Paradigm Shifts Drive Compute Needs
Paradigm Shift #1: The end of lateral scaling
Unlike past decades, we can no longer rely on continuous lateral scaling of transistors to increase computing speed and reduce power. Thus, we have begun to search for alternate materials, devices, and even architectures.
Paradigm Shift #2: The rise of machine learning
Recent developments in machine learning algorithms, and deep learning algorithms in particular, have driven an explosion of interest, dramatically improving performance in many areas. One important effect is that the memory bandwidth has become a severe bottleneck, driving the design of new computing chips specifically optimized for machine learning algorithms.
Materials: Non-epitaxial growth
Can we directly grow crystalline semiconductors on technologically relevant non-epitaxial substrates (e.g. silicon dioxide, silicon nitride, tungsten, etc…)?
Devices: Electronics on Anything
Can we make high-performance electronic and photonic devices directly on amorphous substrates, to enable 3-D integration?
Systems: Beyond von-Neumann
Can we make novel systems that address the challenges of machine learning algorithms?
Confined Liquid-Phase Growth of Crystalline Compound Semiconductors on Any Substrate (ACS Nano, June 2018)
Abstract: 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 atomically sharp lateral heterojunction 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.
Abstract: Neuromorphic or “brain-like” 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 via 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 via back-end processing on a Si wafer using a SiO2 buffer without the need for a crystalline seed, 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 (e.g., 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.
Materials Thrust: Templated Liquid Phase Growth
Our key innovation in this area is the ability to grow crystalline semiconductors directly on non-epitaxial surfaces.
Our Growth Method
Here, we show the general method for our templated liquid phase (TLP) growth approach. The top row shows a schematic of indium circles being phase transformed into single crystal InP, while the bottom row shows SEM images of our process.
Transmission Electron Microscopy
Transmission electron microscopy shows the interface between our grown InP and an a non-epitaxial substrate. The left image shows the interface between InP, graphene, and SiO2. The right images show high resolution imaging of the InP lattice and selected area diffraction.
Photoluminescence spectra allow us to understand the quality of our grown material. The left panel shows our grown InP compared to a single crystalline wafer. The right panel shows PL spectra of InGaP grown by us with different compositions.