Inverse-designed Photonic Computing Core for Parallel Matrix-vector Multiplication

On-chip optical neural networks (ONNs) have recently emerged as an attractive hardware accelerator for deep learning applications, characterized by high computing density, low latency, and compact size. As these networks rely heavily on massive matrix multiplication, photonic computing cores for matrix computation become crucial components for on-chip ONNs, which harness the degree of freedoms (DOFs) in photonics including space, wavelength and mode dimensions. However, previous photonic computing devices have not fully utilized the orthogonality and the conversion characteristic of the waveguide modes, which as we show here, allows for the simultaneous parallel computing of several independent matrix-vector multiplications within the same device. In this work, we propose an inverse-designed photonic computing core for parallel matrix-vector multiplication. The matrices are implemented through a mode conversion process, where the input fundamental modes are simultaneously converted into several orthogonal output modes. Specifically, we target the complex-valued conversion matrices between input and output modes and inversely design the dielectric distribution within the device to achieve parallel matrix-vector multiplication. As a demonstration, the proposed photonic computing core supports simultaneous parallel computing of two independent matrix-vector multiplications, with an ultra-compact footprint and high computing precision (relative error < 8%) at 1550 nm wavelength. The inverse-designed photonic computing devices hold great potential for high-performance on-chip ONNs with low energy consumption and high computing density

Computer Screening of Dopants for the Development of New SnO₂‑Based Transparent Conducting Oxides

Transparent conducting oxides (TCOs) are unique materials with high electrical conductivity and optical transparency and have been extensively used in optoelectronic devices. However, the prototype n-type TCO, Sn-doped In₂O₃ (ITO), is limited by the rarity and high cost of indium. In contrast, SnO₂ is a promising alternative candidate, which is a low-cost and nontoxic material and also exhibits electrical and optical properties, compared to those of ITO. Here, we present a first-principles-based computer screening system to search for suitable dopants for monodoping or codoping SnO₂ to develop new SnO₂-based TCO materials. The screening is based on an efficient and reliable way of calculating the effective mass, the band gap, the formation energy, and the binding energy. The outcomes of the screening include all already known successful SnO₂-based TCO materials (Sb-doped SnO₂, ATO; F-doped SnO₂, FTO) and also some new ones (P-doped SnO₂, PTO; F and P codoped SnO₂, FPTO), which would be hopeful materials of interest for further experimental validation

A flowchart of the multi-model method for classification.

<p>A flowchart of the multi-model method for classification.</p

Regions of interest (ROIs) included in the AAL-atlas.

<p>Regions of interest (ROIs) included in the AAL-atlas.</p

Classification performance of the single metrics and multi-modal combinations.

<p>Classification performance of the single metrics and multi-modal combinations.</p

The brain regions which have been selected as features more than 23 times.

<p>(A) Selected regions with ReHo. (B) Selected regions with ALFF. (C) Selected regions with GM. (D) Selected regions with RFCS. (E) Selected regions with WM. (F) Selected regions with CSF.</p

Clinical details of all patients.

<p>Abbreviations: UPDRS, Unified Parkinson's disease Rating Scale; H&Y, Hoehn and Yahr Scale; M, male; F, female.</p

The brain regions which have been selected as features more than 23 times.

<p>A positive t-value represents increased values in the PD group. Abbreviations: R, right; L left.</p

The number of features retained in the multi-model method per fold.

<p>The number of features retained in the multi-model method per fold.</p

Classification performance of the multi-model method.

<p>The ROC curve of the classifier. The area under the ROC curve was 0.951.</p