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Heterogeneous integration techniques allow the coupling of highly lattice-mismatched solid-state membranes, including semiconductors, oxides, and two-dimensional materials, to synergistically fuse the functionalities. The formation of heterostructures generally requires two processes: the combination of crystalline growth and a non-destructive lift-off/transfer process enables the formation of high-quality heterostructures. Although direct atomic interaction between the substrate and the target membrane ensures high-quality growth, the strong atomic bonds at the substrate/epitaxial film interface hinder the non-destructive separation of the target membrane from the substrate. Alternatively, a 2D material-coated compound semiconductor substrate can transfer the weakened (but still effective) surface potential field of the surface through the 2D material, allowing both high-quality epitaxial growth and non-destructive lift-off of the grown film. This Perspective reviews 2D/3D heterogeneous integration techniques, along with applications of III–V compound semiconductors and oxides. The advanced heterogeneous integration methods offer an effective method to produce various freestanding membranes for stackable heterostructures with unique functionalities that can be applied to novel electrical, optoelectronic, neuromorphic, and bioelectronic systems. 

Abstract Artificial neural networks (ANNs) are widely used in numerous artificial intelligence‐based applications. However, the significant amount of data transferred between computing units and storage has limited the widespread deployment of ANN for the artificial intelligence of things (AIoT) and power‐constrained device applications. Therefore, among various ANN algorithms, quantized neural networks (QNNs) have garnered considerable attention because they require fewer computational resources with minimal energy consumption. Herein, an oxide‐based ternary charge‐trap transistor (CTT) that provides three discrete states and non‐volatile memory characteristics are introduced, which are desirable for QNN computing. By employing a differential pair of ternary CTTs, an artificial synaptic segregation with multilevel quantized values for QNNs is demostrated. The approach establishes a platform that combines the advantages of multiple states and robustness to noise for in‐memory computing to achieve reliable QNN performance in hardware, thereby facilitating the development of energy‐efficient AIoT.