A 64Mpixel CMOS Image Sensor with 0.56μm Unit Pixels Separated by Front Deep-Trench Isolation

Park, Sungbong; Lee, ChangKyu; Park, Sangcheon; Park, Haeyong; Lee, Taeheon; Park, Dami; Heo, Minsung; Park, In-Yong; Yeo, Hyunyoung; Lee, You-Na; Lee, Beomsuk; Lee, Dong Chul; Kim, Jin‐Young; Kim, Bokwon; Pyo, Jinsun; Quan, Shi-Li; You, Sungyong; Ro, Inho; Choi, Sungsoo; Kim, Sung In; Joe, In-Sung; Park, Jong-Eun; Koo, Chang-Hyo; Kim, Jae-Ho; Chang, Chong Kwang; Kim, Tae-Hee; Kim, JinGyun; Lee, Jamie; Kim, Hyunchul; Moon, Cheol; Kim, Hyoung-Sub

doi:10.1109/isscc42614.2022.9731750

Cited by 13 publications

(8 citation statements)

References 4 publications

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“…However, the growing demand for high-quality and high-resolution images has placed pressure on image sensors to process higher-resolution images pushing toward a deep submicron-scale pixel pitch to augment the resolution. Nonetheless, as pixel size diminishes, the amount of photons that captured by the photodiode decreases in proportion to the square of the pixel size, making it more vulnerable to undesired noises (1,2). This can result in a considerable decrease in signal-to-noise ratio (SNR) especially in low-light conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Although color routers have been designed using various methods, including spatial multiplexing (35), dispersion engineering (36,39), volumetric metaoptics (37,41), and genetic algorithms (40), these devices face challenges related to efficiency, low focusing quality, design degree of freedom, and fabrication difficulties. Highly integrated color image sensors, particularly those with deep submicron pixel sizes (1,2), require these nanophotonic solutions even more, but the design space becomes extremely small, making it challenging to solve the associated problems with these devices.…”

Section: Introductionmentioning

confidence: 99%

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Freeform metasurface color router for deep submicron pixel image sensors

Kim,

Hong,

Jang

et al. 2024

Sci. Adv.

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Advances in imaging technologies have led to a high demand for ultracompact, high-resolution image sensors. However, color filter–based image sensors, now miniaturized to deep submicron pixel sizes, face challenges such as low signal-to-noise ratio due to fewer photons per pixel and inherent efficiency limitations from color filter arrays. Here, we demonstrate a freeform metasurface color router that achieves ultracompact pixel sizes while overcoming the efficiency limitations of conventional architectures by splitting and focusing visible light instead of filtering. This development is enabled by a fully differentiable topology optimization framework to maximize the use of the design space while ensuring fabrication feasibility and robustness to fabrication errors. The metasurface can distribute an average of 85% of incident visible light according to the Bayer pattern with a pixel size of 0.6 μm. The device and design methodology enable the compact, high-sensitivity, and high-resolution image sensors for various modern technologies and pave the way for the advanced photonic device design.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Freeform metasurface color router for deep submicron pixel image sensors

Kim,

Hong,

Jang

et al. 2024

Sci. Adv.

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“…In the past 5 years, the pixel pitch of CMOS image sensors (CIS) has shrunk from 0.9 μm to 0.56 μm [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ] at a fast pace of almost one generation every year. Despite the pixel design and process development effort to maintain the quantum efficiency (QE) and the full well capacity (FWC) at comparable levels across the generations, the smaller pixels still face the fundamental physical challenge: under a given scene illumination and integration time, the number of photons that can be captured by smaller pixels is inevitably less.…”

Section: Introductionmentioning

confidence: 99%

“…In general, the known RN consists of the thermal noise, the flicker (1/f) noise, and the random telegraph noise (RTN). Although a median readout noise of 1 e-rms or less can be achieved by careful circuit design, the RTN on the noise distribution tail, typically dominated by the pixel source followers, could be more than 10 times higher than the median or average noise [ 1 , 2 , 3 , 10 , 17 , 18 , 19 , 20 , 21 , 22 ]. Therefore, understanding and reducing RTN are especially critical to achieving high dynamic range and good image quality for CIS products.…”

Section: Introductionmentioning

confidence: 99%

Random Telegraph Noise Degradation Caused by Hot Carrier Injection in a 0.8 μm-Pitch 8.3Mpixel Stacked CMOS Image Sensor

Chao,

Wu,

Yeh

et al. 2023

Sensors

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In this work, the degradation of the random telegraph noise (RTN) and the threshold voltage (Vt) shift of an 8.3Mpixel stacked CMOS image sensor (CIS) under hot carrier injection (HCI) stress are investigated. We report for the first time the significant statistical differences between these two device aging phenomena. The Vt shift is relatively uniform among all the devices and gradually evolves over time. By contrast, the RTN degradation is evidently abrupt and random in nature and only happens to a small percentage of devices. The generation of new RTN traps by HCI during times of stress is demonstrated both statistically and on the individual device level. An improved method is developed to identify RTN devices with degenerate amplitude histograms.

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“…Complimentary metal–oxide–semiconductor (CMOS) active pixel sensors (APS) with at least four transistors per pixel are currently the technology of choice for consumer electronics as well as scientific applications and emerging 3D imaging for specific use cases [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. CMOS APS architecture may vary between different designs tailored to specific applications.…”

Section: Introductionmentioning

confidence: 99%

Revisiting the Modeling of the Conversion Gain of CMOS Image Sensors with a New Stochastic Approach

Cherniak

Nemirovsky²,

Nemirovsky

2022

Sensors

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A stochastic model for characterizing the conversion gain of Active Pixel Complementary metal–oxide–semiconductor (CMOS) image sensors (APS) with at least four transistors is presented. This model, based on the fundamental principles of electronic noise, may provide a reliable calibration of the gain conversion, which is one of the most important parameters of CMOS Image Sensor pixels. The new model revisits the “gold standard” ratio method of the measured variance of the shot noise to the mean value. The model assumes that shot noise is the dominant noise source of the pixel. The microscopic random time-dependent voltage of any shot noise electron charging the junction capacitance C of the sensing node may have either an exponential form or a step form. In the former case, a factor of 1/2 appears in the variance to the mean value, namely, q/2C is obtained. In the latter case, the well-established ratio q/C remains, where q is the electron charge. This correction factor affects the parameters that are based on the conversion gain, such as quantum efficiency and noise. The model has been successfully tested for advanced image sensors with six transistors fabricated in a commercial FAB, applying a CMOS 180 nm technology node with four metals. The stochastic modeling is corroborated by measurements of the quantum efficiency and simulations with advanced software (Lumerical).

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A 64Mpixel CMOS Image Sensor with 0.56μm Unit Pixels Separated by Front Deep-Trench Isolation

Cited by 13 publications

References 4 publications

Freeform metasurface color router for deep submicron pixel image sensors

Freeform metasurface color router for deep submicron pixel image sensors

Random Telegraph Noise Degradation Caused by Hot Carrier Injection in a 0.8 μm-Pitch 8.3Mpixel Stacked CMOS Image Sensor

Revisiting the Modeling of the Conversion Gain of CMOS Image Sensors with a New Stochastic Approach

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