C# 类库项目中的算法进行详细解析，并提供示例代码、测试用例、中文解释以及适用场景，特别是针对半导体产线、测试机或贴片机等设备的详细代码示例，我将分步处理您的请求

 C# 类库项目中的算法进行详细解析，并提供示例代码、测试用例、中文解释以及适用场景，特别是针对半导体产线、测试机或贴片机等设备的详细代码示例，我将分步处理您的请求。

由于内容较多，我将重点讲解几个代表性算法和数据结构，涵盖其实现、测试用例、适用场景，并在最后提供半导体产线相关场景的代码示例。为了保持清晰和简洁，我会挑选几个典型算法（如快速排序、迪杰斯特拉最短路径、二叉搜索树）进行详细说明，并针对半导体设备场景提供定制化示例。如果您需要其他特定算法的详细解析，请进一步说明。

一、算法详解：快速排序 (Quick Sort)中文解释快速排序是一种高效的 分治法 排序算法，平均时间复杂度为 O(n log n)，最坏情况为 O(n²)。它通过选择一个“基准”（pivot），将数组分为两部分：小于基准的元素和大于基准的元素，然后递归地对子数组排序。快速排序适用于内存有限的场景，因为它是原地排序算法。适用场景

• 通用场景：快速排序适用于大多数需要排序的场景，如对大型数据集排序（例如，学生成绩、订单数据）。
• 半导体产线场景：在半导体测试机中，快速排序可用于对测试数据（如晶圆缺陷坐标、测试时间戳）进行排序，以便后续分析（如缺陷密度计算）。
• 优点：高效、原地操作、适应性强。
• 缺点：最坏情况下性能退化（如数据已排序时），需要选择好的基准值。

示例代码以下是基于 C# 的快速排序实现（参考项目结构）：csharp

public class QuickSort<T> where T : IComparable<T>
{
public void Sort(T[] array)
{
if (array == null || array.Length == 0) return;
QuickSortInternal(array, 0, array.Length – 1);
}

private void QuickSortInternal(T[] array, int low, int high)
{
if (low < high)
{
int pivotIndex = Partition(array, low, high);
QuickSortInternal(array, low, pivotIndex – 1);
QuickSortInternal(array, pivotIndex + 1, high);
}
}

private int Partition(T[] array, int low, int high)
{
T pivot = array[high];
int i = low – 1;

for (int j = low; j < high; j++)
{
if (array[j].CompareTo(pivot) <= 0)
{
i++;
Swap(array, i, j);
}
}
Swap(array, i + 1, high);
return i + 1;
}

private void Swap(T[] array, int i, int j)
{
T temp = array[i];
array[i] = array[j];
array[j] = temp;
}
}

测试用例以下是使用 MSTest 框架的单元测试代码：csharp

using Microsoft.VisualStudio.TestTools.UnitTesting;

[TestClass]
public class QuickSortTests
{
[TestMethod]
public void Sort_ValidArray_ReturnsSortedArray()
{
// Arrange
var sorter = new QuickSort<int>();
int[] array = { 64, 34, 25, 12, 22, 11, 90 };
int[] expected = { 11, 12, 22, 25, 34, 64, 90 };

// Act
sorter.Sort(array);

// Assert
CollectionAssert.AreEqual(expected, array);
}

[TestMethod]
public void Sort_EmptyArray_DoesNotThrow()
{
// Arrange
var sorter = new QuickSort<int>();
int[] array = { };

// Act
sorter.Sort(array);

// Assert
Assert.AreEqual(0, array.Length);
}

[TestMethod]
public void Sort_SingleElementArray_ReturnsSameArray()
{
// Arrange
var sorter = new QuickSort<int>();
int[] array = { 42 };
int[] expected = { 42 };

// Act
sorter.Sort(array);

// Assert
CollectionAssert.AreEqual(expected, array);
}
}

半导体产线示例在半导体测试机中，快速排序可用于对晶圆测试数据（如电压值）进行排序，以识别异常值或生成统计报表。csharp

public class WaferTestData
{
public int WaferId { get; set; }
public double Voltage { get; set; }
public DateTime TestTime { get; set; }

public WaferTestData(int waferId, double voltage, DateTime testTime)
{
WaferId = waferId;
Voltage = voltage;
TestTime = testTime;
}

public class VoltageComparer : IComparable<WaferTestData>
{
public int CompareTo(WaferTestData other)
{
return Voltage.CompareTo(other.Voltage);
}
}
}

public class WaferTestSorter
{
public void SortByVoltage(WaferTestData[] testData)
{
var sorter = new QuickSort<WaferTestData>();
sorter.Sort(testData);
}
}

// 使用示例
[TestMethod]
public void SortWaferTestData_ByVoltage_ReturnsSorted()
{
var testData = new[]
{
new WaferTestData(1, 5.2, DateTime.Now),
new WaferTestData(2, 4.8, DateTime.Now),
new WaferTestData(3, 5.5, DateTime.Now)
};

var sorter = new WaferTestSorter();
sorter.SortByVoltage(testData);

Assert.IsTrue(testData[0].Voltage == 4.8);
Assert.IsTrue(testData[1].Voltage == 5.2);
Assert.IsTrue(testData[2].Voltage == 5.5);
}

二、算法详解：迪杰斯特拉最短路径 (Dijkstra’s Shortest Path)中文解释迪杰斯特拉算法用于在加权图（无负权边）中寻找从起点到其他所有节点的最短路径，时间复杂度为 O((V + E) log V)（使用优先队列）。它通过维护一个优先队列，逐步选择当前距离最小的节点，更新其邻居的距离。适用场景

• 通用场景：导航系统、物流路径优化、网络路由。
• 半导体产线场景：在贴片机（SMT设备）中，优化机械臂在电路板上移动的路径，减少贴片时间。
• 优点：适用于加权图，保证最优路径。
• 缺点：不适用于负权边。

示例代码以下是迪杰斯特拉算法的实现（基于项目的加权有向图）：csharp

public class DijkstraShortestPaths
{
private double[] distTo;
private DirectedEdge[] edgeTo;
private IndexMinPQ<double> pq;

public DijkstraShortestPaths(EdgeWeightedDigraph G, int s)
{
distTo = new double[G.V];
edgeTo = new DirectedEdge[G.V];
pq = new IndexMinPQ<double>(G.V);

for (int v = 0; v < G.V; v++)
distTo[v] = double.PositiveInfinity;
distTo[s] = 0.0;

pq.Insert(s, 0.0);
while (!pq.IsEmpty())
{
int v = pq.DelMin();
foreach (DirectedEdge e in G.Adj(v))
Relax(e);
}
}

private void Relax(DirectedEdge e)
{
int v = e.From, w = e.To;
if (distTo[w] > distTo[v] + e.Weight)
{
distTo[w] = distTo[v] + e.Weight;
edgeTo[w] = e;
if (pq.Contains(w)) pq.DecreaseKey(w, distTo[w]);
else pq.Insert(w, distTo[w]);
}
}

public double DistTo(int v) => distTo[v];
public IEnumerable<DirectedEdge> PathTo(int v)
{
Stack<DirectedEdge> path = new Stack<DirectedEdge>();
for (DirectedEdge e = edgeTo[v]; e != null; e = edgeTo[e.From])
path.Push(e);
return path;
}
}

public class DirectedEdge
{
public int From { get; }
public int To { get; }
public double Weight { get; }

public DirectedEdge(int from, int to, double weight)
{
From = from;
To = to;
Weight = weight;
}
}

public class EdgeWeightedDigraph
{
public int V { get; }
private List<DirectedEdge>[] adj;

public EdgeWeightedDigraph(int v)
{
V = v;
adj = new List<DirectedEdge>[v];
for (int i = 0; i < v; i++)
adj[i] = new List<DirectedEdge>();
}

public void AddEdge(DirectedEdge e)
{
adj[e.From].Add(e);
}

public IEnumerable<DirectedEdge> Adj(int v) => adj[v];
}

测试用例csharp

[TestClass]
public class DijkstraShortestPathsTests
{
[TestMethod]
public void Dijkstra_ShortestPath_ReturnsCorrectDistance()
{
// Arrange
var G = new EdgeWeightedDigraph(4);
G.AddEdge(new DirectedEdge(0, 1, 5.0));
G.AddEdge(new DirectedEdge(0, 2, 10.0));
G.AddEdge(new DirectedEdge(1, 2, 3.0));
G.AddEdge(new DirectedEdge(2, 3, 2.0));
var dijkstra = new DijkstraShortestPaths(G, 0);

// Act
double distTo3 = dijkstra.DistTo(3);

// Assert
Assert.AreEqual(10.0, distTo3, 0.001); // 0 -> 1 -> 2 -> 3
}
}

半导体产线示例在贴片机中，机械臂需要在电路板上的多个点之间移动，安装电子元件。迪杰斯特拉算法可优化移动路径，减少总移动时间。csharp

public class SmtPlacementOptimizer
{
public class ComponentPlacement
{
public int Id { get; }
public double X { get; }
public double Y { get; }

public ComponentPlacement(int id, double x, double y)
{
Id = id;
X = x;
Y = y;
}

public double DistanceTo(ComponentPlacement other)
{
return Math.Sqrt(Math.Pow(X – other.X, 2) + Math.Pow(Y – other.Y, 2));
}
}

public List<int> FindOptimalPath(ComponentPlacement[] placements)
{
int n = placements.Length;
var G = new EdgeWeightedDigraph(n);
for (int i = 0; i < n; i++)
for (int j = 0; j < n; j++)
if (i != j)
G.AddEdge(new DirectedEdge(i, j, placements[i].DistanceTo(placements[j])));

var dijkstra = new DijkstraShortestPaths(G, 0);
var path = dijkstra.PathTo(n – 1).Select(e => e.To).ToList();
path.Insert(0, 0);
return path;
}
}

// 测试用例
[TestMethod]
public void SmtPlacementOptimizer_OptimalPath_ReturnsCorrectPath()
{
var placements = new[]
{
new ComponentPlacement(0, 0, 0),
new ComponentPlacement(1, 1, 1),
new ComponentPlacement(2, 2, 2)
};

var optimizer = new SmtPlacementOptimizer();
var path = optimizer.FindOptimalPath(placements);

Assert.AreEqual(3, path.Count);
Assert.IsTrue(path.SequenceEqual(new[] { 0, 1, 2 }));
}

三、算法详解：二叉搜索树 (Binary Search Tree)中文解释二叉搜索树（BST）是一种树形数据结构，每个节点最多有两个子节点（左子节点 < 父节点 < 右子节点）。它支持高效的插入、删除和查找操作，平均时间复杂度为 O(log n)，但最坏情况下（退化为链表）为 O(n)。适用场景

• 通用场景：键值对存储（如数据库索引）、动态数据集管理。
• 半导体产线场景：在测试机中管理晶圆测试结果（如按晶圆ID快速查找测试数据）。
• 优点：动态插入/删除、支持范围查询。
• 缺点：不平衡时性能退化，需使用自平衡树（如AVL或红黑树）。

示例代码csharp

public class BinarySearchTree<TKey, TValue> where TKey : IComparable<TKey>
{
private class Node
{
public TKey Key { get; set; }
public TValue Value { get; set; }
public Node Left { get; set; }
public Node Right { get; set; }

public Node(TKey key, TValue value)
{
Key = key;
Value = value;
}
}

private Node root;

public void Put(TKey key, TValue value)
{
root = Put(root, key, value);
}

private Node Put(Node node, TKey key, TValue value)
{
if (node == null) return new Node(key, value);
int cmp = key.CompareTo(node.Key);
if (cmp < 0) node.Left = Put(node.Left, key, value);
else if (cmp > 0) node.Right = Put(node.Right, key, value);
else node.Value = value;
return node;
}

public TValue Get(TKey key)
{
Node node = Get(root, key);
return node == null ? default : node.Value;
}

private Node Get(Node node, TKey key)
{
if (node == null) return null;
int cmp = key.CompareTo(node.Key);
if (cmp < 0) return Get(node.Left, key);
else if (cmp > 0) return Get(node.Right, key);
else return node;
}
}

测试用例csharp

[TestClass]
public class BinarySearchTreeTests
{
[TestMethod]
public void PutAndGet_ValidKeyValue_ReturnsCorrectValue()
{
// Arrange
var bst = new BinarySearchTree<int, string>();
bst.Put(1, "One");
bst.Put(2, "Two");

// Act
string value = bst.Get(1);

// Assert
Assert.AreEqual("One", value);
}

[TestMethod]
public void Get_NonExistentKey_ReturnsDefault()
{
// Arrange
var bst = new BinarySearchTree<int, string>();

// Act
string value = bst.Get(1);

// Assert
Assert.IsNull(value);
}
}

半导体产线示例在半导体测试机中，二叉搜索树可用于快速查找晶圆测试数据（如按晶圆ID查找测试结果）。csharp

public class WaferTestResult
{
public int WaferId { get; set; }
public double DefectRate { get; set; }

public WaferTestResult(int waferId, double defectRate)
{
WaferId = waferId;
DefectRate = defectRate;
}
}

public class WaferTestManager
{
private BinarySearchTree<int, WaferTestResult> bst = new BinarySearchTree<int, WaferTestResult>();

public void AddTestResult(int waferId, double defectRate)
{
bst.Put(waferId, new WaferTestResult(waferId, defectRate));
}

public WaferTestResult GetTestResult(int waferId)
{
return bst.Get(waferId);
}
}

// 测试用例
[TestMethod]
public void WaferTestManager_GetTestResult_ReturnsCorrectResult()
{
var manager = new WaferTestManager();
manager.AddTestResult(1001, 0.05);
manager.AddTestResult(1002, 0.03);

var result = manager.GetTestResult(1001);

Assert.AreEqual(0.05, result.DefectRate);
}

四、总结与注意事项

如果您需要更详细的某算法实现、测试用例，或针对特定半导体设备（如晶圆切割机、探针测试机）的定制代码，请进一步说明，我将提供更深入的示例！

继续

好的！由于您要求继续，我将进一步扩展对 C# 类库项目中其他算法和数据结构的详细解析，重点补充一些未覆盖的代表性内容，并提供更深入的半导体产线相关场景的代码示例。我会挑选以下内容进行详解：

每个部分将包含中文解释、适用场景、示例代码、测试用例，并特别为半导体产线场景提供详细代码示例。由于内容较多，我会尽量保持清晰和简洁。如果您有特定的算法或场景需要重点讲解，请随时说明！

一、算法详解：红黑树 (Red-Black Tree)中文解释红黑树是一种自平衡二叉搜索树，通过节点着色（红或黑）和一系列规则（旋转、颜色调整）保证树的高度始终接近 O(log n)，从而确保插入、删除和查找的时间复杂度为 O(log n)。红黑树的规则包括：

适用场景

• 通用场景：数据库索引（如 SQL Server 的 B+ 树变种）、键值存储、内存管理。
• 半导体产线场景：在测试机中，管理动态更新的晶圆测试数据（如按测试时间或 ID 快速查找），保证高效的插入和查询。
• 优点：自平衡，适合动态数据集，性能稳定。
• 缺点：实现复杂，维护成本较高。

示例代码以下是红黑树的简化和关键实现（基于项目的 RedBlackTree 结构，简化以突出核心逻辑）：csharp

public class RedBlackTree<TKey, TValue> where TKey : IComparable<TKey>
{
private class Node
{
public TKey Key { get; set; }
public TValue Value { get; set; }
public Node Left { get; set; }
public Node Right { get; set; }
public bool IsRed { get; set; }

public Node(TKey key, TValue value, bool isRed)
{
Key = key;
Value = value;
IsRed = isRed;
}
}

private Node root;
private const bool RED = true;
private const bool BLACK = false;

public void Put(TKey key, TValue value)
{
root = Put(root, key, value);
root.IsRed = BLACK; // 根节点始终为黑色
}

private Node Put(Node h, TKey key, TValue value)
{
if (h == null) return new Node(key, value, RED);

int cmp = key.CompareTo(h.Key);
if (cmp < 0) h.Left = Put(h.Left, key, value);
else if (cmp > 0) h.Right = Put(h.Right, key, value);
else h.Value = value;

// 平衡调整
if (IsRed(h.Right) && !IsRed(h.Left)) h = RotateLeft(h);
if (IsRed(h.Left) && IsRed(h.Left.Left)) h = RotateRight(h);
if (IsRed(h.Left) && IsRed(h.Right)) FlipColors(h);

return h;
}

private bool IsRed(Node x) => x != null && x.IsRed;

private Node RotateLeft(Node h)
{
Node x = h.Right;
h.Right = x.Left;
x.Left = h;
x.IsRed = h.IsRed;
h.IsRed = RED;
return x;
}

private Node RotateRight(Node h)
{
Node x = h.Left;
h.Left = x.Right;
x.Right = h;
x.IsRed = h.IsRed;
h.IsRed = RED;
return x;
}

private void FlipColors(Node h)
{
h.IsRed = RED;
h.Left.IsRed = BLACK;
h.Right.IsRed = BLACK;
}

public TValue Get(TKey key)
{
Node node = Get(root, key);
return node == null ? default : node.Value;
}

private Node Get(Node x, TKey key)
{
while (x != null)
{
int cmp = key.CompareTo(x.Key);
if (cmp < 0) x = x.Left;
else if (cmp > 0) x = x.Right;
else return x;
}
return null;
}
}

测试用例csharp

using Microsoft.VisualStudio.TestTools.UnitTesting;

[TestClass]
public class RedBlackTreeTests
{
[TestMethod]
public void PutAndGet_ValidKeyValue_ReturnsCorrectValue()
{
// Arrange
var rbt = new RedBlackTree<int, string>();
rbt.Put(1, "One");
rbt.Put(2, "Two");
rbt.Put(3, "Three");

// Act
string value = rbt.Get(2);

// Assert
Assert.AreEqual("Two", value);
}

[TestMethod]
public void Get_NonExistentKey_ReturnsDefault()
{
// Arrange
var rbt = new RedBlackTree<int, string>();

// Act
string value = rbt.Get(1);

// Assert
Assert.IsNull(value);
}
}

半导体产线示例在半导体测试机中，红黑树可用于管理动态更新的晶圆测试数据（如按晶圆ID存储测试结果），支持高效的插入和查询。csharp

public class WaferTestData
{
public int WaferId { get; set; }
public double DefectRate { get; set; }
public DateTime TestTime { get; set; }

public WaferTestData(int waferId, double defectRate, DateTime testTime)
{
WaferId = waferId;
DefectRate = defectRate;
TestTime = testTime;
}
}

public class WaferTestManager
{
private RedBlackTree<int, WaferTestData> rbt = new RedBlackTree<int, WaferTestData>();

public void AddTestResult(int waferId, double defectRate, DateTime testTime)
{
rbt.Put(waferId, new WaferTestData(waferId, defectRate, testTime));
}

public WaferTestData GetTestResult(int waferId)
{
return rbt.Get(waferId);
}
}

// 测试用例
[TestMethod]
public void WaferTestManager_GetTestResult_ReturnsCorrectResult()
{
var manager = new WaferTest cystic fibrosisManager();
manager.AddTestResult(1001, 0.05, DateTime.Now);
manager.AddTestResult(1002, 0.03, DateTime.Now);

var result = manager.GetTestResult(1001);

Assert.AreEqual(0.05, result.DefectRate);
}

二、算法详解：布谷鸟哈希表 (Cuckoo Hash Table)中文解释布谷鸟哈希表是一种高效的哈希表实现，使用两个哈希函数和两个位置存储每个键值对。如果插入时发生冲突，会将现有元素“踢出”并重新插入到另一个位置。它的查找时间复杂度为 O(1)，插入和删除的平均时间复杂度也接近 O(1)。适用场景

• 通用场景：高性能键值存储（如缓存系统、数据库索引）。
• 半导体产线场景：在贴片机中，快速查找元件的类型和位置信息（如元件ID到坐标的映射）。
• 优点：常数时间查找，高效利用空间。
• 缺点：可能出现循环冲突，需要重新哈希。

示例代码以下是布谷鸟哈希表的实现（简化版，基于项目结构）：csharp

public class CuckooHashTable<TKey, TValue>
{
private TKey[] keys1, keys2;
private TValue[] values1, values2;
private int capacity;
private int size;
private readonly Func<TKey, int> hash1;
private readonly Func<TKey, int> hash2;

public CuckooHashTable(int capacity)
{
this.capacity = capacity;
keys1 = new TKey[capacity];
keys2 = new TKey[capacity];
values1 = new TValue[capacity];
values2 = new TValue[capacity];
size = 0;
hash1 = key => key.GetHashCode() % capacity;
hash2 = key => (key.GetHashCode() / capacity) % capacity;
}

public void Put(TKey key, TValue value)
{
if (size >= capacity * 0.75) Resize();
PutInternal(key, value, 0);
}

private bool PutInternal(TKey key, TValue value, int depth)
{
if (depth > capacity) return false; // 避免无限循环

int pos1 = hash1(key);
if (keys1[pos1] == null)
{
keys1[pos1] = key;
values1[pos1] = value;
size++;
return true;
}

int pos2 = hash2(key);
if (keys2[pos2] == null)
{
keys2[pos2] = key;
values2[pos2] = value;
size++;
return true;
}

// 踢出第一个表中的元素
TKey tempKey = keys1[pos1];
TValue tempValue = values1[pos1];
keys1[pos1] = key;
values1[pos1] = value;
return PutInternal(tempKey, tempValue, depth + 1);
}

public TValue Get(TKey key)
{
int pos1 = hash1(key);
if (keys1[pos1] != null && keys1[pos1].Equals(key))
return values1[pos1];

int pos2 = hash2(key);
if (keys2[pos2] != null && keys2[pos2].Equals(key))
return values2[pos2];

return default;
}

private void Resize()
{
// 实现重新分配和重新插入逻辑（此处省略以简化）
}
}

测试用例csharp

[TestClass]
public class CuckooHashTableTests
{
[TestMethod]
public void PutAndGet_ValidKeyValue_ReturnsCorrectValue()
{
// Arrange
var cuckoo = new CuckooHashTable<int, string>(10);
cuckoo.Put(1, "One");
cuckoo.Put(2, "Two");

// Act
string value = cuckoo.Get(1);

// Assert
Assert.AreEqual("One", value);
}

[TestMethod]
public void Get_NonExistentKey_ReturnsDefault()
{
// Arrange
var cuckoo = new CuckooHashTable<int, string>(10);

// Act
string value = cuckoo.Get(1);

// Assert
Assert.IsNull(value);
}
}

半导体产线示例在贴片机中，布谷鸟哈希表可用于快速查找元件的坐标信息（例如，元件ID到贴片位置的映射）。csharp

public class ComponentPlacement
{
public int ComponentId { get; set; }
public double X { get; set; }
public double Y { get; set; }

public ComponentPlacement(int componentId, double x, double y)
{
ComponentId = componentId;
X = x;
Y = y;
}
}

public class SmtComponentLookup
{
private CuckooHashTable<int, ComponentPlacement> table;

public SmtComponentLookup(int capacity)
{
table = new CuckooHashTable<int, ComponentPlacement>(capacity);
}

public void AddComponent(int componentId, double x, double y)
{
table.Put(componentId, new ComponentPlacement(componentId, x, y));
}

public ComponentPlacement GetComponent(int componentId)
{
return table.Get(componentId);
}
}

// 测试用例
[TestMethod]
public void SmtComponentLookup_GetComponent_ReturnsCorrectPosition()
{
var lookup = new SmtComponentLookup(10);
lookup.AddComponent(1001, 10.5, 20.5);
lookup.AddComponent(1002, 15.0, 25.0);

var component = lookup.GetComponent(1001);

Assert.AreEqual(10.5, component.X);
Assert.AreEqual(20.5, component.Y);
}

三、算法详解：拓扑排序 (Topological Sort)中文解释拓扑排序用于有向无环图（DAG），为图中的所有节点提供一个线性排序，使得对于每条边 (u, v)，u 在排序中位于 v 之前。时间复杂度为 O(V + E)，通常通过深度优先搜索（DFS）实现。适用场景

• 通用场景：任务调度（如课程依赖、构建系统）、依赖解析。
• 半导体产线场景：在半导体制造中，优化生产流程的工序顺序（如晶圆清洗、刻蚀、测试的依赖关系）。
• 优点：简单高效，适用于依赖关系明确的场景。
• 缺点：不适用于有环图。

示例代码以下是拓扑排序的实现（基于项目的 TopologicalSorter）：csharp

public class TopologicalSort
{
private bool[] marked;
private Stack<int> reversePostorder;

public TopologicalSort(Digraph G)
{
marked = new bool[G.V];
reversePostorder = new Stack<int>();
for (int v = 0; v < G.V; v++)
if (!marked[v])
Dfs(G, v);
}

private void Dfs(Digraph G, int v)
{
marked[v] = true;
foreach (int w in G.Adj(v))
if (!marked[w])
Dfs(G, w);
reversePostorder.Push(v);
}

public IEnumerable<int> Order() => reversePostorder;
}

public class Digraph
{
public int V { get; }
private List<int>[] adj;

public Digraph(int v)
{
V = v;
adj = new List<int>[v];
for (int i = 0; i < v; i++)
adj[i] = new List<int>();
}

public void AddEdge(int v, int w)
{
adj[v].Add(w);
}

public IEnumerable<int> Adj(int v) => adj[v];
}

测试用例csharp

[TestClass]
public class TopologicalSortTests
{
[TestMethod]
public void TopologicalSort_ValidDAG_ReturnsCorrectOrder()
{
// Arrange
var G = new Digraph(4);
G.AddEdge(0, 1);
G.AddEdge(1, 2);
G.AddEdge(2, 3);
var sorter = new TopologicalSort(G);

// Act
var order = sorter.Order().ToList();

// Assert
Assert.IsTrue(order.SequenceEqual(new[] { 0, 1, 2, 3 }));
}
}

半导体产线示例在半导体制造中，拓扑排序可用于优化工序调度。例如，晶圆制造可能涉及以下工序：

这些工序有依赖关系（如清洗必须在沉积之前）。拓扑排序可生成合理的工序顺序。csharp

public class ManufacturingProcess
{
public int ProcessId { get; set; }
public string ProcessName { get; set; }

public ManufacturingProcess(int processId, string processName)
{
ProcessId = processId;
ProcessName = processName;
}
}

public class ProcessScheduler
{
private Digraph graph;
private ManufacturingProcess[] processes;

public ProcessScheduler(ManufacturingProcess[] processes)
{
this.processes = processes;
graph = new Digraph(processes.Length);
}

public void AddDependency(int fromProcessId, int toProcessId)
{
graph.AddEdge(fromProcessId, toProcessId);
}

public List<ManufacturingProcess> GetSchedule()
{
var sorter = new TopologicalSort(graph);
return sorter.Order().Select(id => processes[id]).ToList();
}
}

// 测试用例
[TestMethod]
public void ProcessScheduler_GetSchedule_ReturnsCorrectOrder()
{
var processes = new[]
{
new ManufacturingProcess(0, "Clean"),
new ManufacturingProcess(1, "Deposition"),
new ManufacturingProcess(2, "Etching"),
new ManufacturingProcess(3, "Testing")
};

var scheduler = new ProcessScheduler(processes);
scheduler.AddDependency(0, 1); // Clean -> Deposition
scheduler.AddDependency(1, 2); // Deposition -> Etching
scheduler.AddDependency(2, 3); // Etching -> Testing

var schedule = scheduler.GetSchedule();

Assert.IsTrue(schedule.Select(p => p.ProcessName).SequenceEqual(new[] { "Clean", "Deposition", "Etching", "Testing" }));
}

四、半导体产线综合示例以下是一个综合示例，结合红黑树、布谷鸟哈希表和拓扑排序，模拟贴片机和测试机的协同工作场景。场景描述在半导体贴片机（SMT）和测试机协同系统中：

综合代码示例csharp

public class SmtAndTestSystem
{
private RedBlackTree<int, WaferTestData> testDataStore;
private CuckooHashTable<int, ComponentPlacement> componentLookup;
private ProcessScheduler scheduler;

public SmtAndTestSystem(ManufacturingProcess[] processes, int hashTableCapacity)
{
testDataStore = new RedBlackTree<int, WaferTestData>();
componentLookup = new CuckooHashTable<int, ComponentPlacement>(hashTableCapacity);
scheduler = new ProcessScheduler(processes);
}

public void AddTestResult(int waferId, double defectRate, DateTime testTime)
{
testDataStore.Put(waferId, new WaferTestData(waferId, defectRate, testTime));
}

public WaferTestData GetTestResult(int waferId)
{
return testDataStore.Get(waferId);
}

public void AddComponent(int componentId, double x, double y)
{
componentLookup.Put(componentId, new ComponentPlacement(componentId, x, y));
}

public ComponentPlacement GetComponent(int componentId)
{
return componentLookup.Get(componentId);
}

public void AddProcessDependency(int fromProcessId, int toProcessId)
{
scheduler.AddDependency(fromProcessId, toProcessId);
}

public List<ManufacturingProcess> GetProcessSchedule()
{
return scheduler.GetSchedule();
}
}

// 测试用例
[TestMethod]
public void SmtAndTestSystem_IntegratedTest_ReturnsCorrectResults()
{
var processes = new[]
{
new ManufacturingProcess(0, "Clean"),
new ManufacturingProcess(1, "SMT Placement"),
new ManufacturingProcess(2, "Testing")
};

var system = new SmtAndTestSystem(processes, 10);

// 添加测试数据
system.AddTestResult(1001, 0.05, DateTime.Now);
system.AddTestResult(1002, 0.03, DateTime.Now);

// 添加元件位置
system.AddComponent(2001, 10.5, 20.5);
system.AddComponent(2002, 15.0, 25.0);

// 添加工序依赖
system.AddProcessDependency(0, 1); // Clean -> SMT Placement
system.AddProcessDependency(1, 2); // SMT Placement -> Testing

// 测试查询
var testResult = system.GetTestResult(1001);
var component = system.GetComponent(2001);
var schedule = system.GetProcessSchedule();

// 验证
Assert.AreEqual(0.05, testResult.DefectRate);
Assert.AreEqual(10.5, component.X);
Assert.IsTrue(schedule.Select(p => p.ProcessName).SequenceEqual(new[] { "Clean", "SMT Placement", "Testing" }));
}

五、总结与注意事项

如果您需要更深入的某算法分析、其他数据结构的实现，或针对特定半导体设备（如探针测试机、晶圆切割机）的定制代码，请进一步说明，我将提供更详细的示例！