in short response times and great sensitivity of the devices [27]. This property, when used

in bioelectronic devices, allows for the precise probing of complex biological dynamics. Si

is straightforwardly used to manufacture a variety of designs at various sizes, ranging

from the nanoscale to the macroscale. This sort of multiscale material management is well

suited to the multiscale application of varied biological components and enables in­

tegration with a wide range of biological systems, as demonstrated in the case study. A

more specific example is the administration of Si-based 1D nanostructures into neuronal

cultures or tissues in a drug-like manner while maintaining high spatial resolution. Si-

based 1D nanostructures have improved mechanical flexibility, in addition to carrier

transport capacity [6].

Furthermore, photonic energy may be turned into electrical energy once it has been

absorbed by silicon semiconductors. Si-based materials are frequently surrounded by

biological fluids when used in cell cultures or as implants, leading to the production of

interfaces between the two materials, which are known as semiconductor/saline inter­

faces. It is possible that in the presence of light, this will result in transient photo­

capacitive modulation of cells or tissues or longer-lasting photofaradic reactions in which

cells or tissues are harmed. The participation of electrons and holes in cathodic and

anodic processes, respectively, makes these processes more efficient [28].

Palanker et al. developed a series of photovoltaic retinal implants, in which the high-

pixel-density devices provide local control of rat retinal neurons, with the hope of re­

covering vision one day. When developing nano-bioelectronic devices, it is vital to boost

the photovoltaic or photoelectrochemical impact of these devices to improve photo­

stimulation by these devices [29,30]. Parameswaran et al. [31] observed that dopant al­

teration and surface chemistry of the Si-based nanostructures might increase their efficacy

as neuromodulators by a factor of two, compared to the control group (Figure 1.4a). They

used coaxial p-type/intrinsic/n-type (p–i–n) Si-based nanowires (Si-NWs) to regulate

primary rat dorsal root ganglion neurons by photo-electrochemical processes. Each Si-

NWs was made of a core nanowire doped with p, an interlayer of intrinsic Si, and an n-

doped shell. Further experiments demonstrated that the inclusion of distributed atomic

Au on the sidewalls of Si-NWs might significantly boost the generation of photo-

electrochemical currents and, as a result, the efficacy of neuro-modulation (Figure 1.4b).

Another study, conducted by Jiang et al. [27], looked at 2D p–i–n Si membranes that

were adorned with noble metal nanoparticles (such as Au, Ag, and Pt) to get deposited on

their surfaces via electroless deposition. They came to a similar conclusion. A consider­

able increase in the generation of photo-electrochemical currents was seen when metal-

decorated p–i–n membranes were used, with the increase being at least an order of

magnitude. Consequently, visual stimulation of the cerebral cortex, as well as behavior

control, were both significantly improved (Figure 1.4c). In addition, the researchers be­

lieve that Si photovoltaic devices can play a significant role in the ultrasensitive detection

of biometric signals. Yokota et al. [33] have reported an imager constructed of low-

temperature polycrystalline silicon (LTPS) thin-film transistors (TFTs), which can read

out modest photocurrents of less than 10 µA while producing very little background

noise. Polycrystalline silicon (polySi) was transformed into an amorphous silicon film

(a-Si) by the use of excimer laser annealing, which was then utilized to fabricate the TFT

readout circuits. For the design of the TFT readout circuits, the silicon oxide (SiO) film,

silicon nitride film (SiN), and amorphous silicon film (a-Si) films were all employed.

When used in conjunction with sensitive biological detectors, the imager is capable of

electrically detecting and calibrating the movement of the device based on fingerprint or

vein feature points, which is particularly useful in medical applications.

8

Bioelectronics