TSMC Interview Tips: VO Questions Answers & OA Interviews
TSMC's interview process typically begins with a resume screening designed to assess a candidate's professional background and relevant project experience. Candidates who pass the resume screening are invited to a first round of phone or video interviews, which are usually conducted by the hiring department head or a senior engineer, focusing on specialized knowledge, technical depth, and understanding of the semiconductor industry.
Candidates who successfully pass the first round of interviews will move on to a second round of on-site or video interviews, which usually involves multiple department heads or team members for a more comprehensive assessment. Interviews typically delve into the technical details of the area of expertise, challenges and solutions from past projects, and may involve situational questions to assess the candidate's problem solving skills and stress tolerance. Interviewers will pay close attention to the candidate's communication skills, teamwork and work ethic, as these are critical in the highly specialized and well-defined semiconductor industry.
The whole interview process pays great attention to the professional knowledge and practical ability of the candidates, and also values whether the candidates have a rigorous and meticulous work attitude and willingness to continue learning.
TSMC uses OA assessments as a key entry point for screening talent for its technical positions. This OA rigorously evaluates coding skills, problem-solving abilities, and semiconductor knowledge, next shared from CSOAsupport OA Interview Service, The team has compiled insights on TSMC interview question types and programming examples so that you can effectively improve your interview response.
OA Interview Question 1 with Solutions
Analyze the lighting of a high-rise building complex and simplify the complex to a two-dimensional grid, where 1 represents an area with direct sunlight and 0 represents an area that is shaded. Your task is to compute the total lighting rate (i.e., the proportion of all sunlit areas to the total area) for the entire complex, and at the same time to find the largest contiguous rectangular area that is unshaded and return its area.
Given the following building lighting diagram:
[1, 0, 1, 1],
[1, 1, 1, 1],
[0, 1, 1, 0],
[1, 1, 1, 1]
]
Your program should output::
- Overall Sunlight Rate: 0.75
- Largest Unobstructed Rectangular Area: 6
Ideas for solving the problem
This question can be broken down into two sub-problems:
- Calculate the total light percentage: This one is relatively simple. We just need to traverse the entire 2D grid, count the number of 1's, and divide by the total number of cells.
- Find the largest continuous unobstructed rectangular area: This is the heart of the problem and a classic algorithmic problem. We can solve it using the histogram method (Largest Rectangle in Histogram).
- First, we can think of each row as the bottom of a histogram, and the height of the histogram is determined by the current position and the number of consecutive 1's above it.
- We traverse down through the rows one by one. At each row, we create an array of heights, where heights[j] stores the number of consecutive 1's looking up from the current row.
- If grid[i][j] is 1, then heights[j] is equal to heights[j] + 1.
- If grid[i][j] is 0, then heights[j] is cleared to zero because the continuous region is broken here.
- For the array of heights generated for each row, we can use the classic "maximum rectangle area in histogram" algorithm. This algorithm usually uses a monotonically increasing stack to efficiently find the largest rectangle for each height.
- After traversing each row and calculating the maximum rectangular area for the current row, we keep updating the global maximum area until we have traversed the entire matrix.
def largest_rectangle_in_histogram(heights).
"""
Calculate the area of the largest rectangle in the histogram
"""
stack = [-1]
max_area = 0
for i, h in enumerate(heights):
while stack[-1] ! = -1 and heights[stack[-1]] >= h: height = heights[stack.pop(]].
height = heights[stack.pop()]
width = i - stack[-1] - 1
max_area = max(max_area, height * width)
stack.append(i)
while stack[-1] ! = -1.
height = heights[stack.pop()]
width = len(heights) - stack[-1] - 1
max_area = max(max_area, height * width)
return max_area
def analyze_sunlight(grid).
if not grid or not grid[0].
return {
"Overall Sunlight Rate": 0, "Largest Unobstructed Rectangular Area": 0, "Overall Sunlight Rate": 0
"Largest Unobstructed Rectangular Area": 0
}
rows, cols = len(grid), len(grid[0])
total_cells = rows * cols
sunlit_cells = 0
max_area = 0
heights = [0] * cols
for j in range(cols).
if grid[i][j] == 1.
sunlit_cells += 1
heights[j] += 1
else: sunlit_cells += 1: heights[j] += 1
heights[j] = 0
# Calculate the maximum rectangle area in the current row using the histogram algorithm
max_area = max(max_area, largest_rectangle_in_histogram(heights))
overall_sunlight_rate = sunlit_cells / total_cells
return {
"Overall Sunlight Rate": overall_sunlight_rate,
"Largest Unobstructed Rectangular Area": max_area
}
# example call
sunlight_map = [
[1, 0, 1, 1], [1, 1, 1], [
[1, 1, 1, 1]
]
result = analyze_sunlight(sunlight_map)
print(result)</xmp
OA Interview Question 2 with Solutions
Ideas for solving the problem
This problem is a classic single-source shortest path problem, identical in algorithmic principle to the previous one. The most common and effective way to solve this type of problem is to use Dijkstra's algorithm.- Dijkstra's algorithm: The algorithm is applicable to directed graphs with non-negative weights and efficiently finds the shortest path from a source node to all other nodes.
- data structure::
- You need an adjacency table to represent the graph, where each key is a node and the values are a list containing (neighbor node, delay) tuples.
- A minimal heap (or priority queue) is used to store the nodes to be processed. The heap stores a tuple of (current shortest time, nodes), and the node with the shortest time is taken out for processing each time.
- An array of distances is also needed to record the shortest time from the source node to all other nodes, with the initial value set to infinity (except for the source node).
- algorithmic step::
- Initialize the distance array by setting the source node distance to 0 and the other nodes to infinity.
- Put (0, source) into the minimal heap.
- When the heap is not empty, the loop performs the following actions:
- Remove the node u with the smallest distance from the heap.
- If the current distance of u is greater than the shortest known distance, it is skipped (to prevent double counting).
- Iterate over all neighbors v of u. If the time to reach v through u (dist[u] + delay) is less than the known dist[v], update dist[v] and put (new time, v) into the heap.
- Results processing::
- At the end of the algorithm, the distance array contains the shortest propagation times of all nodes.
- Iterate through this array, replacing all infinity values with -1, indicating that these sites are unreachable from the source.
import heapq
def analyze_defect_propagation(graph, source).
# Initialize distances to infinity for all nodes
distances = {node: float('inf') for node in graph}
# Distances for source nodes are 0
distances[source] = 0
# Priority queue, store (distances, nodes)
priority_queue = [(0, source)]
while priority_queue.
# Take the node with the shortest current distance
current_distance, current_node = heapq.heappop(priority_queue)
# If current_distance is greater than the known shortest distance, there is already a better path, skip it
if current_distance > distances[current_node].
continue
# Iterate through all neighbors of the current node
for neighbor, weight in graph.get(current_node, []): distance = current_distance + weight
distance = current_distance + weight
# If a shorter path is found
if distance < distances[neighbor].
distances[neighbor] = distance
heapq.heappush(priority_queue, (distance, neighbor))
# Set unreachable node distance to -1
result = [distances.get(node, -1) if distances.get(node, float('inf')) ! = float('inf') else -1 for node in sorted(graph.keys())]
return result
# Example input
graph_input = {
2: [(3, 3)],
3: []
}
source_node = 0
output = analyze_defect_propagation(graph_input, source_node)
print(f "Input: {graph_input}, source node: {source_node}")
print(f "Propagation time: {output}")</xmp
VO Interview Programming Questions
Ideas for solving the problem
This question examines basic matrix traversal and numerical processing skills, essentially a 2D Convolution of a matrix, but the kernel used here is a mean filter.- Creating a new matrix: First, create a new matrix with the same dimensions as the original matrix to store the smoothed results. Remember not to operate directly on the original matrix, otherwise the old values that have been modified will be used in the calculation of the next cell, leading to incorrect results.
- Iterate through each cell: Use nested loops to iterate through each cell (i, j) of the original matrix.
- Calculate the local mean::
- For each cell (i, j), you need to consider its neighbors in 8 directions around it, as well as itself, for a total of 9 positions.
- Define a summation variabletotal_sum and a count variablecountThe
- Use two inner loops to traverse all possible neighbors from i-1 to i+1 and from j-1 to j+1.
- When accessing each neighbor (nx, ny), a boundary check is performed to ensure that both nx and ny are within the valid range of the matrix.
- If the neighbor is valid, add its value to total_sum and add count by one.
- Assigning values to the new matrix: Calculate total_sum / count and assign the result to the corresponding (i, j) position in the new matrix.
- Return results: When the traversal is complete, the new matrix is returned as the final result.
def smooth_film_thickness(thickness_matrix).
if not thickness_matrix or not thickness_matrix[0]: if not thickness_matrix or not thickness_matrix[0].
return []
rows = len(thickness_matrix)
cols = len(thickness_matrix[0])
# Create a new matrix to store the results, avoiding modification on the original matrix
smoothed_matrix = [[0] * cols for _ in range(rows)]
for i in range(rows).
for j in range(cols).
total_sum = 0
total_sum = 0
# Iterate over the current cell and its 8 neighbors.
for x in range(i - 1, i + 2): for y in range(j - 1, j + 2): for
for y in range(j - 1, j + 2).
# Boundary check
if 0 <= x < rows and 0 <= y 0.
smoothed_matrix[i][j] = total_sum // count
return smoothed_matrix
# Example Input
thickness = [
[10, 20, 30], [40, 50, 60], [
[70, 80, 90]
]
# Call the function and print the result
output = smooth_film_thickness(thickness)
print(output)</xmp
system debugging
System debugging or debugging is a crucial assessment part of the TSMC interview, which is used to examine the candidate's ability to handle complex systemic problems. In this round of interviews, the interviewer will provide a simulated wafer test script or production scheduling code and ask the candidate to identify the logic pitfalls or performance bottlenecks in it and fix them in order to facilitate the overall operational efficiency of the system.
system design
In TSMC's interview, the system design session is used to examine the candidate's structural design ability. It is different from other software companies in that TSMC's system design mainly centers on semiconductor production scenarios to start the questioning, which includes: wafer production scheduling system, wafer testing process, data analysis platform, etc. The limitations of the hardware conditions and the overall operational efficiency of the system need to be considered at the same time.
For example, design a wafer test scheduling system that allows the system to manage multiple test machines and batches of wafers with different priorities simultaneously, taking into account factors such as machine utilization, test time optimization and error handling.
behavioral interview
TSMC's behavioral interview is mainly through multiple dimensions of questions to observe the candidate's career development direction, values, stress tolerance and teamwork ability in previous projects and other comprehensive elements, will not be involved in specific technical issues, but in the project experience to explore the link, sometimes the interviewer will ask specific questions to test the authenticity of the projects mentioned in the candidate's resume, so this link must be serious! Prepare 2 to 3 stories that make sense, and try to convince the interviewer from the details and data.
Core Concerns for SDE Positions
For TSMC's Software Development Engineer (SDE) job interviews, candidates are required to have an in-depth understanding of the fundamentals of operating systems, data structures and algorithms, computer networks, and databases, as well as mastery of at least one mainstream programming language, such as C++ or Python. in addition, since TSMC's SDE jobs are often closely aligned with hardware, candidates are expected to have a good understanding of hardware architecture, embedded systems, or automated control. During the interview, special attention needs to be paid to demonstrating problem solving skills and logical thinking rather than just giving the right answer. At the same time, it is important to demonstrate a rigorous and meticulous work ethic and a strong willingness to learn, as TSMC's technology iterates very quickly. When discussing your project experience, you should emphasize your role in the project, the technical challenges you encountered, and how you solved them, as a way to demonstrate your teamwork and stress tolerance.
Interview Prep
For the Embedded Software Engineer interview, candidates need to be prepared with a solid grasp of the C/C++ language, which is the cornerstone of the underlying development, as well as an in-depth understanding of computer architecture and embedded systems, especially the principles of Real Time Operating Systems (RTOS) and Linux.
Additionally, mastery of Python scripting for automation and testing, as well as familiarity with hardware communication protocols such as I2C, SPI, UART, etc. is critical, and lastly, demonstrate an understanding of hardware and strong problem solving skills as much of the work will require you to work directly with hardware for debugging and optimization.
