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 Toulouse University publishes a PhD thesis "Defects in silicon: revisiting theoretical frameworks to guide ab initio characterization" by Gabriela Herrero-Saboya.

"In this thesis, we describe the effect of localized defects on the electronic properties of silicon. After 60 years of silicon devices production, one might expect all details of this material to be fully understood, especially considering that the manufacture of nowadays nanometer-sized transistors requires quasi-atomic accuracy. However, as a direct result of such extreme miniaturization, the accidental creation of even one single trapping center can be sufficient to alter the desired electronic properties of the sample, becoming one of the most feared phenomena in the industry. 

Atomistic numerical simulations in silicon, based on the Density Functional Theory, do however typically target specific defect-properties, not giving a complete theoretical picture of the system, often overlooking previous models and experimental evidence. In the present thesis, we provide new insight into iconic defects in silicon through the quantification of long-established atomistic models, making an explicit link with the characterization techniques. Our detailed exploration of the DFT energy surface of the silicon E-center, guided by a simple Jahn-Teller model, confirmed the observed defect-dynamics at different temperature regimes, allowing us to link the presence of such point-like defect to a burst noise in image sensors.

In section 3.2, we analyse the relevance of the silicon E-center for several technologically relevant processes, like the Dark-Current Random Telegraph Signal in image sensors. The former might be defined as a burst noise in electronic devices commonly linked to the finite-temperature dynamics of crystallographic defects, motivating an extensive exploration of the potential energy surface at different temperature regimes. Our DFT and NEB calculations, in excellent agreement with EPR spectroscopy, provide new insight into the defect dynamics, and in particular into the vacancy-mediated dopant diffusion mechanism in silicon."

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University Florida at Gainesville publishes MDPI paper "Towards An Efficient CNN Inference Architecture Enabling In-Sensor Processing" by Md Jubaer Hossain Pantho, Pankaj Bhowmik, and Christophe Bobda. The paper attracts the attention to the high power consumption of CNN processing that limits the possibilities of its integration onto image sensor:

"The astounding development of optical sensing imaging technology, coupled with the impressive improvements in machine learning algorithms, has increased our ability to understand and extract information from scenic events. In most cases, Convolution neural networks (CNNs) are largely adopted to infer knowledge due to their surprising success in automation, surveillance, and many other application domains. However, the convolution operations’ overwhelming computation demand has somewhat limited their use in remote sensing edge devices. In these platforms, real-time processing remains a challenging task due to the tight constraints on resources and power. Here, the transfer and processing of non-relevant image pixels act as a bottleneck on the entire system. It is possible to overcome this bottleneck by exploiting the high bandwidth available at the sensor interface by designing a CNN inference architecture near the sensor. This paper presents an attention-based pixel processing architecture to facilitate the CNN inference near the image sensor. We propose an efficient computation method to reduce the dynamic power by decreasing the overall computation of the convolution operations. The proposed method reduces redundancies by using a hierarchical optimization approach. The approach minimizes power consumption for convolution operations by exploiting the Spatio-temporal redundancies found in the incoming feature maps and performs computations only on selected regions based on their relevance score. The proposed design addresses problems related to the mapping of computations onto an array of processing elements (PEs) and introduces a suitable network structure for communication. The PEs are highly optimized to provide low latency and power for CNN applications. While designing the model, we exploit the concepts of biological vision systems to reduce computation and energy. We prototype the model in a Virtex UltraScale+ FPGA and implement it in Application Specific Integrated Circuit (ASIC) using the TSMC 90nm technology library. The results suggest that the proposed architecture significantly reduces dynamic power consumption and achieves high-speed up surpassing existing embedded processors’ computational capabilities."

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Science China publishes a letter "A variable threshold visual sensing and image reconstruction method based on pulse sequence" and supplementary materials by Jiangtao XU, Peng LIN, Zhiyuan GAO, Kaiming NIE & Liang XU from Tianjin University and Tianjin Key Laboratory of Imaging and Sensing Microelectronic Technology.

"Pulsed-based CMOS image sensor (CIS) outperforms classic CIS in terms of data rate, and is able to achieve ultra-highspeed imaging. A pulsed-based CIS which is able to capture the movement of the hard disk rotating at 6000 r/min, was designed in our previous work.

The structure and working process of the spiking pixel based on variable threshold are shown in Figure 1. The threshold curve consists of periodic ramps. Each ramp consists of many small steps, whose width is set equal to frame period Tu and height is written as Vs. The threshold voltage linearly increases from minimum value Vth0 to maximum value Vth,max by step height Vs every frame cycle during a ramp cycle. Every time a new ramp cycle starts, a ramp pulse is output by a global ramp generator."

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i-Micronews publishes an interview with Agnès Arnaud, head of the Optics and Photonics Department at CEA-Leti "CEA-Leti’s involvement in the CMOS Image Sensor ecosystem." Few quotes:

"CEA-Leti has been involved in CIS development since the mid-1990s. In the early 2000s, CEA-Leti had patents on CIS, including Analog-to-Digital Converters (ADCs), demoisaicing architectures and compression schemes. Some of these technologies have been transferred to the imaging division of STMicroelectronics. STMicroelectronics and CEA-Leti have cooperated for several years on technologies, leading to a boom in imaging applications for mobile telephones. CEA-Leti provided STMicroelectronics with a Through-Silicon-Via (TSV) technology block and processes to make thinner imagers, boost photon collection efficiency and develop innovative architectures. In 2012, a first scientific publication on a Global Shutter Pixel triggered a long-term collaboration on pixel technologies with the imaging division of STMicroelectronics. The cooperation is still on going on advanced concepts, from technologies like dense 3D interconnect announced at IEDM in 2019 to architectures like autonomous imagers announced at VLSI 2020.

Leti has been developing bolometric imagers since 1992 and transferred the technology to start-up Ulis in 2002. Ulis, now Lynred, is a world leading bolometer manufacturer.

You must keep in mind that many innovations require 15 to 20 years before one can find them in a commercial product. Our PhD students are currently working on innovations which may show up in a product in 2040.

Detection in the short wave IR (SWIR) band is very attractive for various applications, such as military, security, telecommunications and medical diagnostics. The SWIR light presents many advantages compared to visible light. It is invisible to the human eye and is less sensitive to extreme weather conditions such as fog and dust. The use of  germanium (Ge) as an active layer in the PiN photodiodes presents many advantages such as its good absorption and its compatibility with the mass-production processes used in the silicon (Si) microelectronics industry. At CEA-Leti, Ge/Si vertical PiN devices have been developed, fabricated, and characterized at room temperature with promising performances such as a low dark current density and good external responsivities. The segmentation of the NIR/SWIR market will ultimately depend on the evolution of InGaAs costs and the improvement of Ge or organic performance. Ge and organic photodiodes are compatible with 300mm diameter silicon wafer production lines. Ge and organic photodiodes are low-cost solutions. Compliance with IC manufacturing make them attractive candidates for consumer products. Yet their intrinsic performance, especially in terms of dark current, is still below InGaAs detectors. And as you know, CEA-Leti is also involved in InGaAs developments. It is CEA-Leti’s mission to investigate the ultimate potential of Ge or organic detectors to offer a reasoned set of technologies.

At CES 2019, CEA-Leti demonstrated a new bioinspired technology for visible image sensors, IR sensors and microdisplays that replicates the curve of the human retina.

This curved image sensor technology breakthrough, called Pixcurve, has several advantages compared to traditional flat image sensors. The form factor of a digital camera module can be reduced by 60% thanks to the reduction of the number of lens elements.

The overall length of the optical system is also shorter. Curved image sensors reduce the cost of the camera module. A lot of markets could be targeted by Pixcurve approach, such as high-end photography, automotive, consumers application or medical."

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BusinessWire: OmniVision announces the OH0TA OVMed medical image sensor—with a package size of just 0.55mm x 0.55mm, featuring a 1.0um pixel and a 1/31” optical format—smaller than the Guinness World Record held by its predecessor for the “Smallest Commercially Available Image Sensor.” The OH0TA also quadruples the RGB image resolution to 400x400, or 160 K Pixels, at 30 fps while reducing the power consumption by 20% to 20mW. This allows designers to add ultra-compact visualization to single-use and reusable endoscopes, as well as catheters and guidewires, with a small outer diameter of 1-2mm. Alternatively, this sensor’s uniquely small size gives medical device OEMs the flexibility to create a larger-diameter scope with a larger working channel.

“The trend toward minimally invasive procedures continues to grow, due to their greater success rates and shorter patient recovery times. However, for the narrowest areas of the anatomy, particularly in neuro and cardiac surgeries, previous sensors did not have the necessary combination of high resolution and extremely small size,” said Ehsan Ayar, medical product marketing manager at OmniVision. “The OH0TA is the world’s first sensor to offer this combination, enabling significant endoscope improvements, especially in comparison to traditional videoscopes made with optical fibers, which have limited resolution, poor imager quality and high cost.”

To achieve this increase in resolution, along with a smaller pixel size and optical format, the OH0TA is built on PureCel Plus-S stacked die technology. This pixel also provides sensitivity of 3600 mV/lux-sec, along with a SNR of 37.5dB. Additionally, PureCel Plus-S enables the OH0TA’s higher FWC, zero blooming and lower power consumption.

Other key features include a 15.5 degree CRA, enabling the use of lenses with high fields of view and short focus distances. It also supports a 4-wire interface, as well as raw analog data output, both of which can transmit via cables as long as 4 meters with minimal signal noise. For backward compatibility and easy adoption, this sensor interfaces with OmniVision’s existing OV426 ADC bridge chip. Additionally, it is autoclavable for reusable endoscope sterilization.

Samples of the OH0TA are available now, in OmniVision’s hCSP chip scale package with 100 micron thick cover glass and an anti-reflective coating.

BusinessWire: OmniVision also announces the OVMed OCHTA camera module based on the new sensor.

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TechInsights has held an excellent webinar on image sensor trends and comparisons on March 3, 2021. The webinar is available for on-demand access now. There is a lot of interesting information there. This is a second post about small pixels:

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Ametek Vision Research introduces the Phantom TMX Series, its first high-speed cameras that use BSI sensors to achieve up to 75 Gpx/sec speed and improve light sensitivity. 

The Phantom TMX Series consists of two models, TMX 7510 and TMX 6410. TMX 7510 achieves 76,000 fps at full 1MP resolution of 1280 x 800, over 300,000 fps at 1280 x 192 resolution, and over 770,000 fps at smaller resolutions. With the export-controlled FAST option, TMX 7510 reaches a 1.75 Million fps and 95ns minimum exposure time, eliminating motion blur.

“We’re excited to introduce this new class of high-speed performance to the market,” says Jay Stepleton, VP and GM of Vision Research. “In bringing BSI technology to high-speed applications in a new, cutting-edge sensor, we continue to advance high-speed capabilities through innovation. We designed the TMX cameras for speed, to support the very high frame rate requirements we see in many new and cutting-edge applications.”

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