2 research outputs found
Mimicking Biological Synaptic Functionality with an Indium Phosphide Synaptic Device on Silicon for Scalable Neuromorphic Computing
Neuromorphic
or ābrain-likeā computation is a leading
candidate for efficient, fault-tolerant processing of large-scale
data as well as real-time sensing and transduction of complex multivariate
systems and networks such as self-driving vehicles or Internet of
Things applications. In biology, the synapse serves as an active memory
unit in the neural system and is the component responsible for learning
and memory. Electronically emulating this element <i>via</i> a compact, scalable technology which can be integrated in a three-dimensional
(3-D) architecture is critical for future implementations of neuromorphic
processors. However, present day 3-D transistor implementations of
synapses are typically based on low-mobility semiconductor channels
or technologies that are not scalable. Here, we demonstrate a crystalline
indium phosphide (InP)-based artificial synapse for spiking neural
networks that exhibits elasticity, short-term plasticity, long-term
plasticity, metaplasticity, and spike timing-dependent plasticity,
emulating the critical behaviors exhibited by biological synapses.
Critically, we show that this crystalline InP device can be directly
integrated <i>via</i> back-end processing on a Si wafer
using a SiO<sub>2</sub> buffer <i>without the need for a crystalline
seed</i>, enabling neuromorphic devices that can be implemented
in a scalable and 3-D architecture. Specifically, the device is a
crystalline InP channel field-effect transistor that interacts with
neuron spikes by modification of the population of filled traps in
the MOS structure itself. Unlike other transistor-based implementations,
we show that it is possible to mimic these biological functions without
the use of external factors (<i>e</i>.<i>g</i>., surface adsorption of gas molecules) and without the need for
the high electric fields necessary for traditional flash-based implementations.
Finally, when exposed to neuronal spikes with a waveform similar to
that observed in the brain, these devices exhibit the ability to learn
without the need for any external potentiating/depressing circuits,
mimicking the biological process of Hebbian learning
Confined Liquid-Phase Growth of Crystalline Compound Semiconductors on Any Substrate
The growth of crystalline
compound semiconductors on amorphous
and non-epitaxial substrates is a fundamental challenge for state-of-the-art
thin-film epitaxial growth techniques. Direct growth of materials
on technologically relevant amorphous surfaces, such as nitrides or
oxides results in nanocrystalline thin films or nanowire-type structures,
preventing growth and integration of high-performance devices and
circuits on these surfaces. Here, we show crystalline compound semiconductors
grown directly on technologically relevant amorphous and non-epitaxial
substrates in geometries compatible with standard microfabrication
technology. Furthermore, by removing the traditional epitaxial constraint,
we demonstrate an <i>atomically sharp lateral heterojunction</i> between indium phosphide and tin phosphide, two materials with vastly
different crystal structures, a structure that cannot be grown with
standard vapor-phase growth approaches. Critically, this approach
enables the growth and manufacturing of crystalline materials without
requiring a nearly lattice-matched substrate, potentially impacting
a wide range of fields, including electronics, photonics, and energy
devices