The heart of the camera is the sensor, and the steps involved in generating an image from photons to electrons to grey levels. For information on how an image is made, see our article of the same name. This article discusses the different camera sensor types and their specifications, including:

We see that the technologies in the S-curve’s early stages of innovation and experimenting are either on the leading edge of progress, such as quantum technologies and robotics, or are more relevant to a specific set of industries, such as bioengineering and space. Factors that could affect the adoption of these technologies include high costs, specialized applications, and balancing the breadth of technology investments against focusing on a select few that may offer substantial first-mover advantages.

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In addition, we updated the selection and definition of trends from last year’s report to reflect the evolution of technology trends:

While an EMCCD can multiply signal far above the reaches of read noise, these cameras are subject to other sources of noise, unique to EMCCDs. The number of photons a camera detects is not the same every second, as photons typically fall like rain rather than arrive at the sensor in regimented rows. This disparity between measurements is called photon shot noise. Photon shot noise and other sources of noise all exist in the signal as soon as it arrives on the sensor, and these noise sources are all multiplied up along with the signal, resulting in the Excess Noise Factor. The combination of random photon arrival and random EM multiplication leads to extra sources of error and noise, with all sources of noise (predominantly photon shot noise) being multiplied by a factor of 1.4x. While an EMCCD may eliminate read noise, it introduces its own sources of noise, impacting the signal-to-noise ratio and the ability of the camera to be sensitive.

The number of electrons is linearly proportional to the number of photons, allowing the camera to be quantitative. The design seen in Fig.2 is known as a full-frame CCD sensor, but there are other designs known as frame-transfer CCD and interline-transfer CCD that are shown in Fig.3.

This order also shows the chronological order of the introduction of these sensor types, we will go through these one at a time, in a journey through the history of scientific imaging.

Every stage that light has to travel through will scatter some light, meaning that the QE of front-illuminated cameras is often limited from 50-80%, even with microlenses specifically to focus light onto each pixel. Due to the additional electronics of CMOS sensors (miniaturized capacitor and amplifier on each pixel), there can be even more scattering.

The first step for a sensor is the conversion of photons of light into electrons (known as photoelectrons). The efficiency of this conversion is known as the quantum efficiency (QE) and is shown as a percentage.

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In addition, CMOS sensors had a large full well capacity, meaning they had a large dynamic range and could simultaneously image dark signals and bright signals, not subject to saturation or blooming like with a CCD.

A graph depicts the adoption curve of technology trends, scored from 1 to 5, where 1 represents frontier innovation, located at the bottom left corner of the curve; 2 is experimenting, located slightly above frontier innovation; 3 is piloting, which follows the upward trajectory of the curve; 4 is scaling, marked by a vertical ascent as adoption increases; and 5 is fully scaled, positioned at the top of the curve, indicating near-complete adoption.

CMOS technology is different to CCD and EMCCD, the main factor being parallelization, CMOS sensors operate in parallel and allow for much higher speeds.

EM gain decay or ageing is a phenomenon that is not fully understood, but essentially involves charge building up in the silicon sensor between the EM electrode and photodetector. This build-up of charge reduces the effect of EM gain, hence EM gain decay. The greater the initial signal intensity and the higher the EM gain, the faster the EM gain will decay. Using an EM gain of 1000x on a large signal would quickly result in EM gain decay. This results in the EM gain not being the same each time, leading to a lack of reproducibility in experiments, limiting the usefulness of the camera as a quantitative imaging tool. EMCCDs essentially have limited lifespans and require regular calibration, leading to these cameras needing to be used in a certain way, limiting the EM gain that can be used in an experiment without damaging the camera. When a camera has been purchased and will be used daily in a research lab, it can be disappointing to learn that the camera will become less and less reliable over time.

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This combination of front-illumination, split sensors, patterns/artifacts, and smaller pixels all led to early sCMOS lacking in sensitivity.

All the sensor types discussed here operate based on the fact that all electrons have a negative charge (the electron symbol being e-). This means that electrons can be attracted using a positive voltage, granting the ability to move electrons around a sensor by applying a voltage to certain areas of the sensor, as seen in Figure 1.

Finally, the large pixels of an EMCCD lead to these cameras having a lower resolution than CCDs; EMCCDs have a small field of view due to their small sensors; and even today (20 years later) EMCCDs are still the most expensive format of scientific camera.

In this manner, electrons can be moved anywhere on a sensor, and are typically moved to an area where they can be amplified and converted into a digital signal, in order to be displayed as an image. However, this process occurs differently in each type of camera sensor.

In extreme cases (such as daylight illumination of a scientific camera), there is a charge overload in the output node, causing the output amplification chain to collapse, resulting in a zero (completely dark) image.

Footnote: Trend is more relevant to certain industries, resulting in lower overall adoption across industries compared with adoption within relevant industries.

2019119 — The working distance of a microscope, defined as the distance between the objective lens and the specimen, is controlled by moving the stage up ...

2024226 — Free moroccan channels could be accessible via https://github.com/iptv-org/iptv?tab=readme-ov-file#playlists, so no need for a paid IPTV service I think.

Our analysis examines quantitative measures of interest, innovation, investment, and talent to gauge the momentum of each trend. Recognizing the long-term nature and interdependence of these trends, we also delve into the underlying technologies, uncertainties, and questions surrounding each trend. (For more about new developments in our research, please see the sidebar “What’s new in this year’s analysis”; for more about the research itself, please see the sidebar “Research methodology.”)

These are among the findings in the latest McKinsey Technology Trends Outlook, in which the McKinsey Technology Council identified the most significant technology trends unfolding today. This research is intended to help executives plan ahead by developing an understanding of potential use cases, sources of value, adoption drivers, and the critical skills needed to bring these opportunities to fruition.

The trajectory of enterprise technology adoption is often described as an S-curve that traces the following pattern: technical innovation and exploration, experimenting with the technology, initial pilots in the business, scaling the impact throughout the business, and eventual fully scaled adoption (Exhibit 2). This pattern is evident in this year’s survey analysis of enterprise adoption conducted across our 15 technologies. Adoption levels vary across different industries and company sizes, as does the perceived progress toward adoption.

This combination of large pixels, back-illumination and electron multiplication makes EMCCDs extremely sensitive, far more so than CCDs.

2024110 — Focal length is an important concept in photography as it affects the magnification and framing of an image. It is measured in millimeters.

The authors wish to thank the following McKinsey colleagues for their contributions to this research: Aakanksha Srinivasan, Ahsan Saeed, Alex Arutyunyants, Alex Singla, Alex Zhang, Alizee Acket-Goemaere, An Yan, Anass Bensrhir, Andrea Del Miglio, Andreas Breiter, Ani Kelkar, Anna Massey, Anna Orthofer, Arjit Mehta, Arjita Bhan, Asaf Somekh, Begum Ortaoglu, Benjamin Braverman, Bharat Bahl, Bharath Aiyer, Bhargs Srivathsan, Brian Constantine, Brooke Stokes, Bryan Richardson, Carlo Giovine, Celine Crenshaw, Daniel Herde, Daniel Wallance, David Harvey, Delphine Zurkiya, Diego Hernandez Diaz, Douglas Merrill, Elisa Becker-Foss, Emma Parry, Eric Hazan, Erika Stanzl, Everett Santana, Giacomo Gatto, Grace W Chen, Hamza Khan, Harshit Jain, Helen Wu, Henning Soller, Ian de Bode, Jackson Pentz, Jeffrey Caso, Jesse Klempner, Jim Boehm, Joshua Katz, Julia Perry, Julian Sevillano, Justin Greis, Kersten Heineke, Kitti Lakner, Kristen Jennings, Liz Grennan, Luke Thomas, Maria Pogosyan, Mark Patel, Martin Harrysson, Martin Wrulich, Martina Gschwendtner, Massimo Mazza, Matej Macak, Matt Higginson, Matt Linderman, Matteo Cutrera, Mellen Masea, Michiel Nivard, Mike Westover, Musa Bilal, Nicolas Bellemans, Noah Furlonge-Walker, Obi Ezekoye, Paolo Spranzi, Pepe Cafferata, Robin Riedel, Ryan Brukardt, Samuel Musmanno, Santiago Comella-Dorda, Sebastian Mayer, Shakeel Kalidas, Sharmila Bhide, Stephen Xu, Tanmay Bhatnagar, Thomas Hundertmark, Tinan Goli, Tom Brennan, Tom Levin-Reid, Tony Hansen, Vinayak HV, Yaron Haviv, Yvonne Ferrier, and Zina Cole.

They also wish to thank the external members of the McKinsey Technology Council for their insights and perspectives, including Ajay Agrawal, Azeem Azhar, Ben Lorica, Benedict Evans, John Martinis, and Jordan Jacobs.

Finally, CCD sensors are typically quite small, with an 11-16 mm diagonal, which limits the field of view that can be displayed on the camera and means that not all of the information from the microscope can be captured by the camera.

4types ofcamera

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Back-illumination allows for a large increase in camera QE across wavelengths from UV to IR, due to the way that light can access the camera sensor. Figure 9 highlights the differences between a front-illuminated and back-illuminated camera sensor.

Despite the advantages of electron multiplication, it introduces a lot of complexity to the camera and leads to several major downsides. The main technological issues are EM Gain Decay, EM Gain Stability and Excess Noise Factor.

The process of scaling technology adoption also requires a conducive external ecosystem where user trust and readiness, business model economics, regulatory environments, and talent availability play crucial roles. Since these ecosystem factors vary by geography and industry, we see different adoption scenarios playing out. For instance, while the leading banks in Latin America are on par with their North American counterparts in deploying gen AI use cases, the adoption of robotics in manufacturing sectors varies significantly due to differing labor costs affecting the business case for automation.

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In a CCD, after exposure to light and conversion of photons to photoelectrons, the electrons are moved down the sensor row by row until they reach an area that isn't exposed to light, the readout register. Once moved into the readout register, photoelectrons are moved off one by one into the output node. In this node they are amplified into a readable voltage, converted into a digital grey level using the analogue to digital converter (ADC) and sent to the computer via the imaging software.

Despite an overall downturn in private equity investment, the pace of innovation has not slowed. Innovation has accelerated in the three trends that are part of the “AI revolution” group: gen AI, applied AI, and industrializing machine learning. Gen AI creates new content from unstructured data (such as text and images), applied AI leverages machine learning models for analytical and predictive tasks, and industrializing machine learning accelerates and derisks the development of machine learning solutions. Applied AI and industrializing machine learning, boosted by the widening interest in gen AI, have seen the most significant uptick in innovation, reflected in the surge in publications and patents from 2022 to 2023. Meanwhile, electrification and renewable-energy technologies continue to capture high interest, reflected in news mentions and web searches. Their popularity is fueled by a surge in global renewable capacity, their crucial roles in global decarbonization efforts, and heightened energy security needs amid geopolitical tensions and energy crises.

Although many trends faced declines in investment and hiring in 2023, the long-term outlook remains positive. This optimism is supported by the continued longer-term growth in job postings for the analyzed trends (up 8 percent from 2021 to 2023) and enterprises’ continued innovation and heightened interest in harnessing these technologies, particularly for future growth.

By rotating the sensor and bringing the photodetector silicon layer to the front (from the 'back'), light has less distance to travel and there is less scattering, resulting in a much higher QE of >95%. While back-illumination was achieved earlier with some CCDs and most EMCCDs, it took longer for CMOS due to the complex electronics involved, and the specific thickness of silicon required to capture different wavelengths of light. Either way, the result is a good 15-20% QE increase at peak, and a 10-15% QE increase out to >1000 nm, doubling the sensitivity in these regions. The lack of microlenses also unlocked a new QE region from 200-400, great for UV imaging.

Mirrorless camera

This year, we reflected the shifts in the technology landscape with two changes on the list of trends: digital trust and cybersecurity (integrating what we had previously described as Web3 and trust architectures) and the future of robotics. Robotics technologies’ synergy with AI is paving the way for groundbreaking innovations and operational shifts across the economic and workforce landscapes. We also deployed a survey to measure adoption levels across trends.

Some early sCMOS, in an effort to run at a higher speed, featured a split sensor, where each half of the sCMOS sensor had its own set of ADCs and the camera image at speeds up to 100 fps. However, this split caused patterns and artifacts in the camera bias, which would be clearly visible in low-light conditions and would interfere with the signal, as seen in Figure 8.

Electrification and renewables was the other trend that bucked the economic headwinds, posting the highest investment and interest scores among all the trends we evaluated. Job postings for this sector also showed a modest increase.

In addition, CCDs have a small full-well capacity, meaning that the number of electrons that can be stored in each pixel is limited. If a pixel can only store 200 electrons, receiving a signal of >200 electrons leads to saturation, where a pixel becomes full and displayed the brightest signal, and blooming, where the pixel overflows and the excess signal is smeared down the sensor as the electrons are moved to the readout register.

For the interline-transfer CCD, a portion of each pixel is masked and not exposed to light. Upon exposure, the electron signal is shifted into this masked portion, and then sent to the readout register as normal. Similarly to the frame-transfer sensor, this helps increase the speed, as the exposed area can generate a new image while the original image is processed. However, each pixel in this sensor is smaller (as a portion is masked), and this decreases the sensitivity as fewer photons can be detected by smaller pixels. These sensors often come paired with microlenses to better direct light and improve the QE.

In a frame-transfer CCD the sensor is divided into two: the image array (where light from the sample hits the sensor) and the storage array (where signal is temporarily stored before readout). The storage array is not exposed to light, so when electrons are moved to this array, a second image can be exposed on the image array while the first image is processed from the storage array. The advantage is that a frame-transfer sensor can operate at greater speeds than a full-frame sensor, but the sensor design is more complex and requires a larger sensor (to accommodate the storage array), or the sensor is smaller as a portion is made into a storage array.

While MOS and CMOS technology has existed since before CCD (~1950's), only in 2009 did CMOS cameras become quantitative enough to be sufficient for scientific imaging, hence why CMOS cameras for science can be referred to as scientific CMOS or sCMOS.

Types of camerasCCTV

In a CMOS sensor there are miniaturized electronics on every single pixel, namely a capacitor and amplifier. This means that a photon is converted to an electron by the pixel, and then the electron is immediately converted to a readable voltage while still on the pixel. In addition, there is an ADC for every single column, meaning that each ADC has far less data to read out than a CCD/EMCCD ADC, which has to read out the entire sensor. This combination allows CMOS sensors to work in parallel, and process data much faster than CCD/EMCCD technologies. By moving electrons much slower than the potential max speed, CMOS sensors also have a much lower read noise than CCD/EMCCD, allowing them to perform low-light imaging and work with weak fluorescence or live cells.

In 2023, the trends are positioned along the adoption curve as follows: future of space technologies and quantum technologies are at the frontier innovation stage; climate technologies beyond electrification and renewables, future of bioengineering, future of mobility, future of robotics, and immersive-reality technologies are at the experimenting stage; digital trust and cybersecurity, electrification and renewables, industrializing machine learning, and next-gen software development are at the piloting stage; and advanced connectivity, applied AI, cloud and edge computing, and generative AI are at the scaling stage.

CCD pixels are also typically quite small (such as ~4 µm) meaning that while these sensors can achieve a high resolution, they lack sensitivity, as a larger pixel can collect more photons. This limits signal collection and is compounded by the limited QE of front-illuminated CCDs, which often only reaches 75% at maximum.

The main issues with CCDs are their lack of speed and sensitivity, making it a challenge to perform low-light imaging or to capture dynamic moving samples.

EMVA1288 is an electronic measurement standard developed by the European Machine Vision Association (EMVA). Its purpose is to define the methods to measure ...

These early sCMOS sensors were front-illuminated and therefore had a limited QE (70-80%), further impacting their sensitivity.

The talent environment largely echoed the investment picture in tech trends in 2023. The technology sector faced significant layoffs, particularly among large technology companies, with job postings related to the tech trends we studied declining by 26 percent—a steeper drop than the 17 percent decrease in global job postings overall. The greater decline in demand for tech-trends-related talent may have been fueled by technology companies’ cost reduction efforts amid decreasing revenue growth projections. Despite this reduction, the trends with robust investment and innovation, such as gen AI, not only maintained but also increased their job postings, reflecting a strong demand for new and advanced skills. Electrification and renewables was the other trend that saw positive job growth, partially due to public sector support for infrastructure spending.

EMCCDs are also faster than CCDs. In CCDs, electrons are moved around the sensor at speeds well below the maximum possible speed, because the faster the electrons are shuttled about, the greater the read noise. Read noise is a fixed +/- value on every signal, if a CCD has a read noise of ±5 electrons and detects a signal of 10 electrons, it could be read out at anywhere between 5-15 electrons depending on the read noise. This has a big impact on sensitivity and speed, as CCDs move electrons slower in order to reduce read noise. However, with an EMCCD you can just multiply your signal up until the read noise has a negligible effect. This means that EMCCDs can move signal around at maximum speed, resulting in huge read noise values from 60-80 electrons, but signals are often multiplied by hundreds of times, meaning that the read noise impact is lessened. In this manner, EMCCDs can operate at much higher speeds than CCDs, achieving around 30-100 fps across the full-frame. This is only possible due to the EM Gain aspect of EMCCDs.

Scientific imaging technologies have continued to advance from CCD, to EMCCD, sCMOS, and back-illuminated sCMOS, in order to deliver the best speed, sensitivity, resolution, and field of view for your sample on your application. Choosing the most suitable camera technology for your imaging system can improve every aspect of your experiments and allow you to be quantitative in your research. While CCD and EMCCD technologies enjoyed popularity in scientific imaging, over the past few decades sCMOS technology has come to the fore as an ideal solution for imaging in life sciences.

To assess the development of each technology trend, our team collected data on five tangible measures of activity: search engine queries, news publications, patents, research publications, and investment. For each measure, we used a defined set of data sources to find occurrences of keywords associated with each of the 15 trends, screened those occurrences for valid mentions of activity, and indexed the resulting numbers of mentions on a 0–1 scoring scale that is relative to the trends studied. The innovation score combines the patents and research scores; the interest score combines the news and search scores. (While we recognize that an interest score can be inflated by deliberate efforts to stimulate news and search activity, we believe that each score fairly reflects the extent of discussion and debate about a given trend.) Investment measures the flows of funding from the capital markets into companies linked with the trend.

A segmented bar graph shows the adoption levels of tech trends in 2023 as a percentage of respondents. The trends are divided into 5 segments, comprising 100%: fully scaled, scaling, piloting, experimenting, and not investing. The trends are arranged based on the combined percentage sum of fully scaled and scaling shares. Listed from highest to lowest, these combined percentages are as follows:

Quantitative scientific cameras are vital for sensitive, fast imaging of a variety of samples for a variety of applications. Camera technologies have advanced over time, from the earliest cameras to truly modern camera technologies, which can push the envelope of what is possible in scientific imaging and allow us to see the previously unseen.

Overall, while CCDs were the first digital cameras, for scientific imaging purposes in the modern day they are lacking in speed, sensitivity and field of view.

Even with the short-term vicissitudes in talent demand, our analysis of 4.3 million job postings across our 15 tech trends underscored a wide skills gap. Compared with the global average, fewer than half the number of potential candidates have the high-demand tech skills specified in job postings. Despite the year-on-year decreases for job postings in many trends from 2022 to 2023, the number of tech-related job postings in 2023 still represented an 8 percent increase from 2021, suggesting the potential for longer-term growth (Exhibit 1).

Types of camerasfor photography

CCDs were the first digital cameras, being available since the 1970s for scientific imaging. CCD have enjoyed active use for a number of decades and were well suited to high-light applications such as cell documentation or imaging fixed samples. However, this technology was lacking in terms of sensitivity and speed, limiting the available samples that could be imaged at acceptable levels.

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10types ofcamera

Early sCMOS cameras featured much higher speeds and larger fields of view than CCD/EMCCD, and with a range of pixel sizes, there were CMOS cameras that imaged at very high resolution, especially compared to EMCCD. However, the large pixel and electron multiplication of EMCCDs meant that early sCMOS cameras couldn't rival EMCCD when it came to sensitivity. When it came to extreme low-light imaging or the need for sensitivity, EMCCD still had the edge.

Despite challenging overall market conditions in 2023, continuing investments in frontier technologies promise substantial future growth in enterprise adoption. Generative AI (gen AI) has been a standout trend since 2022, with the extraordinary uptick in interest and investment in this technology unlocking innovative possibilities across interconnected trends such as robotics and immersive reality. While the macroeconomic environment with elevated interest rates has affected equity capital investment and hiring, underlying indicators—including optimism, innovation, and longer-term talent needs—reflect a positive long-term trajectory in the 15 technology trends we analyzed.

EMCCDs first emerged onto the scientific imaging scene in 2000 with the Cascade 650 from Photometrics. EMCCDs offered faster and more sensitive imaging then CCDs, useful for low-light imaging or even photon counting.

In Fig.8 we can see the bias of a split sensor camera, showing a horizontal line separating the two halves of the sensor, along with the other horizontal scrolling lines. This is due to each sensor half never being exactly the same due to noise and fluctuations. This effect is exacerbated when 100 image frames are averaged, as seen in the lower image. Here the sensor split is also clear, as are vertical columns across the image. This is fixed pattern column noise and is again due to the ADC pairs of the sensor. This noise can interfere with signal in low-light conditions.

EMCCDs work in a very similar way to frame-transfer CCDs, where electrons move from the image array to the masked array, then onto the readout register. At this point the main difference emerges: the EM Gain register. EMCCDs use a process called impact ionisation to force extra electrons out of the silicon sensor, therefore multiplying the signal. This EM process occurs step-by-step, meaning users can choose a value between 1-1000 and have their signal be multiplied that many times in the EM Gain register. If an EMCCD detects a signal of 5 electrons and has an EM Gain set to 200, the final signal that goes into the output node will be 1000 electrons. This allows EMCCDs to detect extremely small signals, as they can be multiplied up above the noise floor as many times as a user desires.

CMOS sensors have also been adopted by the commercial imaging industry, meaning that nearly every smartphone camera, digital camera, or imaging device uses a CMOS sensor. This makes these sensors easier and cheaper to manufacture, allowing sCMOS cameras to feature large sensors and have much larger fields of view than CCD/EMCCD, to the point where some sCMOS cameras can capture all the information from the microscope.

In 2023, technology equity investments fell by 30 to 40 percent to approximately $570 billion due to rising financing costs and a cautious near-term growth outlook, prompting investors to favor technologies with strong revenue and margin potential. This approach aligns with the strategic perspective leading companies are adopting, in which they recognize that fully adopting and scaling cutting-edge technologies is a long-term endeavor. This recognition is evident when companies diversify their investments across a portfolio of several technologies, selectively intensifying their focus on areas most likely to push technological boundaries forward. While many technologies have maintained cautious investment profiles over the past year, gen AI saw a sevenfold increase in investments, driven by substantial advancements in text, image, and video generation.

As executives navigate these complexities, they should align their long-term technology adoption strategies with both their internal capacities and the external ecosystem conditions to ensure the successful integration of new technologies into their business models. Executives should monitor ecosystem conditions that can affect their prioritized use cases to make decisions about the appropriate investment levels while navigating uncertainties and budgetary constraints on the way to full adoption (see the “Adoption developments across the globe” sections within each trend or particular use cases therein that executives should monitor). Across the board, leaders who take a long-term view—building up their talent, testing and learning where impact can be found, and reimagining the businesses for the future—can potentially break out ahead of the pack.

The two trends that stood out in 2023 were gen AI and electrification and renewables. Gen AI has seen a spike of almost 700 percent in Google searches from 2022 to 2023, along with a notable jump in job postings and investments. The pace of technology innovation has been remarkable. Over the course of 2023 and 2024, the size of the prompts that large language models (LLMs) can process, known as “context windows,” spiked from 100,000 to two million tokens. This is roughly the difference between adding one research paper to a model prompt and adding about 20 novels to it. And the modalities that gen AI can process have continued to increase, from text summarization and image generation to advanced capabilities in video, images, audio, and text. This has catalyzed a surge in investments and innovation aimed at advancing more powerful and efficient computing systems. The large foundation models that power generative AI, such as LLMs, are being integrated into various enterprise software tools and are also being employed for diverse purposes such as powering customer-facing chatbots, generating ad campaigns, accelerating drug discovery, and more. We expect this expansion to continue, pushing the boundaries of AI capabilities. Senior leaders’ awareness of gen AI innovation has increased interest, investment, and innovation in AI technologies, such as robotics, which is a new addition to our trends analysis this year. Advancements in AI are ushering in a new era of more capable robots, spurring greater innovation and a wider range of deployments.

Types of camerasfor film

In addition, the EM gain process itself is not stable, different fluctuations can occur. One such example is EM gain being temperature-dependent, in order for EMCCDs to have reliable EM gain they typically operate at temperates from -60 ºC to -80 ºC, meaning they require extensive forced-air or liquid cooling. This all adds to the camera complexity and cost, especially if a liquid cooling rig needs to be installed with the camera.

Essentially, there are very few data readout channels for a CCD, meaning the data processing is slowed. Most CCDs operate at between 1-20 frames per second, as a CCD is a serial device and can only read the electron charge packets one at a time. Imagine a bucket brigade, where electrons can only be passed from area to area one at a time, or a theatre with only one exit but several million seats.

As technologies gain traction and move beyond experimenting, adoption rates start accelerating, and companies invest more in piloting and scaling. We see this shift in a number of trends, such as next-generation software development and electrification. Gen AI’s rapid advancement leads among trends analyzed, about a quarter of respondents self-reporting that they are scaling its use. More mature technologies, like cloud and edge computing and advanced connectivity, continued their rapid pace of adoption, serving as enablers for the adoption of other emerging technologies as well (Exhibit 3).

EMCCDs achieved this in a number of ways. The cameras are back-illuminated (increasing the QE to ~90%) and have very large pixels (16-24 µm), both of which greatly increase the sensitivity. The most significant addition, however, is the EM in EMCCD: electron multiplication.

In 2016 Photometrics released the first back-illuminated sCMOS camera, the Prime 95B. Back-illuminated (BI) sCMOS cameras greatly improve on sensitivity compared to early front-illuminated sCMOS, while retaining all the other CMOS advantages such as high speed, large field of view. The combination of a much higher QE due to back-illuminated (up to 95%, hence the name of the Prime 95B), the single sensor (no split), more varied pixel sizes, and a cleaner background, BI sCMOS is the all-in-one imaging solution.

BI sCMOS have a much greater signal collection ability than FI sCMOS due to the increase in QE and the elimination of patterns/artifacts with a clean background. Along with the low read noise, BI sCMOS is able to match and outperform EMCCD in sensitivity, as well as already featuring much higher speed, resolution, and larger field of view.

While EMCCDs greatly improved on the speed and sensitivity of CCDs, they brought their own issues and continued to limit the amount of information that could be obtained from the microscope.

Lareina Yee is a senior partner in McKinsey’s Bay Area office, where Michael Chui is a McKinsey Global Institute partner, Roger Roberts is a partner, and Mena Issler is an associate partner.

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