US 12,260,339 B2
Generative model for inverse design of materials, devices, and structures
Keisuke Kojima, Weston, MA (US); Toshiaki Koike Akino, Belmont, MA (US); Yingheng Tang, West Lafayette, IN (US); and Ye Wang, Andover, MA (US)
Assigned to Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA (US)
Filed by Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA (US)
Filed on May 3, 2021, as Appl. No. 17/306,003.
Prior Publication US 2022/0358368 A1, Nov. 10, 2022
Int. Cl. G06N 3/088 (2023.01); G06N 3/045 (2023.01)
CPC G06N 3/088 (2013.01) [G06N 3/045 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A system for training a device design network for generating layouts of photonic devices, comprising:
an interface configured to acquire input data of a photonic device, wherein the input data includes user-desired transmission information given as a condition, and a Gaussian distribution;
a memory storing the device design network including a first encoder, a second encoder, a first decoder, a second decoder, and a first adversarial block and a second adversarial block, wherein the first and second encoders have a same structure and share same weights, the first and second decoders have a same structure and share same weights, and the first and second adversarial blocks have a same structure and share same weights; and
a processor, in connection with the memory, configured to:
train the device design network using training data including an input pattern, wherein the input pattern is a hole vector pattern, and an input condition, wherein the input condition is a transmission efficiency;
update the weights of the first and second encoders and the first and second decoders based on a sum of a first loss function and a third loss function to reduce a difference between the input pattern and output pattern of the first and second decoders; and
update the weights in the first and second adversarial blocks by minimizing a second loss function, wherein the second loss function maximizes a loss between the condition and the first and second adversarial conditions.