Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Molecular-scale Integration of Multi-modal Sensing and Neuromorphic
  Computing with Organic Electrochemical Transistors   Abstract: Bionic learning with fused sensing, memory and processing functions
outperforms artificial neural networks running on silicon chips in terms of
efficiency and footprint. However, digital hardware implementation of bionic
learning suffers from device heterogeneity in sensors and processing cores,
which incurs large hardware, energy and time overheads. Here, we present a
universal solution to simultaneously perform multi-modal sensing, memory and
processing using organic electrochemical transistors with designed architecture
and tailored channel morphology, selective ion injection into the
crystalline/amorphous regions. The resultant device work as either a volatile
receptor that shows multi-modal sensing, or a non-volatile synapse that
features record-high 10-bit analog states, low switching stochasticity and good
retention without the integration of any extra devices. Homogeneous integration
of such devices enables bionic learning functions such as conditioned reflex
and real-time cardiac disease diagnose via reservoir computing, illustrating
the promise for future smart edge health informatics.

10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
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The categories of this paper are: eess
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The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
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The categories of this paper are: econ
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-ex