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AI and machine learning have become an integral part of many 21st century industries, and semiconductor manufacturing is no exception.

Artificial Intelligence (AI) has been revolutionising the field of microelectronics manufacturing, particularly in the semiconductor industry. The use of AI and automation has allowed for more efficient and accurate production of semiconductors in fabrication facilities, leading to higher yields and lower costs. In this article, we will discuss the current status of AI in microelectronics manufacturing, specifically its use in the manufacturing process of semiconductors.

Semiconductor fabrication facilities, also known as fabs, are high-tech manufacturing facilities that produce integrated circuits (ICs) or chips. The process of semiconductor manufacturing is highly complex and involves several steps, including photolithography, etching, deposition, and packaging. The entire process is carried out in a cleanroom environment, where even the smallest particle can affect the quality of the final product.


AI and automation have been used in several areas of semiconductor manufacturing, such as process control, defect detection, and yield optimisation. One of the most significant benefits of using AI and automation in the manufacturing process is that it allows for real-time monitoring and adjustment of production processes, leading to higher yields and lower costs.


AI in Process Control

Process control is a critical aspect of semiconductor manufacturing, as even minor variations in the manufacturing process can result in defective products. The use of AI and automation in process control has allowed for more accurate and consistent control of the manufacturing process. AI algorithms can analyse data in real-time, detect anomalies, and adjust the process parameters accordingly. This leads to higher process stability, better process control, and ultimately higher yields.


AI in Defect Detection

Defect detection is another area where AI and automation have been used in semiconductor manufacturing. Traditional defect detection methods involve manual inspection of wafers, which is time-consuming and costly. AI algorithms can analyse images of wafers and detect defects that may be missed by human inspectors. This leads to faster defect detection and higher yields.


AI in Yield Optimisation

Yield optimisation is the process of maximising the number of functional ICs produced per wafer. AI and automation have been used in yield optimisation by analysing data from the manufacturing process and identifying areas where improvements can be made. For example, AI algorithms can analyse data from the manufacturing process and identify trends that may indicate a problem in the manufacturing process. This can lead to adjustments in the manufacturing process that improve yields and reduce costs.


The impact that AI has had on the semiconductor manufacturing industry is profound, and without such precise and cutting edge pieces of software, the semiconductor shortage would have undoubtedly been more severe. AI and machine learning tools allowed fabrication facilities to run at maximum capacity for months on end. Whilst it's clear that we still haven't seen the full extent of the power of AI, it's clear the impact it's having already is incredible.


If you'd like to find out about roles we currently have open in some of the most state of the art fabrication facilities across the USA and Europe, get in touch


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