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Efficient machine learning inference is essential for the rapid adoption of artificial intelligence across various domains.On-chip optical computing has emerged as a transformative solution for accelerating machine learning tasks, owing to…
A maskless post-processing technique for CMOS chips is developed that enables the fabrication of RF MEMS parallel-plate capacitors with a high quality factor and a very compact size. Simulations and measured results are presented for…
Comb-mode resolved spectral domain interferometry (CORE-SDI), which is capable of measuring length of kilometers or more with precision on the order of nanometers, is considered to be a promising technology for next-generation length…
Growing application space in optical communications, computing, and sensing continues to drive the need for high-performance integrated photonic components. Designing these on-chip systems with complex and application-specific functionality…
In this paper, we describe the principle of a multi-aperture interferometer that uses a phase-shifting technique and is suitable for quick, snapshot imaging of astrophysical objects at extreme angular resolution through Fourier inversion. A…
We present a novel architecture for sparse pattern processing, using flash storage with embedded accelerators. Sparse pattern processing on large data sets is the essence of applications such as document search, natural language processing,…
We propose advancing photonic in-memory computing through three-dimensional photonic-electronic integrated circuits using phase-change materials (PCM) and AlGaAs-CMOS technology. These circuits offer high precision (greater than 12 bits),…
Scalable interferometers lie at the heart of photonic quantum technologies, but their expansion has been fundamentally limited by optical losses that grow with circuit depth. Here, we introduce and experimentally demonstrate a…
Reconfigurable photonic devices are rapidly emerging as a cornerstone of next generation optical technologies, with wide ranging applications in quantum simulation, neuromorphic computing, and large-scale photonic processors. A central…
It has been recently demonstrated that integrated optics could enhance accuracy, stability, and ease of use of stellar interferometry techniques. The subject of this thesis is the study of an optical component based on singlemode waveguides…
We demonstrate a method to optically map multiple modes of mechanical structures simultaneously. The fast and robust method, based on a modified phase-lock-loop, is demonstrated on a silicon nitride membrane and compared with three…
We discuss a simple, linear, zero-delay implementation of spectral shearing interferometry for amplitude and phase characterization of optical frequency comb sources and arbitrary waveforms. We demonstrate this technique by characterizing…
Chemical accuracy serves as an important metric for assessing the effectiveness of the numerical method in Kohn--Sham density functional theory. It is found that to achieve chemical accuracy, not only the Kohn--Sham wavefunctions but also…
Tensor processing is the cornerstone of modern technological advancements, powering critical applications in data analytics and artificial intelligence. While optical computing offers exceptional advantages in bandwidth, parallelism, and…
Digital signal processing (DSP) is supporting novel in-field applications of optical interferometry, such as in laser ranging and distributed acoustic sensing. While the highest performances are achieved with field-programmable gated arrays…
Using large-scale multicore systems to get the maximum performance and energy efficiency with manageable programmability is a major challenge. The partitioned global address space (PGAS) programming model enhances programmability by…
Neuromorphic photonics has recently emerged as a promising hardware accelerator, with significant potential speed and energy advantages over digital electronics, for machine learning algorithms such as neural networks of various types.…
Memristive devices hold promise to improve the scale and efficiency of machine learning and neuromorphic hardware, thanks to their compact size, low power consumption, and the ability to perform matrix multiplications in constant time.…
This paper describes a low-power processor tailored for fast Fourier transform computations where transport triggering template is exploited. The processor is software-programmable while retaining an energy-efficiency comparable to existing…
Optical I/O technologies have emerged as a potential industrial solution for high-performance data interconnection in AI/ML computing acceleration. While optical I/Os are deployed at the edge of computational chips by co-packaged optics…