tag: deep learning

Deep Learning Delivers Fast, Accurate Solutions For Object Detection In The Automated Optical Inspection Of Electronic Assemblies
By Charlie Zhu - 07 Dec, 2021 - Comments: 0

When automated optical inspection (AOI) works, it is almost always preferable to human visual inspection. It can be faster, more accurate, more consistent, less expensive, and it never gets tired. However, some tasks that are very simple for humans are quite difficult for machines. Object detection is an example. For example, shown an image containing a cat, a dog, and a duck, a human can insta... » read more

Case Study — Deep Learning For Corner Fill Inspection And Metrology On Integrated Circuits
By CyberOptics - 07 Dec, 2021 - Comments: 0

CyberOptics utilized deep learning to accurately inspect the corner fill on integrated circuits (ICs) produced by a large memory supplier. Traditional methods of inspection showed limitations in their ability to entirely detect the presence and absence of fill, indicating that a more advanced approach was necessary. CyberOptics drew on its large pool of algorithm and neural network expertise to... » read more

Accelerating Inference of Convolutional Neural Networks Using In-memory Computing
By Technical Paper Link - 09 Nov, 2021 - Comments: 0

Abstract: "In-memory computing (IMC) is a non-von Neumann paradigm that has recently established itself as a promising approach for energy-efficient, high throughput hardware for deep learning applications. One prominent application of IMC is that of performing matrix-vector multiplication in (1) time complexity by mapping the synaptic weights of a neural-network layer to the devices of an ... » read more

Enablers And Barriers For Connecting Diverse Data
By Anne Meixner - 09 Nov, 2021 - Comments: 0

More data is being collected at every step of the manufacturing process, raising the possibility of combining data in new ways to solve engineering problems. But this is far from simple, and combining results is not always possible. The semiconductor industry’s thirst for data has created oceans of it from the manufacturing process. In addition, semiconductor designs large and small now ha... » read more

Energy-efficient memcapacitor devices for neuromorphic computing
By Technical Paper Link - 03 Nov, 2021 - Comments: 0

Abstract Data-intensive computing operations, such as training neural networks, are essential for applications in artificial intelligence but are energy intensive. One solution is to develop specialized hardware onto which neural networks can be directly mapped, and arrays of memristive devices can, for example, be trained to enable parallel multiply–accumulate operations. Here we show that ... » read more

Fabs Drive Deeper Into Machine Learning
By Anne Meixner - 07 Sep, 2021 - Comments: 0

Advanced machine learning is beginning to make inroads into yield enhancement methodology as fabs and equipment makers seek to identify defectivity patterns in wafer images with greater accuracy and speed. Each month a wafer fabrication factory produces tens of millions of wafer-level images from inspection, metrology, and test. Engineers must analyze that data to improve yield and to reject... » read more

NN-Baton: DNN Workload Orchestration & Chiplet Granularity Exploration for Multichip Accelerators
By Technical Paper Link - 14 Jun, 2021 - Comments: 0

"Abstract—The revolution of machine learning poses an unprecedented demand for computation resources, urging more transistors on a single monolithic chip, which is not sustainable in the Post-Moore era. The multichip integration with small functional dies, called chiplets, can reduce the manufacturing cost, improve the fabrication yield, and achieve die-level reuse for different system scales... » read more

Applications, Challenges For Using AI In Fabs
By Mark LaPedus - 14 Apr, 2021 - Comments: 0

Experts at the Table: Semiconductor Engineering sat down to discuss chip scaling, transistors, new architectures, and packaging with Jerry Chen, head of global business development for manufacturing & industrials at Nvidia; David Fried, vice president of computational products at Lam Research; Mark Shirey, vice president of marketing and applications at KLA; and Aki Fujimura, CEO of D2S. Wh... » read more

A Novel Complementary Architecture of One-time-programmable Memory and Its Applications as Physical Unclonable Function (PUF) and One-time Password
By Technical Paper Link - 31 Mar, 2021 - Comments: 0

Abstract "For the first time, we proposed a 2T complementary architecture of one-time-programmable memory (OTP) in a foundry logic CMOS chip. It was then used to realize the PUF (Physical unclonable function), and the combination with the AI technology to provide a one-time password capability. At first, an OTP was developed based on a novel 2T CMOS unit cell. The experimental results show t... » read more

Deep Learning (DL) Applications In Photomask To Wafer Semiconductor Manufacturing
By eBeam Initiative - 17 Mar, 2021 - Comments: 0

The Survey: 2021 Deep Learning Applications List by eBeam Initiative members is a list of current deep learning efforts that are being used in photomask to wafer semiconductor manufacturing. Examples come from ASML, D2S, Fraunhofer IPMS, Hitachi High-Tech Corporation, imec, Siemens Industries Software, Inc., Siemens EDA, STMicroelectronics, and TASMIT. Published by the eBeam Initiative Membe... » read more

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