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Flexible Image Sensor Fabrication Based on NIPIN Phototransistors

Hyun Myung Kim, Gil Ju Lee, Min Seok Kim, Young Min Song
Gwangju Institute of Science and Technology, School of Electrical Engineering and Computer Science;

We present a detailed method to fabricate a deformable lateral NIPIN phototransistor array for curved image sensors. The phototransistor array with an open mesh form, which is composed of thin silicon islands and stretchable metal interconnectors, provides flexibility and stretchability. The parameter analyzer characterizes the electrical property of the fabricated phototransistor.

ISOCELL Image Sensor: Ultra-fine Pixel Technologies | Samsung

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In an arXiv preprint, Prof. Stanley Chan of Purdue University writes:

The one-bit quanta image sensor (QIS) is a photon-counting device that captures image intensities using binary bits. Assuming that the analog voltage generated at the floating diffusion of the photodiode follows a Poisson-Gaussian distribution, the sensor produces either a “1” if the voltage is above a certain threshold or “0” if it is below the threshold. The concept of this binary sensor has been proposed for more than a decade and physical devices have been built to realize the concept. However, what benefits does a one-bit QIS offer compared to a conventional multi-bit CMOS image sensor? Besides the known empirical results, are there theoretical proofs to support these findings? The goal of this paper is to provide new theoretical support from a signal processing perspective. In particular, it is theoretically found that the sensor can offer three benefits: (1) Low-light: One-bit QIS performs better at low-light because it has a low read noise and its one-bit quantization can produce an error-free measurement. However, this requires the exposure time to be appropriately configured. (2) Frame rate: One-bit sensors can operate at a much higher speed because a response is generated as soon as a photon is detected. However, in the presence of read noise, there exists an optimal frame rate beyond which the performance will degrade. A Closed-form expression of the optimal frame rate is derived. (3) Dynamic range: One-bit QIS offers a higher dynamic range. The benefit is brought by two complementary characteristics of the sensor: nonlinearity and exposure bracketing. The decoupling of the two factors is theoretically proved, and closed-form expressions are derived.

Pre-print available here: https://arxiv.org/pdf/2208.10350.pdf

The paper argues that, if implemented correctly, there are three main benefits:

1. Better SNR in low light

2. Higher speed (frame rate)

3. Better dynamic range

This paper has many interesting technical results and insights. It provides a balanced view in terms of the regimes where single-photon quanta image sensor provide benefits over conventional image sensors.

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The symposium has many co-located conferences with talks and papers of interest to image sensors community. Short courses on 3D imaging, image sensors and camera calibration, image quality quantification, ML/AI for imaging and computer vision are also being offered.

Please visit the symposium website at https://www.imaging.org/site/IST/IST/Conferences/EI/EI2023/EI2023.aspx for full program. Some interesting papers and talks are listed below.

Evaluation of image quality metrics designed for DRI tasks with automotive cameras, Valentine Klein, Yiqi LI, Claudio Greco, Laurent Chanas, and Frédéric Guichard, DXOMARK (France)

Driving assistance is increasingly used in new car models. Most driving assistance systems are based on automotive cameras and computer vision. Computer Vision, regardless of the underlying algorithms and technology, requires the images to have good image quality, defined according to the task. This notion of good image quality is still to be defined in the case of computer vision as it has very different criteria than human vision: humans have a better contrast detection ability than image chains. The aim of this article is to compare three different metrics designed for detection of objects with computer vision: the Contrast Detection Probability (CDP) [1, 2, 3, 4], the Contrast Signal to Noise Ratio (CSNR) [5] and the Frequency of Correct Resolution (FCR) [6]. For this purpose, the computer vision task of reading the characters on a license plate will be used as a benchmark. The objective is to check the correlation between the objective metric and the ability of a neural network to perform this task. Thus, a protocol to test these metrics and compare them to the output of the neural network has been designed and the pros and cons of each of these three metrics have been noted.

 

Designing scenes to quantify the performance of automotive perception systems, Zhenyi Liu1, Devesh Shah2, Alireza Rahimpour2, Joyce Farrell1, and Brian Wandell1; 1Stanford University and 2Ford Motor Company (United States)

We implemented an end-to-end simulation for perception systems, based on cameras, that are used in automotive applications. The open-source software creates complex driving scenes and simulates cameras that acquire images of these scenes. The camera images are then used by a neural network in the perception system to identify the locations of scene objects, providing the results as input to the decision system. In this paper, we design collections of test scenes that can be used to quantify the perception system’s performance under a range of (a) environmental conditions (object distance, occlusion ratio, lighting levels), and (b) camera parameters (pixel size, lens type, color filter array). We are designing scene collections to analyze performance for detecting vehicles, traffic signs and vulnerable road users in a range of environmental conditions and for a range of camera parameters. With experience, such scene collections may serve a role similar to that of standardized test targets that are used to quantify camera image quality (e.g., acuity, color).

 A self-powered asynchronous image sensor with independent in-pixel harvesting and sensing operations, Ruben Gomez-Merchan, Juan Antonio Leñero-Bardallo, and Ángel Rodríguez-Vázquez, University of Seville (Spain)

A new self-powered asynchronous sensor with a novel pixel architecture is presented. Pixels are autonomous and can harvest or sense energy independently. During the image acquisition, pixels toggle to a harvesting operation mode once they have sensed their local illumination level. With the proposed pixel architecture, most illuminated pixels provide an early contribution to power the sensor, while low illuminated ones spend more time sensing their local illumination. Thus, the equivalent frame rate is higher than the offered by conventional self-powered sensors that harvest and sense illumination in independient phases. The proposed sensor uses a Time-to-First-Spike readout that allows trading between image quality and data and bandwidth consumption. The sensor has HDR operation with a dynamic range of 80 dB. Pixel power consumption is only 70 pW. In the article, we describe the sensor’s and pixel’s architectures in detail. Experimental results are provided and discussed. Sensor specifications are benchmarked against the art.

KEYNOTE: Deep optics: Learning cameras and optical computing systems, Gordon Wetzstein, Stanford University (United States)

Neural networks excel at a wide variety of imaging and perception tasks, but their high performance also comes at a high computational cost and their success on edge devices is often limited. In this talk, we explore hybrid optical-electronic strategies to computational imaging that outsource parts of the algorithm into the optical domain or into emerging in-pixel processing capabilities. Using such a co-design of optics, electronics, and image processing, we can learn application-domain-specific cameras using modern artificial intelligence techniques or compute parts of a convolutional neural network in optics with little to no computational overhead. For the session: Processing at the Edge (joint with ISS).

Computational photography on a smartphone, Michael Polley, Samsung Research America (United States)

Many of the recent advances in smartphone camera quality and features can be attributed to computational photography. However, the increased computational requirements must be balanced with cost, power, and other practical concerns. In this talk, we look at the embedded signal processing currently applied, including new AI-based solutions in the signal chain. By taking advantage of increasing computational performances of traditional processor cores, and additionally tapping into the exponentially increasing capabilities of the new compute engines such as neural processing units, we are able to deliver on-device computational imaging. For the session: Processing at the Edge (joint with ISS).

Analog in-memory computing with multilevel RRAM for edge electronic imaging application, Glenn Ge, Teramem Inc. (United States)

Conventional digital processors based on the von Neumann architecture have an intrinsic bottleneck in data transfer between processing and memory units. This constraint increasingly limits performance as data sets continue to grow exponentially for the various applications, especially for the Electronic Imaging Applications at the edge, for instance, the AR/VR wearable and automotive applications. TetraMem addresses this issue by delivering state-of-the-art in-memory computing using our proprietary non-volatile computing devices. This talk will discuss how TetraMem’s solution brings several orders of magnitude improvement in computing throughput and energy efficiency, ideal for those AI fusion sensing applications at the edge. For the session: Processing at the Edge (joint with ISS).

Processing of real time, bursty and high compute iToF data on the edge (Invited), Cyrus Bamji, Microsoft Corporation (United States)

In indirect time of flight (iToF), a depth frame is computed from multiple image captures (often 6-9 captures) which are composed together and processed using nonlinear filters. iToF sensor output bandwidth is high and inside the camera special purpose DSP hardware significantly improves power, cost and shuffling around of large amounts of data. Usually only a small percentage of depth frames need application specific processing and highest quality depth data both of which are difficult to compute within the limited hardware resources of the camera. Due to the sporadic nature of these compute requirements hardware utilization is improved by offloading this bursty compute to outside the camera. Many applications in the Industrial and commercial space have a real time requirement and may even use multiple cameras that need to be synchronized. These real time requirements coupled with the high bandwidth from the sensor makes offloading the compute purely into the cloud difficult. Thus, in many cases the compute edge can provide a goldilocks zone for this bursty high bandwidth and real-time processing requirement. For the session: Processing at the Edge (joint with ISS)..

A 2.2um three-wafer stacked back side illuminated voltage domain global shutter CMOS image sensor, Shimpei Fukuoka, OmniVision (Japan)

Due to the emergence of machine vision, augmented reality (AR), virtual reality (VR), and automotive connectivity in recent years, the necessity for chip miniaturization has grown. These emerging, next-generation applications, which are centered on user experience and comfort, require their constituent chips, devices, and parts to be smaller, lighter, and more accessible. AR/VR applications, especially demand smaller components due to their primary application towards wearable technology, in which the user experience would be negatively impacted by large features and bulk. Therefore, chips and devices intended for next-generation consumer applications must be small and modular, to support module miniaturization and promote user comfort. To enable the chip miniaturization required for technological advancement and innovation, we developed a 2.2μm pixel pitch Back Side Illuminated (BSI) Voltage Domain Global Shutter (VDGS) image sensor with the three-wafer stacked technology. Each wafer is connected by Stacked Pixel Level Connection (SPLC) and the middle and logic wafers are connected using a Back side Through Silicon Via (BTSV). The separation of the sensing, charge storage, and logic functions to different wafers allows process optimization in each wafer, improving overall chip performance. The peripheral circuit region is reduced by 75% compared to the previous product without degrading image sensor performance. For the session: Processing at the Edge (joint with COIMG).

A lightweight exposure bracketing strategy for HDR imaging without access to camera raw, Jieyu Li¹, Ruiwen Zhen², and Robert L. Stevenson¹; ¹University of Notre Dame and ²SenseBrain Technology (United States)
A lightweight learning-based exposure bracketing strategy is proposed in this paper for high dynamic range (HDR) imaging without access to camera RAW. Some low-cost, power-efficient cameras, such as webcams, video surveillance cameras, sport cameras, mid-tier cellphone cameras, and navigation cameras on robots, can only provide access to 8-bit low dynamic range (LDR) images. Exposure fusion is a classical approach to capture HDR scenes by fusing images taken with different exposures into a 8-bit tone-mapped HDR image. A key question is what the optimal set of exposure settings are to cover the scene dynamic range and achieve a desirable tone. The proposed lightweight neural network predicts these exposure settings for a 3-shot exposure bracketing, given the input irradiance information from 1) the histograms of an auto-exposure LDR preview image, and 2) the maximum and minimum levels of the scene irradiance. Without the processing of the preview image streams, and the circuitous route of first estimating the scene HDR irradiance and then tone-mapping to 8-bit images, the proposed method gives a more practical HDR enhancement for real-time and on-device applications. Experiments on a number of challenging images reveal the advantages of our method in comparison with other state-of-the-art methods qualitatively and quantitatively.

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Information and registration: https://www.espros.com/tof-lidar-technology-day-2023/

The TOF & LiDAR Technology Day — Powered by ESPROS, is carefully aimed at giving engineers and designers a very valuable hands-on, informative dive into the huge potential of TOF and LiDAR applications and eco-systems. Participants are assured of an eye-opening immersion into the ever expanding world of Time-of-Flight and LiDAR.

Thanks to the experience and quality of expert speakers who will be on hand to guide and inform everyone taking part, these comprise: Danny Kent, PhD, Co-Founder & President, Mechaspin, alongside Beat De Coi, CEO & Founder of ESPROS Photonics AG, and Len Cech, Executive Director, Safety Innovations at Joyson Safety Systems as well as Kurt Brendley, COO & Co-Founder, PreAct.
The TOF & LIDAR Technology Day event takes place on January 30th, 2023 in San Carlos California USA. 

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Xenics acquired by Photonis

https://www.imveurope.com/news/xenics-bought-photonis-infrared-tech

Infrared imager maker, Xenics, has been acquired by Photonis, a manufacturer of electro-optic components.

Photonis’ components are used in the detection and amplification of ions, electrons and photons for integration into a variety of applications such as night vision optics, digital cameras, mass spectrometry, physics research, space exploration and many others. The addition of Xenics will bring high-end imaging products to Photonis’ B2B customers.

Jérôme Cerisier, CEO of Photonis, said: “We are thrilled to welcome Paul Ryckaert and the whole Xenics team in Photonis Group. With this acquisition, we are aiming to create a European integrated leader in advanced imaging in high-end markets. We will together combine our forces to strengthen our position in the infrared imaging market.”

Xenics employs 65 people across the world and its headquarters based in Leuven, Belgium.
Paul Ryckaert, CEO of Xenics, said: “By combining its strengths with the ones of Photonis Group, Xenics will benefit from Photonis expertise and international footprint which will allow us to accelerate our growth. It is a real opportunity to boost our commercial, product development and manufacturing competences and bring even more added value to our existing and future customers.” 

[Post title has been corrected as of January 8. Thanks to the commenters for pointing it out. Apologies for the error. --AI]

OmniVision to cut costs

https://www.digitimes.com/news/a20221220PD210/cmos-image-sensor-omnivision.html

https://baijiahao.baidu.com/s?id=1752703911701757853&wfr=spider 

According to domestic media reports, the world's top ten IC design company China Weir Semiconductor, and its CMOS image sensor subsidiary OmniVision recently announced that it will stop recruiting new employees, reduce salaries for senior management, stop work during the Spring Festival, and stop distributing various items. The bonus will be used to reduce the capital expenditure in 2023 by 20% in response to the impact of the current bad environment on the company's operations. 

The report pointed out that according to the internal documents released by OmniVision Technology, the company has announced cost control, and the goal is to reduce costs by 20% in 2023! OmniVision said, "The current market conditions are very severe. We are facing great market challenges. Prices, inventories and supply chains are all under great pressure. Therefore, we must carry out cost control, and the goal is to reduce costs in 2023. 20%."

In order to achieve the goal of cost reduction, OmniVision announced that it will take a series of measures, including stopping all recruitment, no replacement for resignation, salary reduction for senior management, suspension of work in all regions of the group during the Spring Festival, suspension of quarterly bonuses and any other forms Bonuses, tight controls on spending, and some research and development programs will also reduce spending. OmniVision emphasized, "These measures are temporary. We believe that business-level improvements will occur in the second half of 2023, because we have a new product layout in the consumer market, while automobiles and emerging markets are rising steadily. We will be in 2023 The situation will be reassessed by the end of the first quarter of 2020."

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Original article available here: https://www.yolegroup.com/strategy-insights/cmos-image-sensor-snapshot-not-all-doom-and-gloom-good-news-is-also-stacking-up/

CMOS Image Sensor snapshot: not all doom and gloom, good news is also stacking up 

In the CMOS Image Sensor Monitor Q4 2022, Yole Intelligence, part of Yole Group, announces it expects the CMOS Image Sensors (CIS) industry to show a slight revenue decrease of -0.7% YoY in 2022, with a market value of $21.2B. This estimate takes into account the many events in 2022’s first 3 quarters; the downward revision of smartphone sales, the ongoing inventory reduction from most players in the electronics supply chains, and the continued Covid-19-related disruptions in China. 

2021 was a year of growth for CIS, reaching an all-time high of $21.3B in revenue with a relatively small annual growth of 2.8%. The key driver was the rebound in sales of smartphones, computer laptops, and tablets during the year amid the reopening of western economies after severe Covid-19-related lockdowns. Our hope for 2022 was a continuation of this improving trend. We knew the Huawei ban contributed to some inventory build-up in 2020, which had to be cleared in 2021 and maybe 2022. Our expectation for the smartphone market in 2022 was, unfortunately, too high, which translated directly into lost revenue for CIS.

In the past, the increase in the number of cameras per phone would more than compensate for smartphone volume sales declines, but not in 2022. Huawei was the actor adding the greatest number of cameras per phone, and losing such a player in the geopolitical battle has flattened the growth statistic of cameras per phone. Does it mean consumers have lost interest in high-quality phone cameras? Not at all!

 

Video creation using smartphones is at an all-time high due to the short-video craze. The emergence of TikTok, the favored social media of the younger generation, has been quickly copied by large incumbents, resulting in YouTube shorts and Facebook reels. This demand for high-quality video hardware was temporarily over-met during the out-of-Covid-19-lockdowns of 2021, and, therefore, the first 3 quarters of 2022 saw slightly less demand. We have seen even more dramatic but similar patterns with computer laptops and tablets in which cameras played a central role during remote work/school teleconferencing.

Another market that has explosive growth right now is Automotive CIS. The Covid-19 era signaled a turning point in consumer behavior, with demand switching to Connected Autonomous Shared and Electric (CASE) vehicles loaded with semiconductor-based features. Overall, the appetite for cameras remains high, but the dominance of the weakened smartphone market translates into the deceptive -0.7% CIS growth expected for 2022.

 

The smartphone market is down -10% but the sales of CIS have proven relatively resilient, while other semiconductor products, such as memory, are down -12%. The main reason is technical since we are currently experiencing a limited supply of 90nm to 40nm node wafers, the main nodes for CIS, and supporting logic wafers. The prices of these legacy nodes have increased significantly, and we observed, therefore, a continuation of high average selling prices (ASP) for CIS.

At the same time, we noted a product mix shift toward more resolution and larger optical formats; this means more silicon per die and higher ASPs. In this respect, the large smartphone OEMs have different approaches; Apple and Xiaomi favor 12Mp to 48Mp resolution with large pixels, which seems to be the ultra-premium favored approach, while Samsung, Oppo, and Vivo are increasing the resolution to 64Mp and even 108Mp with smaller pixels, which appears as the mid-end favored approach. The market is, therefore, relatively well educated and understands what a good picture means, as described in our publication with DXOMARK, “Ultra-Premium Flagship Smartphones Image Performance: End-User Perspective 2021”.

This year, both Sony and OmniVision have presented products with three-layer stacks. There are two technical reasons for this. First, the “in-pixel connection” allows removing some transistors from the upper wafer layer and moving these to the second wafer layer. This improves the volume of sensing silicon in each pixel. This technology is helpful in optimizing the signal-to-noise ratio (SNR), a critical factor in improving image quality. The second reason is that the triple stack enables high-performance sensing. New uses, such as tiny AR/VR cameras, must go beyond the current rolling-shutter (RS) approach and use either global-shutter (GS), time-of-flight (ToF), or even event-based (EB) cameras. All these require more transistors per pixel than RS approaches, so a second CIS layer is more than welcome in the drive to super compact sensing cameras. The market share of these triple-stack image sensors will grow, which will add again to the increasing silicon content per camera. This trend opens a path for sustained improvement and market growth for CIS.

The 8 leading CIS players – Sony, Samsung, OmniVision, STMicroelectronics, onsemi, SK Hynix, GalaxyCore, and SmartSens – that we have been monitoring every quarter have very different business models. Sony is a hybrid IDM, manufacturing its own 12’’ CIS wafers but outsourcing logic wafers to TSMC, UMC, and possibly also Global Foundry (unconfirmed as yet). Samsung, STMicroelectronics, and SK Hynix are IDMs with some open foundry activity. OmniVision, onsemi, GalaxyCore, and SmartSens, are fabless with varying degrees of desire for internalization; onsemi now having ownership of the East Fishkill, New York fab, and GalaxyCore investing the proceeds of its IPO into a brand new 12’’ foundry. All these players have felt pain from their supply chain structure in 2021 and 2022, either from their dependencies on others or their own limited or vulnerable capabilities. The drought and fires that happened in Samsung’s Austin, Texas, fab last year and the similar events that occurred in Taiwan’s TSMC fabs are clear reminders that no one is immune to supply-side issues in the context of climate change and geopolitical uncertainties.

The next few years will be a race to add new industrial capacities, combined with renewed technological capabilities and a high level of consumer demand. Predictions are very difficult, especially if it’s about the future! With our CIS monitor quarterly publication, we make sure to stick to reality and include some accountability in our forecast. In our view, the future is bright for CIS, but large vulnerabilities exist from the economic and geopolitical context. Let us all make this a well-informed journey with the CIS Monitor publications.

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A paper by Dodda et al. from a research group in the Material Science and Engineering department at Pennsylvania State University was recently published in Nature Materials. 

Link: https://www.nature.com/articles/s41563-022-01398-9

Active pixel sensor matrix based on monolayer MoS2 phototransistor array

Abstract:

In-sensor processing, which can reduce the energy and hardware burden for many machine vision applications, is currently lacking in state-of-the-art active pixel sensor (APS) technology. Photosensitive and semiconducting two-dimensional (2D) materials can bridge this technology gap by integrating image capture (sense) and image processing (compute) capabilities in a single device. Here, we introduce a 2D APS technology based on a monolayer MoS2 phototransistor array, where each pixel uses a single programmable phototransistor, leading to a substantial reduction in footprint (900 pixels in ∼0.09 cm2) and energy consumption (100s of fJ per pixel). By exploiting gate-tunable persistent photoconductivity, we achieve a responsivity of ∼3.6 × 107 A W−1, specific detectivity of ∼5.6 × 1013 Jones, spectral uniformity, a high dynamic range of ∼80 dB and in-sensor de-noising capabilities. Further, we demonstrate near-ideal yield and uniformity in photoresponse across the 2D APS array.

 

 Fig 1: 2D APS. a, 3D schematic (left) and optical image (right) of a monolayer MoS2 phototransistor integrated with a programmable gate stack. The local back-gate stacks, comprising atomic layer deposition grown 50 nm Al2O3 on sputter-deposited Pt/TiN, are patterned as islands on top of an Si/SiO2 substrate. The monolayer MoS2 used in this study was grown via an MOCVD technique using carbon-free precursors at 900 °C on an epitaxial sapphire substrate to ensure high film quality. Following the growth, the film was transferred onto the TiN/Pt/Al2O3 back-gate islands and subsequently patterned, etched and contacted to fabricate phototransistors for the multipixel APS platform. b, Optical image of a 900-pixel 2D APS sensor fabricated in a crossbar architecture (left) and the corresponding circuit diagram showing the row and column select lines (right).

Fig. 2: Characterization of monolayer MoS2. a, Structure of MoS2 viewed down its c axis with atomic-resolution HAADF-STEM imaging at an accelerating voltage of 80 kV. Inset: the atomic model of 2H-MoS2 overlayed on the STEM image. b, SAED of the monolayer MoS2, which reveals a uniform single-crystalline structure. c,d, XPS of Mo 3d (c) and S 2p (d) core levels of monolayer MoS2 film. e,f, Raman spectra (e) and corresponding spatial colourmap of peak separation between the two Raman active modes, E12g and A1g, measured over a 40 µm × 40 µm area, for as-grown MoS2 film (f). g,h, PL spectra (g) and corresponding spatial colourmap of the PL peak position (h), measured over the same area as in f. The mean peak separation was found to be ~20.2 cm−1 with a standard deviation of ~0.6 cm−1 and the mean PL peak position was found to be at ~1.91 eV with a standard deviation of ~0.002 eV. i, Map of the relative crystal orientation of the MoS2 film obtained by fitting the polarization-dependence of the SHG response shown in j, which is an example polarization pattern obtained from a single pixel of i by rotating the fundamental polarization and collecting the harmonic signal at a fixed polarization.
 Fig. 3: Device-to-device variation in the characteristics of MoS2 phototransistors. a, Transfer characteristics, that is, source to drain current (IDS) as a function of the local back-gate voltage (VBG), at a source-to-drain voltage (VDS) of 1 V and measured in the dark for 720 monolayer MoS2 phototransistors (80% of the devices that constitute the vision array) with channel lengths (L) of 1 µm and channel widths (W) of 5 µm. b–d, Device-to-device variation is represented using histograms of electron field-effect mobility values (μFE) extracted from the peak transconductance (b), current on/off ratios (rON/OFF) (c), subthreshold slopes (SS) over three orders of magnitude change in IDS (d) and threshold voltages (VTH) extracted at an isocurrent of 500 nA µm−1 for 80% of devices in the 2D APS array (e). f, Pre- and post-illumination transfer characteristics of 720 monolayer MoS2 phototransistors after exposure to white light with Pin = 20 W m−2 at Vexp = −3 V for τexp = 1 s. g–j, Histograms of dark current (IDARK) (green) and photocurrent (IPH) (yellow) (g), the ratio of post-illumination photocurrent to dark current (rPH) (h), responsivity (R) (i) and detectivity (D*) (j), all measured at VBG = −1 V.

Fig. 4: HDR and spectral uniformity. a–c, The post-illumination persistent photocurrent (IPH) read out using VBG = 0 V and VDS = 1 V under different exposure times (τexp) is plotted against Pin for Vexp = −2 V at red (a), green (b) and blue (c) wavelengths. Clearly, the 2D APS demonstrates HDR for all wavelengths investigated. d–f, However, the 2D APS displays spectral non-uniformity in the photoresponse, which can be adjusted by exploiting gate-tunable persistent photoconductivity, that is, by varying Vexp. This is shown by plotting IPH against Pin for different Vexp at red (d), green (e) and blue (f) wavelengths.

 Fig. 5: Photodetection metrics. a–c, Responsivity (R) as a function of Vexp and Pin for τexp = 100 ms for red (a), green (b) and blue (c) wavelengths. R increases monotonically with the magnitude of Vexp. d, Transfer characteristics of a representative 2D APS in the dark and post-illumination at Vexp = −6 V with Pin = 0.6 W m−2 for τexp = 200 s and VDS = 6 V. e, R as a function of VBG. For VDS = 6 V and VBG = 5 V we extract an R value of ~3.6 × 107 A W−1. f, Specific detectivity (D*) as a function of VBG at different VDS. At lower VBG, both R and Inoise, that is, the dark current obtained from d, are low, leading to lower D*, whereas at higher VBG both R and Inoise are high, also leading to lower D*. Peak D* can reach as high as ~5.6 × 1013 Jones. g, Energy consumption per pixel (E) as a function of Vexp.

Fig. 6: Fast reset and de-noising. a, After the read out, each pixel can be reset by applying a reset voltage (Vreset) for time periods as low as treset = 100 µs. b, The conductance ratio (CR), defined as the ratio between the conductance values before and after the application of a reset voltage, is plotted against different Vreset. c, Energy expenditure for reset operations under different Vreset. d, Heatmaps of conductance (G) measured at VBG = 0 V from the image sensor with and without Vreset when exposed to images under noisy conditions. Clearly, application of Vreset helps in de-noising image acquisition.
 

This work was covered in the IEEE Spectrum magazine in an article titled "New Pixel Sensors Bring Their Own Compute: Atomically thin devices that combine sensing and computation also save power".

Link: https://spectrum.ieee.org/active-pixel-sensor

In the new study, the researchers sought to add in-sensor processing to active pixel sensors to reduce their energy and size. They experimented with the 2D material molybdenum disulfide, which is made of a sheet of molybdenum atoms sandwiched between two layers of sulfur atoms. Using this light-sensitive semiconducting material, they aimed to combine image-capturing sensors and image-processing components in a single device.

The scientists developed a 2D active pixel sensor array in which each pixel possessed a single programmable phototransistor. These light sensors can each perform their own charge-to-voltage conversion without needing any extra transistors.

The prototype array contained 900 pixels in 9 square millimeters, with each pixel about 100 micrometers large. In comparison, state-of-the-art CMOS sensors from Omnivision and Samsung have reached about 0.56 µm in size. However, commercial CMOS sensors also require additional circuitry to detect low light levels, increasing their overall area, which the new array does not... .

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https://www.globenewswire.com/news-release/2022/12/20/2576935/0/en/VoxelSensors-and-OQmented-collaborate-on-laser-scanning-based-3D-perception-to-blend-the-physical-with-digital-worlds.html

BRUSSELS, Belgium and ITZEHOE, Germany, Dec. 20, 2022 (GLOBE NEWSWIRE) -- VoxelSensors, the inventor of Switching Pixels®, a revolutionary 3D perception technology, and OQmented, the technology leader in MEMS-based AR/VR display and 3D sensing solutions, have entered a strategic partnership. The collaboration focuses on the system integration and commercialization of a high-performance 3D perception system for AR/VR/MR and XR devices. Both companies will demonstrate this system and their technologies during CES 2023 in Las Vegas.

Switching Pixels® resolves major challenges in 3D perception for AR/VR/MR/XR devices. The solution is based on laser beam scanning (LBS) technology to deliver accurate and reliable 3D sensing without compromising on power consumption, data latency or size. VoxelSensors’ key patented technologies ensure optimal operation under any lighting condition and with concurrent systems. Their new sensor architecture provides asynchronous tracking of an active light source or pattern. Instead of acquiring frames, each pixel within the sensor array only generates an event upon detecting active light signals, with a repetition rate of up to 100 MHz.

This system is enabled through OQmented’s unique Lissajous scan pattern: in contrast to raster scanning which works line by line to complete a frame, the Lissajous trajectories scan much faster and are created very power efficiently. They can capture complete scenes and fast movements considerably quicker and require less data processing. That makes this particular technique essential for the low latency and the power efficiency of the combined perception system.

“The partnership with VoxelSensors is a great opportunity to unlock the potential of Lissajous laser beam scanning for 3D perception in lightweight Augmented Reality glasses,” said Ulrich Hofmann, co-CEO/CTO and co-founder of OQmented. “We are proud to deliver the most efficient scanning solution worldwide which enables the amazing products of our partner, bringing us one step closer to our goal of allowing product developers to build powerful but also stylish AR glasses.”

“At VoxelSensors, we wanted to revolutionize the perception industry. For too long, innovation in our space has focused on data processing, while there is so much efficiency to gain when working on the boundaries of photonics and physics. Combined with OQmented technology, we have the ability to transform the industry, enabling strong societal impact in multiple verticals, such as Augmented and Virtual Reality,” explains Johannes Peeters, founder and CEO of VoxelSensors. “Blending the physical and virtual worlds will create astonishing experiences for consumers and productivity gains in the enterprise world.”

This cooperation between two fabless deep tech semiconductor startups demonstrates Europe’s innovation capabilities in the race to produce next-generation technologies for AR/XR/VR and many other applications. These are crucial to Europe’s strategic objective of increasing its market share in semiconductors through key contributions of EU fabless companies as part of the European Chips Act.

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https://www.espros.com/espros-voted-no-1-optoelectronic-company-of-2022/ 

The Swiss company has been voted by the influential Semiconductor Review publication, going so far as to say ESPROS is “shaping a new paradigm of Time of Flight technologies”, with exceptional performance under full sunlight with moving objects and varying target reflectivity. ESPROS’ unique technology and its ability to help clients analyze an application and offer proven engineering solutions have ensured its growth as a custom ASIC chip manufacturer and 3D TOF module designer.

The company’s true system-on-chip TOF imager enables improved time delayed imaging and fluorescent lifetime imaging outcomes.

In the current scenario merging 3D imaging and optical sensors for mass applications requires very fast time resolving capabilities plus high sensitivity in NIR, conventional manufacturing processes are not robust enough dealing with background light movement and reflectivity. That’s where ESPROS has a major advantage having developed a backside-illuminated imager that merges CCD and CMOS technology.

The ESPROS approach means expensive peripheral components such as FPGAs and A/D converters are not required. This also means ESPROS products are both more cost effective and compact. ESPROS Photonics offers a wide range of TOF chips and line imagers as well as sensor modules, using its proprietary OHC15L silicon imager technology. Meanwhile, its off the shelf reference design 3D modules speed up a customer’s time to market.

Full article in Semiconductor Review available here: https://www.semiconductorreview.com/espros-photonics

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From Businesswire: https://www.businesswire.com/news/home/20221220005152/en/MagikEye-to-Present-Disruptive-3D-Sensing-with-Invertible-Light%E2%84%A2-Image-Sensor-Technology-at-CES

STAMFORD, Conn.--(BUSINESS WIRE)--Magik Eye Inc. (www.magik-eye.com), an innovative 3D sensing company will be holding demonstrations for its latest Invertible Light™ Technology (ILT) at the 2023 Consumer Electronics Show in Las Vegas Nevada. ILT is a patented alternative to older Time of Flight and Structured Light solutions, enabling the smallest, fastest and most power-efficient 3D sensing method. At its essence, ILT uses a patent protected regular dot projector pattern versus current random dot projection used by Structured Light. This allows for transformative simplicity of design, compute and form factor. “We see that the simplicity of ILT is driving demand for automotive and smarter home use cases. As we see more use cases opening up for the robotics age that lies ahead, we envision a world where there is 3D everywhere with ILT” said Takeo Miyazawa, Founder & CEO of MagikEye.

CES 2023 will take place in Las Vegas on Jan. 5-8, 2023. Attendees will experience new technologies from global brands, hear about the future of technology from thought leaders and collaborate face-to-face with other attendees. Live demonstrations of MagikEye’s latest ILT solutions for next-gen 3D sensing solutions will be held from January 5-8 at the Luxor Hotel. Demonstration times are limited and private reservations will be accommodated by contacting ces2023@magik-eye.com.

About Magik Eye Inc. www.magik-eye.com
Founded in 2015, Magik Eye Inc. has a family of 3D depth sensing solutions that support a wide range of applications for smartphones, robotic and surveillance. Magik Eye’s patent protected technology is based on Invertible Light™ that enables the smallest, fastest & most power-efficient 3D sensing.

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Yole published a webinar on SWIR imaging potential applications for mass market:

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Coverage in Wall St