Artificial Intelligence is Being Applied to Electronics and Semiconductor Manufacturing Industries

It can improve production quality, optimize processes, and identify defective parts faster to reduce waste costs.

Applications Overview

The electronics and semiconductor industry are fast growing industries. They are the industries handling the production of complex electronic devices. With the trend of lightweighting devices, the components of circuit boards are becoming smaller, making it difficult to detect small changes between small parts as well as increasing the difficulty of inspection.

Here we will explore how artificial intelligence can be applied to electronics and semiconductor manufacturing industries, and how it can improve production efficiency and reduce cost waste.

Challenges of AOI

PCB Defect Detection

AOI equipment is now commonly used for PCB defect detection. Rules-based machine vision is difficult to define all defective issues clearly. It is likely to miss or determine products that are repaired and ready for shipment as defective, resulting in increased labor costs and workload.

Image recognition technology in deep learning
Reduce the Bias of Defect Definition and Increase the Accuracy of AOI Inspection

You can use AI for image analysis to classify defects that cannot be identified by AOI, reducing the burden on inspectors and improving factory productivity.

Solution: Nilvana Vision Studio + Nilvana Vision Inference Runtime + AIoT Edge

PCBA Component Inspection

The assembled board is called PCBA. After the assembly is completed, it has to be verified in detail whether the ICs and components are correctly installed in the right position to ensure proper function.

Most of the production lines use manual visual inspection to check the correctness of the assembly. Given the wide variety of electronic components on PCBAs, long-term inspection is likely to cause eye fatigue.

Image recognition technology in deep learning
Accelerating Production and Improving Product Yield

Solution: Nilvana Vision Studio + Nilvana Vision Inference Runtime + AIoT Edge

The Best Process Parameters for SMT Solder Paste Printing

Solder paste printing is the first step in the SMT process, which involves printing the paste on the pads of the PCB parts to be soldered. The position, volume, and amount of solder paste will affect the quality of the subsequent parts to be soldered.

Most manufacturers use Solder Paste Inspection machines to monitor the paste printing afterwards.

Prevent Errors and Enhance Process Quality

Simulate process results and recommend parameters through machine learning.

Improve quality by using machine learning to analyze historical data on SPI and printed solder paste to recommend the best settings for solder paste printing parameters during the production process.

Solution: AI Starter Kit + AIoT Edge

SMT Pre and Post Solder Defect Inspection

The SMT process involves gently adhering electronic components, such as resistors, capacitors, transistors, and integrated circuits, to a printed circuit board with solder paste, and then melting the solder paste on the pad in a solder reflow oven to secure it to the printed circuit board.

Prevent Welding Defects and Reduce Cost Waste of Remedial Work

Artificial intelligence is excellent in distinguishing defects and the difference between before and after welding. Through AI application, it can reduce the misjudgment of machine vision in judging shadows or reflections as defects.

Solution: Nilvana Vision Studio + Nilvana Vision Inference Runtime + AIoT Edge

Semiconductor Wafer Quality Control Monitoring

In the wafer manufacturing process, it is typical to use AOI equipment for quality inspection in order to maintain a stable yield of wafer production.

However, the range of potential defects on wafers is very wide and subtle, and they are randomly distributed on the wafer. It is difficult to for traditional optical inspection and often requires human intervention to perform a second re-inspection.

The Perfect Combination of AOI and AI

Through AOI equipment, samples and defect photos are collected for AI to train better models and re-deploy them to the equipment to enhance the inspection accuracy. This will effectively solve the industry's dependence on manpower for quality inspection, improve product quality, and enhance the value of the industry.

Solution:Nilvana Vision Studio + Nilvana Vision Inference Runtime + AIoT Edge

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