Study of Organic and Quantum Dots-Based Resistive Memory and Synaptic Devices
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Date
2023-09
Researcher
Betal, Atanu
Supervisor
Sahu, Satyajit
Journal Title
Journal ISSN
Volume Title
Publisher
Indian Institute of Technology Jodhpur
Abstract
The data storage requirement in the digital world is increasing day by day with the
advancement of the Internet of Things (IoT). The current generation of silicon-based memory
technology is facing serious problems in terms of performance, data storage density, power
consumption, data processing time, cost-effectiveness, and so on. Conventional flash memory
is one type of non-volatile memory that relay on tunneling through the oxide layer, consumes
high power, and response time is high. The hard disk drive (HDD) is a widely used memory
in the current era, but the main disadvantage is finding the particular magnetic domain where
the data is saved, which leads the memory response time to a few milliseconds. The limitations
of conventional memories can be tackled by next-generation memories. Ferroelectric random
access memory (FERAM), Phase change memory (PCM), Magnetic random access memory
(MRAM) and Resistive random access memory (RRAM) are considered as next-generation
memory and have the potential to solve the problem. Among the other memories, non-volatile
RRAM is an option that provides high-density and low power data storage capabilities. The
information is stored in terms of resistance, where the high resistance state (HRS) is 0 bit, and
the low resistance state (LRS) is 1 bit. The computers are based on von Neumann architecture,
where processor units are separated from the memory unit and connected via a data bus. This
causes a delay in response time and cannot go beyond a certain size limit, called von Neumann
bottleneck. The current resistive memory has the capability to solve the von Neumann
bottleneck, where the memory can simultaneously process and store data similar to the
biological brain.
The small molecule-based RRAM device is a point of interest because of its capability to be
used in high-density data storage devices. A small organic molecule 5-Mercapto-1-methyl
tetrazole (MMT) has been used with a polymer poly (4-vinyl pyridine) (PVP) matrix for the
active layer of RRAM device. The MMT molecule with a different weight ratio in PVP was
studied for RRAM application which reveals the invariant RRAM property. The maximum
on?off current ratio for all the devices is 105, suggesting that the MMT molecule does not show
any change in its characteristic properties when surrounded by an insulating material. When
the device was fabricated without the polymer matrix, the surface morphology of the device
completely changed as it was filled with large holes. These holes provide short-circuited
pathways for the current by forming the direct metal contact between the top and bottom
electrodes.
Size miniaturization of the electronic device can be done using organic small molecules as
well as inorganic nanoparticle QDs. The synthesis of CdS QDs and the study of RRAM
properties has been studied. Al/CdS+PVP/ITO like MIM structured device was fabricated
which shows extremely good switching properties. The data retention capability of 60000
seconds and 300 endurance cycles were studied. The charge-trapping mechanism is associated
with the RS property.
With the development of artificial intelligence and ultra-high speed computing the solution
of von Neumann bottleneck is needed. In this regards, resistive memory can provide solution
as RRAM has the capability to act as biological synapse that can store, process and transfer
data. This attracts researchers to study resistive memory devices. Here fabrication of a small
organic molecule Trimesic acid (TMA) and PVP composite-based resistive memory device. It
shows excellent resistive switching with a high on-off ratio, excellent stability and data
storage capability. Pulse transient measurements on the device demonstrated the capability
of neuromorphic computation. The gradual set and reset process and change of conductance
with an applied pulse confirmed the neuromorphic application. Paired pulse facilitation
shows that the device can behave like the human brain. The redox active molecule and its
change in conformation are the reasons for the switching behaviour of the device th led to
the neuromorphic application of the device. For an important application like RRAM, it is crucial to understand the mechanism in
nanoscale and control the Resistive switching (RS) by various means. Different models have
been proposed to explain the RS behaviour of the material. First, the electrode effect on the
switching, which includes contact type, and charge trapping/de-trapping near the electrodematerial
interface. The other mechanisms are conducting filament formation, electrochemical
metallization, ionic diffusion, and oxidation-reduction of the materials. So, there are many
disagreements on the proposed models of RS, and it requires understanding using different
experimental techniques. STM is one of the best tools for understanding the surface property,
as well as studying the local density of states (LDOS) of the material. So, using the scanning
tunnelling microscopy (STM) technique to understand the RS in materials in the nanoscale
range would be very helpful. The RS properties and capabilities of neuromorphic computing
of single AgInS2 quantum dot with the help of STM and scanning tunnelling spectroscopy
(STS) have been studied in this chapter. The bandgap of the material and its temperature
dependency has been studied and it suggests a nonlinear and linear variation at lower and
higher temperature than the Debye temperature respectively. The STS shows the change of
conducting states after applying localized pulses. The devices made from the quantum dots
replicate these properties as well. The neuromorphic application of the device was tested by
using the pulse transient measurement that mimics the learning and forgetting of information
through the gradual set and reset process. The localized ionic transport is involved in the RS
mechanism.
Description
Keywords
Citation
Betal, Atanu. (2023). Study of Organic and Quantum Dots-Based Resistive Memory and Synaptic Devices (Doctor's thesis). Indian Institute of Technology Jodhpur, Jodhpur.