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Arab Hassani. Soft Sci 2023;3:31 https://dx.doi.org/10.20517/ss.2023.23 Page 13 of 33

The human skin consists of the epidermis, dermis, and hypodermis, as shown in Figure 6A [119,138] . The hairs
present within the dermis can sensitively detect environmental physical changes and transmit them to the
brain through mechanoreceptors and nerve fibres.

[119]
Inspired by hairy human skin, Zhou et al. developed a flexible micro cilia array (MCA) layer . The MCA
layer worked as a dielectric layer sandwiched between two layers of Ag nanowire (Ag NW)/PDMS
electrodes to form a flexible capacitive pressure sensor, as shown in Figure 6B. The MCA layer was then cut
into 0.5 × 0.5 cm pieces and applied as separate dielectric layers in a flexible 5 × 5 capacitive sensor array. A
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facile method was used to fabricate the MCA layer. First, a PDMS layer was formed on a glass substrate.
Then, a layer composed of a mix of carbonyl iron particles (CIPs), PDMS, and a curing agent was spin-
coated onto the cured PDMS membrane. The substrate was then moved to the surface, where the PDMS/
CIP layer was magnetised using a permanent neodymium (NdFeB) magnet. This magnetic field caused
aggregation of the CIPs into chains and, finally, into the cilia array patterns. The images of this sensor array
in the presence of four objects of different weights and two objects having rectangular and triangular shapes
are presented in Figure 6C. The detected pressure distributions and magnitudes of the four objects and the
detected shapes of the rectangular and triangular objects are depicted in Figure 6C. The sensor exhibited a
wide pressure detection range of 0-200 kPa with a low detectable pressure of 2 Pa. This property ensured
that the sensor was suitable for a range of applications, such as real-time monitoring of elbow and finger
bending, voice monitoring, wrist pulse monitoring, and pressure monitoring of the hind sole during
standing, walking, and jumping. The device can be used in various applications, including wearable devices,
artificial intelligence, and interactive robotics.

Inspired by mechanoreceptors and synapses (i.e., the signal transmission junctions between two
[139]
[120]
neurons), Kim et al. developed a stretchable sensory-neuromorphic system (SSNS) [Figure 7A] . The
SSNS consisted of one 5 × 5 capacitive pressure sensor array (i.e., artificial mechanoreceptors) and four
arrays (i.e., one artificial neuron), each including 5 × 5 resistive random-access memories (RRAMs) (i.e., an
artificial synapse). In addition, they integrated an array of quantum dot light-emitting diodes (QLEDs)
mimicking the epidermal photonic actuators that exist on golden tortoise beetles to display the output
signals of the pressure sensor array. The usage of intrinsically stretchable printed interconnects between
these components stabilised the operation of the system under up to 25% stretching, which is similar to the
range of skin deformation. To fabricate the pressure sensor array, a sinter-free ink was prepared by mixing
the polymer [PDMS-(4,4′-methylenebis(phenyl isocyanate) (MPU)0.4-isophorone diisocyanate (IU)0.6]
with methyl isobutyl ketone (MIBK) and Ag flakes. The ink was screen-printed using a metal screen mask
to pattern the bottom and top electrode layers of the capacitive sensors. Moreover, the dielectric layer was
screen-patterned using a metal screen mask. RRAMs consisting of layers of patterned PI, Au/Al, titanium
dioxide (TiO ), Al/Au, and the final SU8 encapsulation layer were fabricated on a SiO wafer. A thermal
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release tape was used to detach PRAMs from the SiO 2 wafer and transfer them onto a PDMS substrate. The
sinter-free ink was used at the end to pattern the interconnects by means of screen printing. A similar
fabrication method was used to fabricate QLEDs. The fabrication process consisted of patterning islands of
PI, Ag/Au, zinc oxide (ZnO) nanoparticles, and red-emitting cadmium selenide/cadmium zinc sulphide
(CdSe/CdZnS) quantum dots on a SiO wafer. The islands were transferred onto a PDMS substrate by using
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a thermal release tape. This step was followed by thermal evaporation of a hole-transport layer (HTL), a
hole-injection layer (HIL), an Ag layer as the anode, and a final SiO encapsulation layer. The interconnects
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were then screen-printed. Figure 7B shows the system receiving four types of patterned stimuli (“S”, “N”,
“U”, and “K”) through a 5 × 5 capacitive pressure sensor array. The output of the capacitive sensor array
with and without 25% stretching is depicted in Figure 7C. The outputs of 25 capacitive sensors were
converted to voltage signals and applied to the four RRAM arrays, shaping an artificial neural network