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The rapid growth of brain-inspired computing coupled with the inefficiencies in the CMOS implementations of neuromrphic systems has led to intense exploration of efficient hardware implementations of the functional units of the brain,…
In the last decade phase change materials (PCM) research has switched from practical application in optical data storage toward electrical phase change random access memory technologies (PCRAM). As these devices are commercialised, we…
Reconfigurability of photonic integrated circuits (PICs) has become increasingly important due to the growing demands for electronic-photonic systems on a chip driven by emerging applications, including neuromorphic computing, quantum…
Neuromorphic photonic computing represents a paradigm shift for next-generation machine intelligence, yet critical gaps persist in emulating the brain's event-driven, asynchronous dynamics,a fundamental barrier to unlocking its full…
Scalable programmable photonic integrated circuits (PICs) can potentially transform the current state of classical and quantum optical information processing. However, traditional means of programming, including thermo-optic, free carrier…
We introduce phase-change material Ge2Sb2Te5 (GST) into metal-insulator-metal (MIM) waveguide systems to realize chipscale plasmonic modulators and switches in the telecommunication band. Benefitting from the high contrast of optical…
We report an electrically actuated, low-loss non-volatile optical switch based on the phase-change material (PCM) Sb2Se3 integrated on a silicon nitride (Si3N4) platform. The device is fabricated using an 8-inch wafer-scale process flow,…
One of the most widely used active materials for phase-change memories (PCM), the ternary stoichiometric compound Ge$_2$Sb$_2$Te$_5$ (GST), has a low crystallization temperature of around 150$^\circ$C. One solution to achieve higher…
The integration of silicon photonics (SiPh) and phase change materials (PCMs) has created a unique opportunity to realize adaptable and reconfigurable photonic systems. In particular, the nonvolatile programmability in PCMs has made them a…
Current large scale implementations of deep learning and data mining require thousands of processors, massive amounts of off-chip memory, and consume gigajoules of energy. Emerging memory technologies such as nanoscale two-terminal…
Leveraging the latent heat of phase change materials (PCMs) can reduce the peak temperatures and transient variations in temperature in electronic devices. But as the power levels increase, the thermal conduction pathway from the heat…
The integration of computing with memory is essential for distributed, massively parallel, and adaptive architectures such as neural networks in artificial intelligence (AI). Accelerating AI can be achieved through photonic computing, but…
Inverse-designed nanophotonic devices offer promising solutions for analog optical computation. High-density photonic integration is critical for scaling such architectures toward more complex computational tasks and large-scale…
Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally…
DSP (digital signal processing) has been widely applied in optical communication systems to mitigate signal distortions and has become one of the key technologies that have sustained data traffic growth over the past decade. However, the…
With the recent advances in optical phase change material (PCM), photonic in-memory neurocomputing has demonstrated its superiority in optical neural network (ONN) designs with near-zero static power consumption, time-of-light latency, and…
Multiple-phase estimation exploiting quantum states has broad applications in novel sensing and imaging technologies. However, the unavoidable presence of lossy environments in practical settings often diminishes the precision of phase…
In-memory computing is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. Crossbar arrays of resistive memory devices can be used to encode the network weights and perform efficient analog…
As modern computing gets continuously pushed up against the von Neumann Bottleneck -- limiting the ultimate speeds for data transfer and computation -- new computing methods are needed in order to bypass this issue and keep our computer's…
Recent reports of large photonic nonlinearities in integrated photonic devices, using the strong excitonic light-matter coupling in semiconductors, necessitate a tailored design framework for quantum processing in the limit of low photon…