Mr. Alexander Y. Piggott Profile

Alexander Y. Piggott

at Pointcloud Inc

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Publications (2)

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Proceedings Article | 5 March 2021 Presentation + Paper

3D imaging via silicon-photonics-based LIDAR

Remus Nicolaescu, Christopher Rogers, Alexander Piggott, David Thomson, Ion Opris, Steven Fortune, Andrew Compston, Alexander Gondarenko, Fanfan Meng, Xia Chen, Graham Reed

Proceedings Volume 11691, 116910G (2021) https://doi.org/10.1117/12.2591284

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Accurate 3D imaging is essential for machines to map and interact with the physical world1,2. While numerous 3D imaging technologies exist, each addressing niche applications with varying degrees of success, none have achieved the breadth of applicability and impact that digital image sensors have achieved in the 2D imaging world3-10. A large-scale twodimensional array of coherent detector pixels operating as a light detection and ranging (LIDAR) system could serve as a universal 3D imaging platform. Such a system would other high depth accuracy and immunity to interference from sunlight, as well as the ability to directly measure the velocity of moving objects11. However, due to difficulties in providing electrical and photonic connections to every pixel, previous systems have been restricted to fewer than 20 pixels12-15. Here, we demonstrate the first large-scale coherent detector array consisting of 512 (32×16) pixels, and its operation in a 3D imaging system. Leveraging recent advances in the monolithic integration of photonic and electronic circuits, a dense array of optical heterodyne detectors is combined with an integrated electronic readout architecture, enabling straightforward scaling to arbitrarily large arrays. Meanwhile, two-axis solid-state beam steering eliminates any tradeoff between field of view and range. Operating at the quantum noise limit16,17, our system achieves an accuracy of 3.1 mm at a distance of 75 meters using only 4 mW of light, an order of magnitude more accurate than existing solid-state systems at such ranges. Future reductions of pixel size using state-of-the-art components could yield resolutions in excess of 20 megapixels for arrays the size of a consumer camera sensor. This result paves the way for the development and proliferation of low cost, compact, and high-performance 3D imaging cameras, enabling new applications from robotics and autonomous navigation to augmented reality and healthcare.

Proceedings Article | 26 April 2017 Presentation

Design grid optimization for OPC of silicon photonics (Conference Presentation)

Wenhui Wang, Xiaochi Chen, Lei Sun, Alexander Piggott, Jelena Vuckovic, Jongwook Kye

Proceedings Volume 10147, 1014710 (2017) https://doi.org/10.1117/12.2261428

KEYWORDS: Optical proximity correction, Silicon photonics, High volume manufacturing, Sun, Optical lithography, Current controlled current source

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The layout design for silicon photonics can be complicated and usually have edges with arbitrary angles. The critical dimension can be less than 100 nm, requiring the layouts to be OPCed in order to have large enough process windows for high volume manufacturing. However, the well-established CMOS-orientated IC industry OPC tools for advanced nodes can only handle Manhattan designs in which the Manhattan style polygons with edges of 0°, 90° or 45° to the reference direction. Silicon photonics layouts need to be discretized in order to use the existing OPC tools. From optical performance point of view, the design grid is expected to be as small as possible and it is usually from 1 nm to 5 nm. However, the design grid has never been optimized based on the OPC performance. In this paper, we demonstrate the impacts of design grid on the OPC performance. Design grid for silicon photonics is not always the smaller the better anymore. Our study shows that small 2D designs require large design grids while smooth curves with large radius require small design grids. We proposed a novel design-based discretization algorithm to convert a non-Manhattan style layout to an OPC-friendly Manhattan style layout. Simulation results show that the pattern fidelity is optimized for both small 2D patterns and smooth curves.

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