Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing (Infineon, U. Padova et al.)


A new technical paper titled "Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing" was published by researchers at Infineon Technologies, University of Padova and University of Bologna. Abstract "In the semiconductor sector, due to high demand but also strong and increasing competition, time to market and quality are key factors in securing significant marke... » read more

High-Quality Data Needed To Better Utilize Fab Data Streams


Fab operations have wrestled with big data management issues for decades. Standards help, but only if sufficient attention to detail is taken during collection. Semiconductor wafer manufacturing represents one of the most complex manufacturing processes in the world. With each generation of process improvement comes more sophisticated fab equipment, new process recipes, and exponential incre... » read more

Quantifying The PFAS Impact In ICs Manufacturing (Harvard University)


A new technical paper titled "Modeling PFAS in Semiconductor Manufacturing to Quantify Trade-offs in Energy Efficiency and Environmental Impact of Computing Systems" was published by researchers at Harvard University and Mohamed Bin Zayed University of AI (MBZUAI). "The electronics and semiconductor industry is a prominent consumer of per- and poly-fluoroalkyl substances (PFAS), also known a... » read more

Overview Of 103 Research Papers On Automatic SEM Image Analysis Algorithms For Semiconductor Defect Inspection (KU Leuven, Imec)


A new technical paper titled "Scanning electron microscopy-based automatic defect inspection for semiconductor manufacturing: a systematic review" was published by researchers at KU Leuven and imec. "We identified, categorized, and discussed automatic defect inspection algorithms that analyze scanning electron microscopy (SEM) images for semiconductor manufacturing (SM). This is a topic of c... » read more

Secure Handling Of Financial Data In Manufacturing


Experts at the Table: Semiconductor Engineering sat down to discuss the advantages associated with linking financial data with manufacturing data analytic platforms, real security challenges and the best uses for AI/ML methods, with Dieter Rathei, CEO of DR Yield; Jon Holt, senior director of product management at PDF Solutions, Alex Burlak, vice president of advanced analytics and test at p... » read more

Cutting IC Manufacturing Costs By Combining Data


Experts at the Table: Semiconductor Engineering sat down to discuss the benefits of incorporating financial data into fab floor decision-making, including what kind of cost data is most useful, with Dieter Rathei, CEO of DR Yield; Jon Holt, senior director of product management at PDF Solutions, Alex Burlak, vice president of advanced analytics and test at proteanTecs; and Dirk de Vries, techni... » read more

EUV’s Future Looks Even Brighter


The rapidly increasing demand for advanced-node chips to support everything-AI is putting pressure on the industry's ability to meet demand. The need for cutting-edge semiconductors is accelerating in applications ranging from hyperscale data centers powering large language models to edge AI in smartphones, IoT devices, and autonomous systems. But manufacturing those chips relies heavily on ... » read more

Transformational Opportunities Coming To Semiconductor Manufacturing


During the GSA US Executive Forum in September 2024, a panel discussion brought together Marco Chisari, EVP from Samsung Semiconductor, Jeff Howell, Global VP for High Tech at SAP, and John Kibarian, CEO of PDF Solutions. The purpose of the discussion was to compare and contrast the perspectives from one of the largest global semiconductor companies with that of the most widely used enterpri... » read more

Simulation Closes Gap Between Chip Design Optimization And Manufacturability


Simulation is playing an increasingly critical and central role throughout the design-through-manufacturing flow, fusing together everything from design to manufacturing and test in order to reduce the number and cost of silicon respins. The sheer density of modern chips, combined with advanced packaging techniques like 3D stacking and heterogeneous integration, has made iterative physical p... » read more

Using AI In Semiconductor Inspection


AI is exceptionally good at spotting anomalies in semiconductor inspection. The challenge is training different models for different inspection tools and topographies, and knowing which model to use at any particular time. Different textures in backgrounds are difficult for traditional algorithms, for example. But once machine learning models are trained properly, they have proven effective in ... » read more

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