Next Generation Chip Embedding Technology For High Efficiency Power Modules and Power SiPs


Cost, performance, and package size are some of the key drivers required in the next generation of package interconnect and package structure evolution. Embedding active die into substrates was mainly driven by package miniaturization for communication handheld devices. However, in the case of power modules, miniaturization is not the only driver that enhances the need for embedded die substrat... » read more

Interactive Point-To-Point Resistance Simulations


Point to point (P2P) resistance simulations calculate the effective resistance of the layout traces between points on an IC net trace, and let the designer know that there may be too much parasitic resistance from a particular net trace that would affect the reliability or performance of the circuit. However, traditional P2P simulation runs are time-consuming, and often require multiple iterati... » read more

Preparation Of Geometry Models For Mesh Generation And CFD


Making geometry models suitable for CFD meshing is a time-consuming bottleneck in CFD analysis. We will discuss why and ways to fix the problems. Click here to read more. » read more

A New Era For HPC-Driven Engineering Simulation


Market pressure and technological advancements have rapidly changed the way engineers work. Design engineers increasingly work with larger and more complex models, must conduct more frequent simulation analysis, and iterate more rapidly. Compute constraints, however, often result in engineers limiting model sizes and simulation fidelity, or relying on lengthy, overnight simulation runs. ... » read more

Keyword Transformer: A Self-Attention Model For Keyword Spotting


The Transformer architecture has been successful across many domains, including natural language processing, computer vision and speech recognition. In keyword spotting, self-attention has primarily been used on top of convolutional or recurrent encoders. We investigate a range of ways to adapt the Transformer architecture to keyword spotting and introduce the Keyword Transformer (KWT), a fully... » read more

A Multi-Level Analog IC Design Flow For Fast Performance Estimation Using Template-Based Layout Generators And Structural Models


Analog IC design is a very challenging task as essential information is missing in the early design stages. Because the simulation of larger designs is exceedingly computationally expensive at lower abstraction levels, conservative assumptions are usually applied that often result in suboptimal performances such as area and power consumption. In order to enable both early performance estimates ... » read more

Affordable And Comprehensive Testing Of 3D Stacked Die Devices


Developers of high-end semiconductor products who face manufacturing limitations with respect to die sizes are investing in 3D stacked die technology. These advanced designs already push current design-for-test (DFT) solutions to the limits: tool run time, on-chip area demand, test pattern count, and test time. How then, can designers manage DFT for these new 3D devices? In this paper, we outli... » read more

Deep Learning To Classify And Establish Structure Property Predictions With PeakForce QNM Atomic Force Microscopy


Machine learning and specifically, deep learning, is a powerful tool to establish the presence (or absence) of microstructure correlations to bulk properties with its ability to flesh out relationships and trends that are difficult to establish otherwise. This application note discusses the use of deep learning tools, to explore AFM phase and PeakForce Quantitative Nanomechanics (QNM) im... » read more

Open-Short Normalization Method For A Quick Defect Identification In Branched Traces With High-Resolution Time-Domain Reflectometry


Time-domain reflectometry (TDR) that employs electro-optical sampling affords excellent resolution at the femtosecond level and exhibits a comprehensible impulse waveform, thereby allowing quick defect identification in a single trace. However, it remains challenging to identify a defect in a trace of multiple branches; the TDR waveform is complex. Generally, the TDR waveform of a defective uni... » read more

Methods To Overcome Limited Labeled Data Sets In Machine Learning-Based Optical Critical Dimension Metrology


With the aggressive scaling of semiconductor devices, the increasing complexity of device structure coupled with tighter metrology error budget has driven up Optical Critical Dimension (OCD) time to solution to a critical point. Machine Learning (ML), thanks to its extremely fast turnaround, has been successfully applied in OCD metrology as an alternative solution to the conventional physical... » read more

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