Light‐intensity selective superlinear photodetectors with ultralow dark current can provide an essential breakthrough for the development of high‐performing near‐sensor vision processing. However, the development of near‐sensor vision processing is not only conceptually important for device operation (given that sensors naturally exhibit linear/sublinear responses), but also essential to get rid of the massive amount of data generated during object sensing and classification with noisy inputs. Therefore, achieving the giant superlinear photoresponse while maintaining the picoampere leakage current, irrespective of the measurement bias, is one of the most challenging tasks. Here, Mott material (vanadium dioxide) and silicon‐based integrated infrared photodetectors are developed that show giant superlinear photoresponse (exponent >18) and ultralow dark current of 4.46 pA. Specifically, the device demonstrates an electro‐opto‐coupled insulator‐to‐metal transition, which leads to outstanding photocurrent on/off ratio (>106), a high responsivity (>1 mA W−1), and excellent detectivity (>1012 Jones), while maintaining response speed (τr = 6 µs and τf = 10 µs). Further, intensity‐selective near‐sensor processing is demonstrated and night vision pattern reorganization even with noisy inputs is exhibited. This research will pave the way for the creation of high‐performance photodetectors with potential uses, such as in night vision, pattern recognition, and neuromorphic processing.
Infrared Photodetectors
In article number 2210907, Mohit Kumar, Hyungtak Seo, and co‐workers report on the development of Mott material (vanadium dioxide) and silicon‐based integrated infrared photodetectors with giant superlinear photoresponse (exponent >18) and an ultralow dark current of 4.46 pA., which are then used for intensity‐selective high‐performance near‐sensor processing and the reconstruction of night vision patterns despite noisy inputs.
Processing
data demands volatile memory, whereas storage necessitates
nonvolatile devices. Typically, during the operations, data moves
back and forth between them, causing an increase in energy consumption,
undesired heating, and von Neumann bottleneck. Therefore, overcoming
these obstacles could provide an essential breakthrough for on-demand
in-memory processing; however, this would undoubtedly necessitate
the unification of both volatile and nonvolatile memory. Ferroelectric
materials have the potential for such unification of memory; yet,
because of their different physical origins, getting both memories
in one device has been difficult. Here, we developed high-performance,
two-terminal, hybrid volatile/nonvolatile memory devices using ternary
metal oxide as an active material. Particularly, formation of the
ferroelectric NiTiO3 phase by solid-state synthesis is
revealed by transmission electron microscopy and related tools, which
is further corroborated by polarization–voltage measurements.
Additionally, devices exhibiting trapping/detrapping-based volatile
and ferroelectric polarization governed nonvolatile, remarkably stable
(>105 s), and controlled multilevel (>6) analog memory
with a configurable switching ratio of 103. Further, intrinsic
Hopfield natural learning and object classification of input noisy
patterns without any back propagation is demonstrated. The study proposes
a route to develop on-demand hybrid volatile/nonvolatile memory devices,
which will have a potential impact on multiple applications, including
memory storage, processing, and neural classification.
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