University of Wisconsin-Madison engineers have developed a "smart" piece of glass that recognizes images without any external power or circuits. The glass could potentially be used as a biometric lock, tuned to recognize only one person's face.

Erfan Khoram, Ang Chen, Dianjing Liu, Lei Ying, Qiqi Wang, Ming Yuan, and Zongfu Yu, "Nanophotonic media for artificial neural inference," Photon. Res.  7, 823-827 (2019)

DOI: https://doi.org/10.1364/PRJ.7.000823

We show optical waves passing through a nanophotonic medium can perform artificial neural computing. Complex information is encoded in the wavefront of an input light. The medium transforms the wavefront to realize sophisticated computing tasks such as image recognition. At the output, the optical energy is concentrated in well-defined locations, which, for example, can be interpreted as the identity of the object in the image. These computing media can be as small as tens of wavelengths and offer ultra-high computing density. They exploit subwavelength scatterers to realize complex input/output mapping beyond the capabilities of traditional nanophotonic devices.

Fig. 2.
Fig. 2. - (a) NNM trained to recognize handwritten digits. The input wave encodes the image as the intensity distribution. On the right side of the NNM, the optical energy concentrates to different locations depending on the image’s classification labels. (b) Two samples of the digit 2 and their optical fields inside the NNM. As can be seen, although the field distributions differ for the images of the same digit, they are classified as the same digit. (c) The same as (b) but for two samples of the digit 8. Also, in both (b) and (c), the boundaries of the trained medium have been shown with black borderlines (see Visualization 1). © Erfan Khoram, Ang Chen, Dianjing Liu, Lei Ying, Qiqi Wang, Ming Yuan, and Zongfu Yu
Fig. 3.
Fig. 3. - (a) Training starts by encoding an image as a vector of current source densities in the FDFD simulation. This step is followed by an iterative process to solve for the electric field in a nonlinear medium. Next, we use the ASM to calculate the gradient, which is then used to update the level-set function and consequently, the medium itself. Here we use mini-batch SGD (explained in the supplementary materials section of Ref. [17]). In training with mini-batches, we sum the cost functions calculated for different images in the same batch and compute the gradients. (b)–(d) show an NNM in training after 1, 33, and 66 training iterations, respectively. (After iteration 66, the medium has already seen each of the training samples at least once, since we are using batches of 100 images.) At each step, the boundary between the host material and the inclusions is shown, along with the field distribution for the same randomly selected digit 8. Also, the accuracy of the medium on the test set can be seen for that particular stage in training. © Erfan Khoram, Ang Chen, Dianjing Liu, Lei Ying, Qiqi Wang, Ming Yuan, and Zongfu Yu Author Information