Our Mission

Ajay Baranwal

The Center for Deep Learning in Electronics Manufacturing (CDLe) is an alliance of leaders.

We have come together to pool talent and resources to advance the state-of-the-art in deep learning for our unique problem space and to accelerate the adoption of deep learning in each of our company’s products to improve our respective offerings for our customers.

“GPU computing has achieved great success in semiconductor design, simulation, and manufacturing. But as process technology shrinks, the physics is becoming increasingly difficult to simulate. At the same time, the sensor data is growing exponentially. This creates an opportunity for data-driven approaches like deep learning to complement physical models. We look forward to supporting CDLe and their efforts to achieve breakthrough results.”

Jerry Chen

Business Development Lead for Industrial Applications

“In addition to deep learning of big data available from manufacturing, availability of accurate simulation provides an opportunity for training in novel applications. With our commitment and collaboration to form the CDLe, NuFlare looks forward to speeding the time-to-market and use of deep learning to solve the many challenges for electronics manufacturing.”

Hirokazu Yamada

Director Mask Lithography Division


A Deep Learning Mask Analysis Toolset Using Mask SEM Digital Twins

Thomas Kurian of Mycronic describes work at CDLe to identify “Mura” on flat panel display (FPD) masks

Ajay Baranwal, Director of CDLe, describes five deep learning recipes for the semiconductor mask making industry

CDLe Celebrates One-year Anniversary

About Deep Learning in Electronics Manufacturing

Meet Ajay Baranwal, the new CDLe Director

Aki Fujimura and Steve Teig talk about Deep Learning

“Deep learning can provide both novel solutions to existing problems as well as new applications and services to help our customers increase yield, productivity and performance. Establishing the center will provide access to industry expertise and computing resources to accelerate our progress in these areas.”

Johan Franzén

Sr. Vice President R&D


Article from Ajay Baranwal of the CDLe, Noriaki Nakayamada of NuFlare, Mikael Wahlsten of Micronic, and Aki Fujimura of D2S
Ajay Baranwal, Director of CDLe, Presented at 2020 Photomask Technology Conference
Aki Fujimura explains the critical role of digital twins in deep learning
Experts at the Table, Part 2: ML is playing a bigger role in metrology and lithography, but it can’t replace physics-based models.
Experts at the Table: It’s not as accurate as simulation, but it’s a lot faster.
Presented at SPIE eBeam Initiative lunch by Thomas Kurian from Mycronic
Presented at SEMICON Europa 2019 by Javier Cabello from Mycronic
Ajay Baranwal, Director of CDLe, Presented at 2019 Photomask Technology Conference
Leo Pang of D2S explains why GPU-accelerated simulation is so important to deep learning
Mikael Wahlsten, Director and Product Area Manager for Photomask Generators at Mycronic, gives his insights into the idea behind the new collaboration and what it can mean for Mycronic customers in the near future.
Experts at the Table, part 3: Where can this technology be applied and what’s ahead.
Experts at the Table, part 2: Where can this technology be applied, why it is taking so long, and what challenges lie ahead.
NVIDIA, NuFlare, Mycronic and D2S executives give their perspective
Aki Fujimura pens an editorial on Deep Learning for the BACUS Newsletter

Deep Learning Primer: Data is the New Source Code

publication • September 19, 2018

Presentation at 2018 Photomask Technology Conference
NuFlare, Mycronic, and D2S Partnership tops the manufacturing news for the week
Alliance of NuFlare Technology, Mycronic and D2S using NVIDIA Technology

“GPU-accelerated computing power has fueled the recent growth and wide application of deep learning. …Together, NuFlare and Mycronic represent a very important part of the electronics manufacturing supply chain. We look forward to working with these industry leaders in the formation of the CDLe to advance the use of deep learning technologies for the electronics manufacturing industry.”

Aki Fujimura