Research Bits: August 22

Photonic memory

Researchers from Zhejiang University, Westlake University, and the Chinese Academy of Sciences developed a 5-bit photonic memory capable of fast volatile modulation and proposed a solution for a nonvolatile photonic network supporting rapid training.

This was made possible by integrating the low-loss phase-change material (PCM) antimonite (Sb2S3) into a silicon photonic platform.

The photonic memory utilizes the carrier dispersion effect of a PIN diode to achieve volatile modulation with a rapid response time of under 40 nanoseconds, preserving the stored weight information. After training, the photonic memory utilizes the PIN diode as a microheater to enable multilevel and reversible phase changes of Sb2S3, allowing the storage of trained weights in the photonic computing network. The team claims this leads to an energy-efficient photonic computing process.

Using the demonstrated photonic memory and working principle, the research team simulated an optical convolutional kernel architecture. They achieved over 95% accuracy in recognizing the MNIST dataset, showing the feasibility of fast training through volatile modulation and weight storage through 5-bit nonvolatile modulation.

Maoliang Wei, Junying Li, Zequn Chen, Bo Tang, Zhiqi Jia, Peng Zhang, Kunhao Lei, Kai Xu, Jianghong Wu, Chuyu Zhong, Hui Ma, Yuting Ye, Jialing Jian, Chunlei Sun, Ruonan Liu, Ying Sun, Wei. E. I. Sha, Xiaoyong Hu, Jianyi Yang, Lan Li, Hongtao Lin, “Electrically programmable phase-change photonic memory for optical neural networks with nanoseconds in situ training capability,” Adv. Photon. 5(4) 046004 (18 July 2023) https://doi.org/10.1117/1.AP.5.4.046004

Wires from proteins

Researchers from the University of Bristol and the City College of New York built conductive, biodegradable wires from designed proteins that could be compatible with conventional electronic components made from copper or iron, as well as the biological machinery responsible for generating energy in all living organisms.

The nanoscale wires are made of amino acids and heme molecules, found in proteins such as hemoglobin, and can be manufactured using bacteria.

“While our designs take inspiration from the protein-based electronic circuits necessary for all life on Earth, they are free from much of the complexity and instability that can prevent the exploitation of their natural equivalents on our own terms. We can also build these minute electronic components to order, specifying their properties in a way that is not possible with natural proteins,” said Ross Anderson, professor of biological chemistry at the University of Bristol.

The team designed simple building blocks that could be combined into longer, wire-like protein chains for conducting electrons. They were able to visualize the structures of these wires using protein X-ray crystallography and electron cryo-microscopy.

Potential applications include biosensors for the diagnosis of diseases and detection of environmental pollutants. The researchers also hope to spur development of new electrical circuits for creating tailor-made catalysts for green industrial biotechnology and artificial photosynthetic proteins for capturing solar energy.

George H. Hutchins, Claire E. M. Noble et al., An expandable, modular de novo protein platform for precision redox engineering. PNAS 120 (2023). https://doi.org/10.1073/pnas.2306046120

Heat-assisted detection and ranging

Researchers from Purdue University and Michigan State University propose using HADAR, or heat-assisted detection and ranging, to augment sensing capabilities of autonomous vehicles and robots.

In contrast to active sensors like lidar and radar, traditional thermal imaging is a fully passive sensing method that collects invisible heat radiation originating from all objects in a scene. It can sense through darkness, inclement weather and solar glare.

“Objects and their environment constantly emit and scatter thermal radiation, leading to textureless images famously known as the ‘ghosting effect,’” said Fanglin Bao, a research scientist at Purdue. “Thermal pictures of a person’s face show only contours and some temperature contrast; there are no features, making it seem like you have seen a ghost. This loss of information, texture and features is a roadblock for machine perception using heat radiation.”

To overcome those problems, the team designed HADAR, which combines thermal physics, infrared imaging, and machine learning for fully passive and physics-aware machine perception.

“HADAR vividly recovers the texture from the cluttered heat signal and accurately disentangles temperature, emissivity and texture, or TeX, of all objects in a scene. It sees texture and depth through the darkness as if it were day and also perceives physical attributes beyond RGB, or red, green and blue, visible imaging or conventional thermal sensing. It is surprising that it is possible to see through pitch darkness like broad daylight,” said Bao.

Bao noted that during nighttime testing, the system was able to overcome the ghosting effect. “It recovered fine textures such as water ripples, bark wrinkles and culverts in addition to details about the grassy land.”

The researchers have applied for a patent on the technology, but more work is still needed. “The current sensor is large and heavy since HADAR algorithms require many colors of invisible infrared radiation,” Bao said. “To apply it to self-driving cars or robots, we need to bring down the size and price while also making the cameras faster. The current sensor takes around one second to create one image, but for autonomous cars we need around 30 to 60 hertz frame rate, or frames per second.”

Bao, F., Wang, X., Sureshbabu, S.H. et al. Heat-assisted detection and ranging. Nature 619, 743–748 (2023). https://doi.org/10.1038/s41586-023-06174-6