢɡɦɟɪɟɧɢɹ ɡɧɚɱɟɧɢɣ ɜɵɛɪɨɫɨɜ ɩɪɢ ɚɧɚɥɢɡɟ ɩɟɪɟɯɨɞɧɵɯ ɩɪɨɰɟɫɫɨɜ ɢ ɩɭɥɶɫɚɰɢɣ ɤɨɷɮɮɢɰɢɟɧɬɚɩɟɪɟɞɚɱɢ ɮɢɥɶɬɪɨɜ ɩɪɢ ɩɪɨɜɟɞɟɧɢɢ AC ɚɧɚɥɢɡɚ.Valley_X(Y_expr,Boolean_expr,N) — ɮɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɤɨɨɪɞɢɧɚɬɭ X ɨɱɟɪɟɞɧɨɝɨ ɥɨɤɚɥɶɧɨɝɨ ɦɢɧɢɦɭɦɚ (VALLEY) ɜɵɛɪɚɧɧɨɣ ɩɟɪɟɦɟɧɧɨɣ Y_expr. Ʌɨɤɚɥɶɧɵɣ ɦɢɧɢɦɭɦ — ɷɬɨ ɬɨɱɤɚ,ɡɧɚɱɟɧɢɟ ɮɭɧɤɰɢɢ Y ɜ ɤɨɬɨɪɨɣ ɦɟɧɶɲɟ ɱɟɦ ɜ ɫɨɫɟɞɧɢɯ ɬɨɱɤɚɯ ɫ ɨɛɟɢɯ ɫɬɨɪɨɧ. ȼ ɪɟɠɢɦɟ CursorMode ɩɪɢ ɷɬɨɦ ɞɨɩɨɥɧɢɬɟɥɶɧɨ ɩɨɦɟɳɚɟɬɫɹ ɥɟɜɵɣ ɢɥɢ ɩɪɚɜɵɣ ɤɭɪɫɨɪ ɜ ɨɱɟɪɟɞɧɭɸ ɬɨɱɤɭ ɥɨɤɚɥɶɧɨɝɨ ɦɚɤɫɢɦɭɦɚ.Valley_Y(Y_expr,Boolean_expr,N): ɗɬɚ ɮɭɧɤɰɢɹ ɚɧɚɥɨɝɢɱɧɚ ɮɭɧɤɰɢɢ Valley_X, ɧɨ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɤɨɨɪɞɢɧɚɬɵ Y ɬɨɱɤɢ ɥɨɤɚɥɶɧɨɝɨ ɦɢɧɢɦɭɦɚ. Ɏɭɧɤɰɢɹ ɦɨɠɟɬ ɢɫɩɨɥɶɡɨɜɚɬɶɫɹ ɞɥɹɢɡɦɟɪɟɧɢɹ ɡɧɚɱɟɧɢɣ ɨɬɪɢɰɚɬɟɥɶɧɵɯ ɜɵɛɪɨɫɨɜ ɩɪɢ ɚɧɚɥɢɡɟ ɩɟɪɟɯɨɞɧɵɯ ɩɪɨɰɟɫɫɨɜ ɢ ɩɭɥɶɫɚɰɢɣɤɨɷɮɮɢɰɢɟɧɬɚ ɩɟɪɟɞɚɱɢ ɮɢɥɶɬɪɨɜ ɩɪɢ ɩɪɨɜɟɞɟɧɢɢ AC ɚɧɚɥɢɡɚ.Peak_Valley(Y_expr,Boolean_expr,N) — ɜɨɡɜɪɚɳɚɟɬ ɪɚɡɧɨɫɬɶ ɤɨɨɪɞɢɧɚɬ Y 2-ɯ ɫɨɫɟɞɧɢɯɬɨɱɟɤ ɥɨɤɚɥɶɧɨɝɨ ɦɚɤɫɢɦɭɦɚ ɢ ɦɢɧɢɦɭɦɚ ɜɵɛɪɚɧɧɨɣ ɩɟɪɟɦɟɧɧɨɣ Y_expr.
ȼ ɪɟɠɢɦɟ CursorMode ɞɨɩɨɥɧɢɬɟɥɶɧɨ ɩɨɦɟɳɚɸɬɫɹ ɥɟɜɵɣ ɢ ɩɪɚɜɵɣ ɤɭɪɫɨɪɵ ɜ ɨɱɟɪɟɞɧɵɟ ɧɚɣɞɟɧɧɵɟ 2 ɬɨɱɤɢɦɚɤɫɢɦɭɦɚ ɢ ɦɢɧɢɦɭɦɚ. Ɇɨɠɟɬ ɢɫɩɨɥɶɡɨɜɚɬɶɫɹ ɞɥɹ ɢɡɦɟɪɟɧɢɹ ɪɚɡɦɚɯɚ ɪɚɡɧɨɨɛɪɚɡɧɵɯ ɩɭɥɶɫɚɰɢɣ, ɜɵɛɪɨɫɨɜ ɢ ɚɦɩɥɢɬɭɞ ɫɢɝɧɚɥɨɜ.Period(Y_expr,Boolean_expr,N) — ɜɨɡɜɪɚɳɚɟɬ ɩɟɪɢɨɞ ɤɨɥɟɛɚɧɢɣ ɩɟɪɟɦɟɧɧɨɣ Y_expr ɩɭɬɟɦ ɢɡɦɟɪɟɧɢɹ ɪɚɫɫɬɨɹɧɢɹ ɩɨ ɨɫɢ X ɦɟɠɞɭ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɵɦɢ ɨɞɢɧɚɤɨɜɵɦɢ ɡɧɚɱɟɧɢɹɦɢ ɜɟɥɢɱɢɧɵ Y_expr. ɉɟɪɜɨɧɚɱɚɥɶɧɨ ɧɚɯɨɞɢɬɫɹ ɫɪɟɞɧɟɟ ɡɧɚɱɟɧɢɹ ɜɟɥɢɱɢɧɵ Y_expr ɧɚ ɢɧɬɟɪɜɚɥɟɦɨɞɟɥɢɪɨɜɚɧɢɹ, ɝɞɟ ɫɨɛɥɸɞɚɟɬɫɹ ɢɫɬɢɧɧɨɫɬɶ ɥɨɝɢɱɟɫɤɨɝɨ ɜɵɪɚɠɟɧɢɹ Boolean_expr. Ɂɚɬɟɦɢɳɭɬɫɹ 2 ɨɱɟɪɟɞɧɵɯ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɵɯ ɭɱɚɫɬɤɚ ɜɨɡɪɚɫɬɚɧɢɹ ɜɟɥɢɱɢɧɵ ɨɬ ɫɪɟɞɧɟɝɨ ɡɧɚɱɟɧɢɹ.Ɋɚɡɧɢɰɚ ɜ ɪɚɫɫɬɨɹɧɢɢ ɩɨ ɨɫɢ X ɦɟɠɞɭ ɷɬɢɦɢ ɬɨɱɤɚɦɢ ɢ ɩɪɢɧɢɦɚɟɬɫɹ ɡɚ ɡɧɚɱɟɧɢɟ ɩɟɪɢɨɞɚ ɤɨɥɟɛɚɧɢɣ.
Ʉɚɤ ɩɪɚɜɢɥɨ, ɜɜɨɞɢɦɨɟ ɥɨɝɢɱɟɫɤɨɟ ɜɵɪɚɠɟɧɢɟ ɛɵɜɚɟɬ ɬɢɩɚ "T>500ns" ɢ ɢɫɩɨɥɶɡɭɟɬɫɹɞɥɹ ɢɫɤɥɸɱɟɧɢɹ ɨɲɢɛɨɤ ɨɩɪɟɞɟɥɟɧɢɹ ɩɟɪɢɨɞɚ ɧɚ ɧɟɤɨɥɟɛɚɬɟɥɶɧɨɦ ɭɱɚɫɬɤɟ ɩɪɨɰɟɫɫɚ. ɍɞɨɛɧɚɞɥɹ ɨɩɪɟɞɟɥɟɧɢɹ ɩɟɪɢɨɞɚ ɤɨɥɟɛɚɧɢɣ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɟɣ ɧɚɩɪɹɠɟɧɢɟ-ɱɚɫɬɨɬɚ, ɝɞɟ ɫɭɳɟɫɬɜɭɟɬɧɟɨɛɯɨɞɢɦɨɫɬɶ ɢɡɦɟɪɟɧɢɹ ɩɟɪɢɨɞɚ ɤɨɥɟɛɚɧɢɣ ɫ ɜɵɫɨɤɨɣ ɬɨɱɧɨɫɬɶɸ.
Ɏɭɧɤɰɢɹ ɪɚɛɨɬɚɟɬ ɧɚɢɛɨɥɟɟ ɷɮɮɟɤɬɢɜɧɨ ɞɥɹ ɤɨɥɟɛɥɸɳɟɣɫɹ ɩɟɪɟɦɟɧɧɨɣ, ɩɪɨɯɨɞɹɳɟɣ ɩɪɢ ɷɬɨɦ ɱɟɪɟɡ ɫɜɨɟ ɫɪɟɞɧɟɟɡɧɚɱɟɧɢɟ ɜ ɬɟɱɟɧɢɟ ɩɟɪɢɨɞɚ. Ɉɧɚ ɧɟ ɛɭɞɟɬ ɪɚɛɨɬɚɬɶ ɫ ɞɨɫɬɚɬɨɱɧɨɣ ɬɨɱɧɨɫɬɶɸ ɫ ɤɨɥɟɛɚɧɢɹɦɢ,ɤɨɬɨɪɵɟ ɫɨɞɟɪɠɚɬ ɝɚɪɦɨɧɢɤɢ ɡɧɚɱɢɬɟɥɶɧɨɣ ɜɟɥɢɱɢɧɵ. ȼ ɪɟɠɢɦɟ Cursor Mode ɞɨɩɨɥɧɢɬɟɥɶɧɨɩɨɦɟɳɚɸɬɫɹ ɥɟɜɵɣ ɢ ɩɪɚɜɵɣ ɤɭɪɫɨɪɵ ɜ 2 ɭɤɚɡɚɧɧɵɟ ɬɨɱɤɢ ɝɪɚɮɢɤɚ (ɤɨɬɨɪɵɟ ɨɩɪɟɞɟɥɹɸɬɫɹ ɤɚɤɩɨɤɚɡɚɧɨ ɜɵɲɟ) ɢ ɜɨɡɜɪɚɳɚɟɬɫɹ ɪɚɫɫɬɨɹɧɢɟ ɦɟɠɞɭ ɧɢɦɢ ɩɨ ɨɫɢ X.Frequency(Y_expr,Boolean_expr,N) — ɞɨɩɨɥɧɟɧɢɟ ɮɭɧɤɰɢɢ Period.
Ɋɚɛɨɬɚɟɬ ɜ ɬɨɱɧɨɫɬɢɬɚɤɠɟ ɤɚɤ ɢ ɮɭɧɤɰɢɹ PERIOD, ɧɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ 1/Period.131D:\Ɉɩɢɫɚɧɢɟ MC8\MC8_V1_2.DOCWidth(Y_expr,Boolean_expr,N,level): ɗɬɚ ɮɭɧɤɰɢɹ ɢɡɦɟɪɹɟɬ ɪɚɫɫɬɨɹɧɢɟ ɩɨ ɨɫɢ X ɦɟɠɞɭ 2ɦɹ ɬɨɱɤɚɦɢ ɝɪɚɮɢɤɚ Y_expr ɫ ɡɚɞɚɧɧɵɦɢ ɡɧɚɱɟɧɢɹɦɢ ɨɪɞɢɧɚɬɵ level. ȼ ɪɟɠɢɦɟ Cursor Mode ɞɨɩɨɥɧɢɬɟɥɶɧɨ ɩɨɦɟɳɚɸɬɫɹ ɤɭɪɫɨɪɵ ɝɪɚɮɢɱɟɫɤɨɝɨ ɨɤɧɚ (ɥɟɜɵɣ ɢ ɩɪɚɜɵɣ) ɜ ɨɱɟɪɟɞɧɵɟ ɜɵɛɪɚɧɧɵɟ ɬɨɱɤɢ ɝɪɚɮɢɤɚ ɢ ɜɨɡɜɪɚɳɚɟɬɫɹ ɤɚɤ ɪɟɡɭɥɶɬɚɬ ɪɚɫɫɬɨɹɧɢɟ ɩɨ ɨɫɢ X ɦɟɠɞɭ ɷɬɢɦɢ ɬɨɱɤɚɦɢ.High_X(Y_expr,Boolean_expr) — ɨɩɪɟɞɟɥɹɟɬ ɤɨɨɪɞɢɧɚɬɭ X ɬɨɱɤɢ ɝɥɨɛɚɥɶɧɨɝɨ ɦɚɤɫɢɦɭɦɚɮɭɧɤɰɢɢ Y_expr. ȼ ɪɟɠɢɦɟ Cursor Mode ɞɨɩɨɥɧɢɬɟɥɶɧɨ ɩɨɦɟɳɚɟɬɫɹ ɜɵɛɪɚɧɧɵɣ ɥɟɜɵɣ (ɢɥɢɩɪɚɜɵɣ) ɤɭɪɫɨɪ ɜ ɧɚɣɞɟɧɧɭɸ ɬɨɱɤɭ ɢ ɜɨɡɜɪɚɳɚɟɬɫɹ ɟɟ ɤɨɨɪɞɢɧɚɬɚ ɩɨ ɨɫɢ X.High_Y(Y_expr,Boolean_expr) — ɨɩɪɟɞɟɥɹɟɬ ɤɨɨɪɞɢɧɚɬɭ Y ɬɨɱɤɢ ɝɥɨɛɚɥɶɧɨɝɨ ɦɚɤɫɢɦɭɦɚɮɭɧɤɰɢɢ Y_expr.
ȼ ɪɟɠɢɦɟ Cursor Mode ɞɨɩɨɥɧɢɬɟɥɶɧɨ ɩɨɦɟɳɚɟɬɫɹ ɜɵɛɪɚɧɧɵɣ ɥɟɜɵɣ (ɢɥɢɩɪɚɜɵɣ) ɤɭɪɫɨɪ ɜ ɧɚɣɞɟɧɧɭɸ ɬɨɱɤɭ ɢ ɜɨɡɜɪɚɳɚɟɬɫɹ ɟɟ ɤɨɨɪɞɢɧɚɬɚ ɩɨ ɨɫɢ Y.Low_X(Y_expr,Boolean_expr) — ɨɩɪɟɞɟɥɹɟɬ ɤɨɨɪɞɢɧɚɬɭ X ɬɨɱɤɢ ɝɥɨɛɚɥɶɧɨɝɨ ɦɢɧɢɦɭɦɚɮɭɧɤɰɢɢ Y_expr. ȼ ɪɟɠɢɦɟ Cursor Mode ɞɨɩɨɥɧɢɬɟɥɶɧɨ ɩɨɦɟɳɚɟɬɫɹ ɜɵɛɪɚɧɧɵɣ ɥɟɜɵɣ (ɢɥɢɩɪɚɜɵɣ) ɤɭɪɫɨɪ ɜ ɧɚɣɞɟɧɧɭɸ ɬɨɱɤɭ ɢ ɜɨɡɜɪɚɳɚɟɬɫɹ ɟɟ ɤɨɨɪɞɢɧɚɬɚ ɩɨ ɨɫɢ X.Low_Y(Y_expr,Boolean_expr) — ɨɩɪɟɞɟɥɹɟɬ ɤɨɨɪɞɢɧɚɬɭ Y ɬɨɱɤɢ ɝɥɨɛɚɥɶɧɨɝɨ ɦɢɧɢɦɭɦɚɮɭɧɤɰɢɢ Y_expr.
ȼ ɪɟɠɢɦɟ Cursor Mode ɞɨɩɨɥɧɢɬɟɥɶɧɨ ɩɨɦɟɳɚɟɬɫɹ ɜɵɛɪɚɧɧɵɣ ɥɟɜɵɣ (ɢɥɢɩɪɚɜɵɣ) ɤɭɪɫɨɪ ɜ ɧɚɣɞɟɧɧɭɸ ɬɨɱɤɭ ɢ ɜɨɡɜɪɚɳɚɟɬɫɹ ɟɟ ɤɨɨɪɞɢɧɚɬɚ ɩɨ ɨɫɢ Y.X_Level(Y_expr,Boolean_expr,N,Y_level) — ɨɩɪɟɞɟɥɹɟɬ ɤɨɨɪɞɢɧɚɬɭ X ɨɱɟɪɟɞɧɨɣ ɬɨɱɤɢɝɪɚɮɢɤɚ, ɜ ɤɨɬɨɪɨɣ ɩɟɪɟɦɟɧɧɚɹ Y_expr ɩɪɢɧɢɦɚɟɬ ɡɧɚɱɟɧɢɟ Y_Level. ȼ ɪɟɠɢɦɟ Cursor Mode ɞɨɩɨɥɧɢɬɟɥɶɧɨ ɩɨɦɟɳɚɟɬɫɹ ɜɵɛɪɚɧɧɵɣ ɥɟɜɵɣ (ɢɥɢ ɩɪɚɜɵɣ) ɤɭɪɫɨɪ ɜ ɧɚɣɞɟɧɧɭɸ ɬɨɱɤɭ ɢ ɜɨɡɜɪɚɳɚɟɬɫɹ ɟɟ ɤɨɨɪɞɢɧɚɬɚ ɩɨ ɨɫɢ X.Y_Level(Y_expr,Boolean_expr,N,X_level) — ɨɩɪɟɞɟɥɹɟɬ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ Y_expr ɜɬɨɱɤɟ ɫ ɚɛɫɰɢɫɫɨɣ X_Level. ȼ ɪɟɠɢɦɟ Cursor Mode ɞɨɩɨɥɧɢɬɟɥɶɧɨ ɩɨɦɟɳɚɟɬɫɹ ɜɵɛɪɚɧɧɵɣ ɥɟɜɵɣ (ɢɥɢ ɩɪɚɜɵɣ) ɤɭɪɫɨɪ ɜ ɧɚɣɞɟɧɧɭɸ ɬɨɱɤɭ ɢ ɜɨɡɜɪɚɳɚɟɬɫɹ ɟɟ ɤɨɨɪɞɢɧɚɬɚ ɩɨ ɨɫɢ Y.X_Delta(Y_expr,Boolean_expr,N,Y_low,Y_high) — ɨɩɪɟɞɟɥɹɟɬ ɪɚɡɧɨɫɬɶ ɚɛɫɰɢɫɫ 2-ɯ ɨɱɟɪɟɞɧɵɯ ɬɨɱɟɤ ɝɪɚɮɢɤɚ, ɜ ɤɨɬɨɪɵɯ ɩɟɪɟɦɟɧɧɚɹ Y_expr ɩɪɢɧɢɦɚɟɬ ɡɧɚɱɟɧɢɹ Y_High ɢ Y_Low.
