Ansys, Inc.

Jimin Wen

Norman H Chang

Deqi Zhu

Akhilesh Kumar

Wen-Tze Chuang

Hsiming Pan

Ying-Shiun Li

En-Cih Yang

Wenbo Xia

Karthik Srinivasan

Lang Lin

Hua Chen

Dinesh Kumar Selvakumaran

Jay Prakash Pathak

Xi Chen

Lei Yin

En Cih Yang

Bolong Zhang

Gang Ni

Ying Shiun Li

Ming-Chih Shih

Chia-Wei Chen

A method in one embodiment creates a model of an authentic IC for use in comparisons with counterfeit ICs. The model can be created by determining a first or initial set of points of interest (POIs) on the simulated physical (e.g., gate level) layout and simulating side channel leakage from each POI and then expanding the size of the POI and repeating the simulation and comparing successive simulation results (between successive sizes of POIs for a given POI) to determine if a solution for the size of the POI has converged. The final POIs are then processed in a simulation that can use multiple payloads (e.g., cryptographic data) over the entire set of final POIs, and the resulting data set can be used to create the model.

Adaptive leakage impact region detection and modeling for counterfeit chips detection

A specification for a semi-conductor chip is received. The specification specifies a set of photomasks associated with a metal layer of the semi-conductor chip. Multiple portions of an area of the metal layer are identified. A respective image is generated for each portion of the area based on the photomasks. A respective drawn density of metal wires for each portion of the area is calculated. A trained machine learning model is invoked to predict a respective silicon density of metal wires for each respective portion of the area based on an image and a drawn density for the respective portion of the area. A silicon density for the area of the metal layer is calculated based on a combination of predicted silicon densities for the multiple portions of the area. The combination is based on an average value of the predicted silicon densities for the multiple portions of the area.

Methods and systems for predicting silicon density for a metal layer of semi-conductor chip via machine learning

Machine assisted systems and methods for enhancing the resolution of an IC thermal profile from a system analysis are described. These systems and methods can use a neural network based predictor, that has been trained to determine a temperature rise across an entire IC. The training of the predictor can include generating a representation of two or more templates identifying different portions of an integrated circuit (IC), each template associated with location parameters to position the template in the IC; performing thermal simulations for each respective template of the IC, each thermal simulation determining an output based on a power pattern of tiles of the respective template, the output indicating a change in temperature of a center tile of the respective template relative to a base temperature of the integrated circuit; and training a neural network. The trained predictor can be used to determine a temperature rise and then can be appended to a system level thermal profile of the IC to generate a detailed thermal profile of the IC.

Systems and methods for machine learning based fast static thermal solver

Methods, machine readable media and systems for evaluating, through one or more simulations, the leakage of sensitive data in an integrated circuit, such as cryptographic data or keys, are described. The embodiments can use machine learning models, such as one or more neural networks to generate one or more leakage related scores for each portion in a set of portions of the cryptographic data. In one embodiment, leakage data associated the first set of POIs with one or more neural networks is processed by the one or more neural networks to identify the POIs that leak the most and determine one or more scores for each portion in the set of portions of the cryptographic data.

Machine learning for automatic identification of points of interest for side channel leakage

Machine assisted systems and methods for enhancing the resolution of an IC thermal profile from a system analysis are described. The methods can include generating a representation of two or more templates identifying different portions of an integrated circuit (IC); performing a thermal simulation for each respective template of the IC based on a sequence of power patterns of tiles of the respective template; and training a neural network with a plurality of training data collected via thermal simulations performed for the templates of the IC. These systems and methods can use a machine learning predictor, that has been trained to determine a transient temperature rise across an entire IC, and then append the determined transient temperature rise to a system level thermal profile of the IC.

Predicting on chip transient thermal response in a multi-chip system using an RNN-based predictor

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Pleasanton, CA, United States of America

Jimin Wen