Category: Information

Internship Visit – BHE – Infineon – Where Theory Meets the Fabrication Floor

Today, we had the opportunity to conduct an industrial internship (LI) visit to the newly launched Infineon Technologies plant in Kulim, a milestone not only for our student but also for our faculty–industry engagement in the semiconductor domain.

The visit was led by Dr. Aqilah Othman, Head of the Department of Engineering, FTKEE, and was part of our continuous effort to monitor, assess, and enrich students’ industrial training experiences, in line with EAC requirements and our broader academic objectives.

Student Presentation: Learning Beyond the Classroom

The visit began with a presentation by our trainee, Anum, who is currently undergoing her six-month industrial placement at Infineon Kulim.

Throughout her internship, Anum was tasked with developing an inventory management system—a real, production-relevant problem. Her work involved:

      1. Barcode scanning of reticle wafers

      2. Generating structured log files

      3. Storing and managing data using an SQL database

      4. Developing a web-based dashboard for monitoring and traceability

For context:
A reticle wafer is a critical component in semiconductor fabrication, used in the photolithography process. It acts as a patterned mask that transfers circuit designs onto wafers. Accurate tracking and inventory control are essential to ensure process integrity, yield, and traceability—making Anum’s system highly relevant to real manufacturing operations.

What stood out was not just the technical execution, but her ability to connect software development, database management, and manufacturing workflows—a clear example of how engineering theory transforms into industrial practice.

Following the student presentation, we had a meaningful discussion with Mr. Jeffery, Managing Director of Infineon Kulim.

Mr. Jeffery has been serving as CEO@Faculty for FTKEE, UMPSA, and has been extremely active in driving semiconductor-related initiatives with the faculty. Among the notable engagements:

      1. Industry input into semiconductor-focused workshops

      2. Support for elective development

      3. Ongoing collaboration to align curriculum with industry needs

One of the key outcomes of this visit was guidance for FTKEE to structure and strengthen elective offerings, particularly in the areas of:

      1. VLSI Design

      2. Analog IC Design

      3. Digital Design

      4. Semiconductor Technology & Manufacturing

These directions are timely and crucial as FTKEE continues to position itself as a strong contributor to Malaysia’s semiconductor talent pipeline.

Into the Fab: A Walk Through Advanced Manufacturing

Towards the end of the visit, we were brought into the fabrication area, where we witnessed state-of-the-art semiconductor manufacturing facilities.

Dressed in full cleanroom suits, the experience was both impressive and nostalgic. It brought back memories of my own early industry exposure at Agilent Technologies back in 2001, where I had the chance to observe etching processes for LED wafers, including die placement.

At that time, white LEDs were still a technological challenge. I vividly remember reading textbooks that explained why LEDs existed in almost every color—except white. Producing white light required combining multiple wavelengths, making the process complex and expensive.

Fast forward to today: white LEDs are everywhere—in our homes, streets, and devices. Standing in Infineon’s fab yesterday was a powerful reminder of how engineering innovation evolves from complexity to everyday reality.

For a better understanding of what’s happening in a litography machine (the one that is always in a cleanroom facilities), refer to the link below:-

A nice explaination about litography process by Verastium

Closing Reflections: Why Internship Matters

This visit reinforced a belief I hold strongly:

Industrial training is one of the most important phases of an engineering student’s journey.

It is during internship that students:

  • Experience real job training

  • Understand industrial constraints

  • Learn how theory translates into practice

  • Discover what it truly means to be an engineer

Seeing Anum confidently present her work, engaging with engineers, and contributing to a real production environment affirmed the value of these experiences.

As educators, our role is not only to teach theory—but to create pathways where students can live the engineering profession before they graduate.

And visits like this remind us why that effort is always worth it.

 

 

BTE1522 DRE 2213 – Project Submission

Well done everyone!

Students from BTE 1522 and DRE2213 presented their final projects, and the outcomes were impressive =).

What began at the start of the course as an introduction to beginner Python programming through a simple Pygame slider game (Pygame assignments) has now evolved into fully functional sensor-based systems using Raspberry Pi and the BME280 environmental sensor.

This transition from a purely digital game environment to a real-world, physically embedded system, was intentional =).

By first grounding students in Python fundamentals (variables, loops, conditionals, event handling, and logic flow) through game development, students were able to focus later on how their code interacts with the physical world.

BTE1522 – IMU Data Collection

Learning Python Through Motion, Data, and Innovation: BTE1522 Project Showcase

Students from BTE1522 – Innovation (Python) recently presented their final projects, and the results clearly demonstrated how hands-on, sensor-driven learning can elevate Python programming skills.

In this course, students worked with the MPU6050 motion sensor on the LilEx 5 platform, moving beyond basic scripting to build end-to-end data-driven systems involving sensing, storage, and visualization.

Project Focus

Each student group was tasked to:

      1. Read motion data from the MPU6050 using Python

      2. Design and conduct structured data collection for different human movements

      3. Store the data in a database of their choice

      4. Build a dashboard to visualize and interpret the collected data

This workflow mirrors real-world IoT and data engineering pipelines.

Movement-Based Data Collection

Students collected sensor data based on well-defined criteria, including:

      1. Standing

      2. Leaning left and right (roll)

      3. Bending forward and backward (pitch)

      4. Lying down

They carefully controlled parameters such as:

      1. Sampling rate

      2. Timeframe per movement

      3. Sensor placement

      4. Calibration procedures

This encouraged students to think critically about data quality, consistency, and repeatability, not just code correctness.

From Raw Sensor Data to Insight

Using Python, students transformed raw accelerometer and gyroscope readings into structured datasets. They then explored different tools and platforms to:

      1. Build databases

      2. Create dashboards for visualization and interpretation

Through this process, students learned that innovation is not only about building something new, but also about making data understandable and useful.

Physical Embodiment as a Learning Strategy

Similar to DRE2213, this course emphasized learning through physical embodiment. Students could directly observe how body movement affected sensor readings, reinforcing their understanding of:

        1. Coordinate axes

        2. Sensor fusion concepts

        3. Time-series data behavior

By linking physical motion to Python code and visual dashboards, abstract programming concepts became concrete and intuitive.

Overall, student performance was very satisfying. Good job everyone.

The projects demonstrated strong engagement, creativity, and a growing confidence in Python programming.

The project videos embedded below highlight how students applied Python not just as a programming language, but as a tool for sensing, data analysis, and innovation.

DRE2213 – BM280 Data Monitoring – SULAM

Project Highlights

In their final projects, DRE2213 students successfully demonstrated:

  1. Closed-loop sensing systems
    Integrating the BME280 sensor with Raspberry Pi using Python, where sensor readings triggered real-time responses such as LEDs and buzzers.

  2. Data logging and storage
    Students independently explored multiple database solutions:

      • Firebase

      • Google Sheets / Spreadsheet-based logging
        This showed strong initiative and adaptability beyond what was explicitly taught.

  3. Dashboard development and visualization
    A wide range of dashboard approaches were implemented, including:

      • HTML-based dashboards

      • Adafruit IO

      • Flask web applications

      • Streamlit dashboards

Each solution reflected different design choices, yet all achieved the same goal: making sensor data meaningful, visible, and interactive.

https://youtube.com/shorts/KYIy1CRj6Nk?si=2cHH3TGO1eFjDp9y

https://www.youtube.com/shorts/KYIy1CRj6Nk

https://youtube.com/shorts/KYIy1CRj6Nk?si=2cHH3TGO1eFjDp9y

 

 

Learning Through Physical Embodiment

What stood out most was how BTE1522 and DRE2213 students connected abstract Python code to tangible outcomes. Seeing a buzzer activate, an LED respond, or a dashboard update in real time helped students understand what their code is doing, not just whether it runs.

This combination of:

      1. Digital embodiment (game-based learning with Pygame), and

      2. Physical embodiment (real sensors, real data, real feedback)

proved to be a powerful approach in helping students grasp programming concepts more deeply and confidently.

Reflection

The quality of the projects and the variety of technical approaches exceeded expectations. Students demonstrated not only programming skills, but also problem-solving, system integration, and creativity.

The embedded project videos below showcase their work and reflect a learning journey that truly bridges Python programming and real-world applications.

 

Nurul Hazlina – Feb 4th

Python Programming for Beginners – BHE 2526 Sem I

Explore Programming with the Slider Game

  1. Learn basic Python via Slider game
  2. Installation of Computer visual code (cvs) for advance application in image and data processing

Today students from BHE 25/26 I participated in a hands-on programming training session designed to make learning Python both engaging and intuitive. The session ran from 8:30 AM to 12:45 PM and combined game-based learning with practical tool setup for future advanced applications.

The training introduced students to core programming concepts through an interactive Slider Game, followed by the installation of Visual Studio Code (VS Code) to prepare them for more advanced work in image and data processing.

Session 1: Learning Python Basics Through the Slider Game

Time: 8:30 AM – 12:30 PM

Instead of starting with abstract syntax and long code examples, students learned Python fundamentals by building and modifying a simple Slider Game. This approach allowed concepts to emerge naturally through interaction and experimentation.

Through guided activities, students progressively explored:

      1. Variables – storing and updating player positions, scores, and timers

      2. Mathematical operations – controlling movement speed, scoring, and boundaries

      3. Control structures (loops & conditionals) – managing enemy movement, collisions, and game flow

      4. Event handling – responding to keyboard inputs for real-time player control

      5. Data structures – using lists to manage multiple enemies and game objects

      6. Functions – organizing code for clarity and reusability

      7. Debugging and logical thinking – testing, observing outcomes, and refining logic

By the end of the session, students were not just reading code—they were seeing their code come alive on the screen.

Learning by Seeing and Doing: Digital & Physical Embodiment

A key strength of the Slider Game approach is embodied learning. As students interacted with the game—moving the player, triggering collisions, or adjusting timing—they could immediately visualize the effect of each programming concept.

This form of digital embodiment supports deeper understanding:

      1. Students test hypotheses by changing values and logic

      2. Immediate visual feedback reinforces correct reasoning

      3. Errors become learning opportunities rather than frustrations

By observing how their code directly influences game behavior, students developed stronger intuition about how programming logic works in real systems.

Session 2: Preparing for Advanced Applications

Time: 12:30 PM – 12:45 PM

In the final segment, students were guided through the installation and setup of Visual Studio Code (VS Code)—a widely used development environment for professional and academic programming. This is as part of their preparation for their upcoming flying professor’s class in March 2026.

This step prepares students for:

      1. Advanced Python development

      2. Image processing and computer vision

      3. Data analysis and visualization

      4. Future projects involving AI and intelligent systems

Introducing VS Code early helps students transition smoothly from learning concepts to building more complex, real-world applications.

 

Looking Ahead

This training demonstrated that game-based and embodied learning can significantly enhance how students grasp programming fundamentals. By combining interaction, visualization, and hands-on practice, students build confidence, curiosity, and problem-solving skills—key foundations for future work in computing and engineering.

Moving forward, similar sessions will continue to explore how interactive digital environments and intelligent scaffolding can further support meaningful learning in programming education.

Further UMPSA STEM Lab work on Slider Game and Digital Embodiment can be accessed here.