Multiclayer

Multiclayer is a term used across disciplines to describe structures or systems composed of multiple layers. In computer science, it most often denotes a neural network architecture that contains more than one hidden layer between the input and the output. In materials science, multiclayer refers to thin-film stacks assembled from sequential layers of different materials.

In neural networks, a multiclayer network processes inputs through a sequence of linear transformations and nonlinear activations. Each layer maps its inputs to a new representation, enabling hierarchical feature extraction. The depth of the network—the number of layers—affects its expressive capacity and learning difficulty. Training typically uses backpropagation with gradient-based optimization, along with regularization techniques to prevent overfitting. Early multiclayer networks were limited by training difficulties, but advances in optimization, activation functions, and computational resources gave rise to deep learning, where many hundreds or thousands of layers may be used in specialized architectures such as feedforward, convolutional, or recurrent networks.

In materials science and engineering, multiclayer or multilayer films are stacks of alternating layers of different materials, engineered to achieve specific optical, electrical, or mechanical properties. Thickness, material choice, and interface quality control properties such as reflectivity, barrier performance, or thermal expansion. Fabrication methods include deposition techniques such as chemical vapor deposition, physical vapor deposition, sputtering, or atomic layer deposition. Applications include optical coatings, dielectric stacks in electronics, barrier films for packaging, and photovoltaic devices where interference effects enhance performance.

The term multiclayer thus spans disciplines, with domain-specific meanings but a shared emphasis on modular, layered structure. In computing, depth often relates to learning capacity; in materials science, design focuses on controlling interfaces and combined properties.