Back‐End‐Of‐Line Integrated Non‐Volatile Reconfigurable Devices for Programmable Photonic Integrated Circuits

ABSTRACT Chalcogenide phase change materials (PCMs) integrated photonics is characterized by zero static power consumption and compact footprint, offering a breakthrough tuning method for large‐scale programmable photonic integrated circuits (PICs). To facilitate the development of large‐scale PCMs‐based PICs, the performance requirements of units in non‐volatile PICs for specific applications are analyzed, and back‐end‐of‐line (BEOL) integrated non‐volatile devices with enhanced performance are proposed. First, loss reduction is proven to benefit reconfigurable filtering to a significant degree. With the integration of Sb 2 Se 3 , a low‐loss (0.38 dB π −1 ) phase shifter with high phase modulation efficiency (0.135π µm −1 ) is realized. Meanwhile, the participation of wide‐range attenuations enables more versatile programming, a Ge 2 Sb 2 Se 4 Te 1 ‐based attenuator is developed, achieving high switching endurance (>10 000 cycles) with a large extinction ratio (>40 dB). Second, a non‐volatile 2 × 2 Mach–Zehnder interferometer (MZI) is proposed, since lower switch crosstalk (<−20 dB) enables improved encoding performance with reduced bit error rates (BER). Finally, sufficient multilevel precision is crucial for optical computing because of the direct association between mapping errors and prediction accuracy. Accordingly, an add‐drop microring resonator (MRR) is fabricated, achieving a multilevel precision of 44 levels. This study presents an essential step to scaling up PCMs‐based PICs for diverse photonic applications.

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Publication Details

Journal
Laser & Photonics Review
Published
2025-11-11
DOI
https://doi.org/10.1002/lpor.202501846
Primary Topic
Neural Networks and Reservoir Computing
Type
article
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article

Back‐End‐Of‐Line Integrated Non‐Volatile Reconfigurable Devices for Programmable Photonic Integrated Circuits

Yingchun Wu, Hui Ma, Kai Xu, Chunlei Sun et al.
Laser & Photonics Review
Neural Networks and Reservoir Computing
article

Back‐End‐Of‐Line Integrated Non‐Volatile Reconfigurable Devices for Programmable Photonic Integrated Circuits

Yingchun Wu, Hui Ma, Kai Xu, Chunlei Sun, Kunhao Lei, W. B. Wang, Hongtao Lin, Ming Li, Lan Li, Tao Xu, Bo Tang, Junying Li, Yiting Yun, Maoliang Wei
article en

Abstract

ABSTRACT Chalcogenide phase change materials (PCMs) integrated photonics is characterized by zero static power consumption and compact footprint, offering a breakthrough tuning method for large‐scale programmable photonic integrated circuits (PICs). To facilitate the development of large‐scale PCMs‐based PICs, the performance requirements of units in non‐volatile PICs for specific applications are analyzed, and back‐end‐of‐line (BEOL) integrated non‐volatile devices with enhanced performance are proposed. First, loss reduction is proven to benefit reconfigurable filtering to a significant degree. With the integration of Sb 2 Se 3 , a low‐loss (0.38 dB π −1 ) phase shifter with high phase modulation efficiency (0.135π µm −1 ) is realized. Meanwhile, the participation of wide‐range attenuations enables more versatile programming, a Ge 2 Sb 2 Se 4 Te 1 ‐based attenuator is developed, achieving high switching endurance (>10 000 cycles) with a large extinction ratio (>40 dB). Second, a non‐volatile 2 × 2 Mach–Zehnder interferometer (MZI) is proposed, since lower switch crosstalk (<−20 dB) enables improved encoding performance with reduced bit error rates (BER). Finally, sufficient multilevel precision is crucial for optical computing because of the direct association between mapping errors and prediction accuracy. Accordingly, an add‐drop microring resonator (MRR) is fabricated, achieving a multilevel precision of 44 levels. This study presents an essential step to scaling up PCMs‐based PICs for diverse photonic applications.

Laser & Photonics ReviewVol. 20(6)
Chinese Academy of Sciences (CN), Westlake University (CN), State Key Laboratory on Integrated Optoelectronics (CN), Institute of Microelectronics (CN), Zhejiang Lab (CN), Communication University of Zhejiang (CN), University of Chinese Academy of Sciences (CN), Zhejiang University (CN)
Openalex Percentile: Top 82%
Neural Networks and Reservoir Computing
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