Silicon is vital for its high abundance, vast production, and perfect compatibility with the well-established CMOS processing industry. Recently, artificially stacked layered 2D structures have gained tremendous attention via fine-tuning properties for electronic devices. This article presents neuromorphic devices based on silicon nanosheets that are chemically exfoliated and surface-modified, enabling self-assembly into hierarchical stacking structures. The device functionality can be switched between a unipolar memristor and a feasibly reset-able synaptic device. The memory function of the device is based on the charge storage in the partially oxidized SiNS stacks followed by the discharge activated by the electric field at the Au-Si Schottky interface, as verified in both experimental and theoretical means. This work further inspired elegant neuromorphic computation models for digit recognition and noise filtration. Ultimately, it brings silicon - the most established semiconductor - back to the forefront for next-generation computations.

Charge-coupled devices (CCD) allow imaging by photodetection, charge integration, and serial transfer of the stored charge packets from multiple pixels to the readout node. The functionality of CCD can be extended to the non-destructive and in-situ readout of the integrated charges by replacing metallic electrodes with graphene in the metal-oxide-semiconductors (MOS) structure of a CCD pixel. The electrostatic capacitive coupling of graphene with the substrate allows the Fermi level tuning that reflects the integrated charge density in the depletion well. This work demonstrates the in-situ monitoring of the serial charge transfer and interpixel transfer losses in a reciprocating manner between two adjacent Gr-Si CCD pixels by benefitting the electrostatic and gate-to-gate couplings. We achieved the maximum charge transfer efficiency (CTE) of $92.4\%$, which is mainly decided by the inter-pixel distance, phase clock amplitudes, switching slopes, and density of surface defects. The discussion on overcoming transfer losses and improving CTE by realizing a graphene-electron multiplication CCD is also presented. The proof of the concept of the in-situ readout of the out-of-plane avalanche in a single Gr-Si CCD pixel is also demonstrated, which can amplify the photo packet in a pre-transfer manner.

We demonstrate photogating in a graphene/Si–SiO₂ stack, where vertical motion of photogenerated charge is converted into a corresponding change in graphene channel conductance in real time. Under pulsed illumination, holes accumulate at the Si/SiO₂ interface, creating a surface photovoltage that shifts the flat-band condition and electrostatically suppresses graphene conductance. A dual-readout scheme—simultaneously tracking interfacial charging dynamics and the graphene channel—cleanly separates optical charge injection (cause) from electronic transduction (effect). This separation allows for the direct extraction of practical figures of merit without conventional transfer sweeps, including flat-band shift per pulse, retention time constants, and trap occupancy. Interface kinetics then define two operating regimes: a fast, resettable detector when traps are sparse or rapid, and a trap-assisted analog-memory state when slow traps retain charge between pulses. The mechanism is complementary metal-oxide–semiconductor compatible (CMOS-compatible) and needs no cryogenics or exotic materials. Together, these results outline a compact route to engineer integrating photodetectors, pixel-level memory for adaptive imaging, and neuromorphic optoelectronic elements that couple sensing with in situ computation.

In this study, we develop a graphene-trenched silicon Schottky junction for humidity sensing. This novel structure comprises suspended graphene bridging etched trenches on a silicon substrate, creating both free-standing and substrate-contacting regions of graphene that enhance water adsorption sensing. Suspended graphene is intrinsically insensitive to water adsorption, making it difficult for adsorbed H₂O to effectively dope the graphene. In contrast, when graphene is supported on the silicon substrate, water molecules can effectively dope the graphene by modifying the silicon’s impurity bands and their hybridization with graphene. This humidity-induced doping leads to a significant modulation of the Schottky barrier at the graphene–silicon interface, which serves as the core sensing mechanism. We investigate the current–voltage (I–V) characteristics of these devices as a function of trench width and relative humidity. Our analysis shows that humidity influences key device parameters, including the Schottky barrier height, ideality factor, series resistance, and normalized sensitivity. Specifically, larger trench widths reduce the graphene density of states, an effect that is accounted for in our analysis of these parameters. The sensor operates under both forward and reverse bias, enabling tunable sensitivity, high selectivity, and low power consumption. These features make it promising for applications in industrial and home safety, environmental monitoring, and process control.