FIELD OF THE INVENTION

The invention generally related to method for use conditional variational autoencoder to randomly generate materials, device, or structural designs wherein desired characteristics of materials, devices, or structures are given as conditions. We use adversary network to refine the design.

BACKGROUND OF THE INVENTION

In many areas of materials, devices, and structures, designing is a challenge because tens, hundreds, or even more parameters need to be optimized simultaneously, and each simulation or experiment to verify the updated chracteristics for new parameter sets takes a long time. So efficient optimization methods are desired.

Inverse design of optical devices have used using deep neural network for regression in forward or inverse directions have been done before. (Tahersima et al., Scientific Reports). Once the inverse model is fully trained, it can theoretically generate the design parameters for us. However, the previous inverse neural network model is for optimizing the binary structure (such as 0 or 1), which reduce the dimension of the actual optimization problem. This may bring up some limitations such as narrower bandwidth and semi optimized result which requires to be further optimized. So a better generative model can be used for such a sophisticated optimizing problem.

SUMMARY OF THE INVENTION

The invention proposes to use a conditional variational autoencoder, combined with an adversary network to randomly generate device designs wherein desired device performances are given as conditions. Active training (co-training) can be added for further improving the performance.

Some embodiments of the present disclosure are the silicon photonics splitters based on the MMI structure. The dimension of the final MMI is 2.25 min by 2.25 μm. There are 400 hole-vectors over the MMI structure and these holes have variable hole diameter ranging from 40 μm to 90 μm. The fully trained CVAE model can generate different hole vectors combinations based on different splitting ratio inputs. The overall transmission efficiency for all the generated devices is above 89% across a very broad bandwidth (from 1300 nm to 1800 nm), with negligible insertion loss (below −25 dB). Such model can be applied to different devices such as mode convert, directional coupler and etc.

Some of the embodiments are based on the training data combined with two different datasets: first is the “semi-optimized” results with binary hole size (either no hole or 90 nm hole). The bandwidth for these devices is relatively low (100 nm). The second part of the dataset is combination of multiple patterns along with its performances. According to embodiments, it is shown that with the above “semi-optimized” results, we can train model which is capable of generating devices with excellent performance (90% total transmission) over a broad bandwidth (500 nm). In according to some embodiments of the present disclosure, the model structure is a Conditional Variational Auto Encoder along with the adversarial block, which is based on Bayesian Theorem. It wants the model to underlie the probability distribution of data so that it could sample new data from that distribution. Our training data are generated by doing FDTD simulations. The data are constructed by the following: several DBS simulations and some random generated patterns. The training patterns are all binary holes and the bandwidth is relatively small (1500-1600 nm). The total training data is ˜15000.

