Get Started
Menu

Digital twins are the key to success for deep learning (DL) projects – especially DL projects that involve processes that are dangerous, expensive, or time-consuming. A digital twin is a digital replica of an actual physical process, system, or device that can be used in simulations.

Digital twins are used in simulations to create the right amount of the right kind of simulation data to train DL networks successfully. There are several reasons to use digital twins in simulations to create DL training data:

  • The data you work with belongs to your customers, so you can’t use it for DL training
  • The resources you need to create the data you need for DL are fully committed to customer projects
  • You don’t have access to enough anomalous data to train your DL networks adequately
  • You have developed DL applications but have found that you need specific data to tune and train your neural networks to reach the required level of accuracy, but the cost of using mask shop/fab resources to create those data is prohibitive

 

The Promise of Deep Learning

DL employs neural networks to perform advanced pattern-matching. In semiconductor manufacturing, DL has already been applied in areas such as defect classification and anomaly detection. As companies start to explore DL and how it can help them, many are finding two things: first it’s easy to get to a prototype; and second, it’s harder to get from “good prototype” results to “production quality” results. Why?

The DL Data Gap

DL applications are “programmed” by presenting neural networks with data that represent a target to matched. Masses of data train the network to recognize the target (and to know when it’s not the target).

The predictive ability of any DL application depends on the depth and breadth of the data set used in training. If the training data set is too small, too narrow, or too “normal,” it will not be more predictive than standard techniques. It’s important to train networks with masses of data representing every possible state or presentation in equal volumes.

The difficulty for some fields, such as autonomous driving, or semiconductor manufacturing, is that some of the most serious anomalous conditions occur very rarely. But, if you want a DL application to recognize a child darting in front of a car – or a fatal mask error – you have to train the networks with a multitude of these scenarios…which don’t exist in any great volume in the real world. Simulation is the only way to create enough anomalous data to properly train the networks to recognize these conditions.

Digital Twins Bridge the Gap

The Center for Deep Learning in Electronics Manufacturing (CDLe) was a five-year (2018-2023) alliance between D2S, Mycronic and NuFlare Technology, industry leaders who recognized deep learning’s problem-solving potential for electronics manufacturing. CDLe has successfully completed over 30 production DL projects for electronic manufacturing by using digital twin technology to bridge the data gap. At the 2019 SPIE Photomask Technology conference, D2S presented a paper demonstrating the creation of two digital twins – a SEM digital twin, and a curvilinear ILT digital twin – using DL techniques. These digital twins have been used both for DL training and validation.

Linyong Pang; Suhas Pillai; Thang Nguyen; Mike Meyer; Ajay Baranwal; Henry Yu; Mariusz Niewczas; Ryan Pearman; Abhishek Shendre; Aki Fujimura, “Making digital twins using the Deep Learning Kit (DLK),” Proc. SPIE 11148, Photomask Technology 2019, 111480B (21 October 2019); doi: https://doi.org/10.1117/12.2538508

Examples of SEM images generated by SEM digital twin and the real SEM image

The image intensity on a horizontal cutline at the same location are shown as well. Note that not only do the images look very similar, but also the signal response on edges are similar as well.

D2S Can Put You on the Path to DL Success

If you’ve already started down the road with your DL projects but have encountered issues due to the DL data gap, D2S can help you build the digital twins you need to augment and tune your data sets for DL success.

If you’re starting to gather your resources for DL, D2S offers TrueMask® DLK, a deep learning kit that includes GPU computing, modeling, simulation, and validation technologies, as well as neural networks trained for semiconductor manufacturing, and is available with some digital twins, such as SEM and inspection digital twins.