Recently, there has been news of collaboration between NVIDIA and MediaTek. Speculation suggests that the future collaboration may extend to smartphone SoCs, allowing MediaTek to enhance the graphical computing and AI performance of Dimensity smartphone SoCs through NVIDIA’s GPU technology licensing.

Currently, the focus of this collaboration is primarily on NB SoC development, with some progress in the automotive-related chip sector. As for the scope of smartphone SoC collaboration, it is still under discussion, but the potential for related partnerships is worth noting.

In the announced collaboration between NVIDIA and MediaTek for the NB SoC products, MediaTek is mainly responsible for CPU, while other part such as GPU, DSP, ISP, and interface IP are provided by NVIDIA or external partners. NVIDIA holds the leadership position, while MediaTek plays a supporting role in this collaboration.

Regarding the industry’s speculation about possible collaboration in smartphone SoC development, it is estimated that MediaTek will take the lead in the design. Therefore, it is necessary to explore the motivations behind MediaTek’s adoption of related technologies.

Firstly, since the era of the Arm V9 instruction set, Arm’s reference GPU, Immortalis, has incorporated ray tracing functionality, assisting MediaTek’s flagship SoCs in improving gaming performance. This indicates that optimizing gaming scenarios is a key development focus for SoC manufacturers.

However, for high-end gaming applications, the current GPU performance of smartphone SoCs still cannot maintain high frame rates and native resolutions during gameplay. While selecting a pure core stacking approach to improve computational power is effective, it puts pressure on device power consumption. In light of this, Qualcomm introduced Snapdragon Game Super Resolution (GSR) technology this year, which simultaneously reduces power consumption and enhances game graphics quality. MediaTek has not yet explored this technology, and Arm Immortalis has not been released. Therefore, when it comes to GPU performance computing, MediaTek has incentives to seek external collaborations.

Furthermore, with the rapid upgrading of GPUs on smartphone SoCs, PC-level games are now being introduced to smartphones, and industry players are promoting compatibility with graphics APIs, opening doors for NVIDIA, AMD, and even Intel to enter the mobile gaming market. Samsung has partnered with AMD for its Exynos SoC GPU, while NVIDIA, with similar technology to Qualcomm Snapdragon GSR, becomes a logical choice as a cooperation partner for MediaTek.

TrendForce believes that if MediaTek integrates NVIDIA GPUs into Dimensity SoCs and leverages TSMC’s process power efficiency advantages, it could bring a new wave of excitement to MediaTek in the flagship or gaming device market, attracting consumer interest. However, despite the potential technical benefits of collaboration, considering the influence of geopolitical factors, MediaTek, which primarily sells its smartphone SoCs to Chinese customers, may ultimately abandon this collaboration option due to related policy risks.

ChatGPT’s debut has sparked a thrilling spec upgrade in the server market, which has breathed new life into the supply chain and unlocked unparalleled business opportunities. Amidst all this, the big winners look set to be the suppliers of ABF (Ajinomoto Build-up Film) substrates, who are poised to reap enormous benefits.

In the previous article, “AI Sparks a Revolution Up In the Cloud,” we explored how the surge in data volumes is driving the spec of AI servers as well as the cost issue that comes with it. This time around, we’ll take a closer look at the crucial GPU and CPU platforms, focusing on how they can transform the ABF substrate market.

NVIDIA’s Dual-Track AI Server Chip Strategy Fuels ABF Consumption

In response to the vast data demands of fast-evolving AI servers, NVIDIA is leading the pack in defining the industry-standard specs.

This contrasts with standard GPU servers, where one CPU backs 2 to 6 GPUs. Instead, NVIDIA’s AI servers, geared towards DL(Deep Learning) and ML(Machine Learning), typically support 2 CPUs and 4 to 8 GPUs, thus doubling the ABF substrate usage compared to conventional GPU servers.

NVIDIA has devised a dual-track chip strategy, tailoring their offerings for international and Chinese markets. The primary chip for ChatGPT is NVIDIA’s A100. However, for China, in line with U.S. export regulations, they’ve introduced the A800 chip, reducing interconnect speeds from 600GBps (as on the A100) to 400GBps.

Their latest H100 GPU chip, manufactured at TSMC’s 4nm process, boasts an AI training performance 9 times greater than its A100 predecessor and inferencing power that’s 30 times higher. To match the new H100, H800 was also released with an interconnect speed capped at 300GBps. Notably, Baidu’s pioneering AI model, Wenxin, employs the A800 chip.

To stay competitive globally in AI, Chinese manufacturers are expected to aim for the computational prowess on par with the H100 and A100 by integrating more A800 and H800 chips. This move will boost the overall ABF substrate consumption.

With the ChatBot boom, it is predicted a 38.4% YoY increase in 2023’s AI server shipments and a robust CAGR of 22% from 2022 to 2026 – significantly outpacing the typical single-digit server growth, according to TrendForce’s prediction.

AMD, Intel Server Platforms Drive ABF Substrate Demand

Meanwhile, examining AMD and Intel’s high-end server platforms, we can observe how spec upgrades are propelling ABF substrate consumption forward.

• AMD Zen 4:

Since 2019, AMD’s EPYC Zen 2 server processors have used Chiplet multi-chip packaging, which due to its higher conductivity and cooling demands, has consistently bolstered ABF substrate demand.

• Intel Eagle Stream:

Intel’s advanced Eagle Stream Sapphire Rapids platform boasts 40-50% higher computation speed than its predecessor, the Whitley, and supports PCIe5, which triggers a 20% uptick in substrate layers. This platform employs Intel’s 2.5D EMIB tech and Silicon Bridge, integrating various chips to minimize signal transmission time.

The Sapphire Rapids lineup includes SPR XCC and the more advanced SPR HBM, with the latter’s ABF substrate area being 30% larger than the previous generation’s. The incorporation of EMIB’s Silicon Bridge within the ABF substrate increases lamination complexity and reduces overall yield. Simply put, for every 1% increase in Eagle Stream’s server market penetration, ABF substrate demand is projected to rise by 2%.

As the upgrades for server-grade ABF substrates continue to advance, production complexity, layer count, and area all increase correspondingly. This implies that the average yield rate might decrease from 60-70% to 40-50%. Therefore, the actual ABF substrate capacity required for future server CPU platforms will likely be more than double that of previous generations.

ABF Substrate Suppliers Riding the Tide

By our estimates, the global ABF substrate market size is set to grow from $9.3 billion in 2023 to $15 billion in 2026 – a CAGR of 17%, underscoring the tremendous growth and ongoing investment potential in the ABF supply chain.

Currently, Taiwanese and Japanese manufacturers cover about 80% of the global ABF substrate capacity. Major players like Japan’s Ibiden, Shinko and AT&S, along with Taiwan’s Unimicron, Nan Ya, and Kinsus all consider expanding their ABF substrate production capabilities as a long-term strategy.

As we analyzed in another piece, “Chiplet Design: A Real Game-Changer for Substrates,” despite the recent economic headwinds, capacity expansion of ABF substrate can still be seen as a solid trend, which is secured by the robust growth of high-end servers. Hence, the ability to precisely forecast capacity needs and simultaneously improve production yields will be the key to competitiveness for all substrate suppliers.

Read more:

• HBM Supply Leader SK Hynix’s Market Share to Exceed 50% in 2023 Due to Demand for AI Servers, Says TrendForce
• Shipments of AI Servers Will Climb at CAGR of 10.8% from 2022 to 2026, Says TrendForce

(Photo Credit: Google)

The excitement surrounding ChatGPT has sparked a new era in generative AI. This fresh technological whirlwind is revolutionizing everything, from cloud-based AI servers all the way down to edge-computing in smartphones.

Given that generative AI has enormous potential to foster new applications and boost user productivity, smartphones have unsurprisingly become a crucial vehicle for AI tech. Even though the computational power of an end device isn’t on par with the cloud, it has the double benefit of reducing the overall cost of computation and protecting user privacy. This is primarily why smartphone OEMs started using AI chips to explore and implement new features a few years ago.

However, Oppo’s recent decision to shut down its chip design company, Zheku, casted some doubts on the future of smartphone OEMs’ self-developed chips, bringing the smartphone AI chip market into focus.

Pressing Needs to Speed Up AI Chips Iterations

The industry’s current approach to running generative AI models on end devices involves two-pronged approaches: software efforts focus on reducing the size of the models to lessen the burden and energy consumption of chips, while the hardware side is all about increasing computational power and optimizing energy use through process shrinkage and architectural upgrades.

IC design houses, like Qualcomm with its Snapdragon8 Gen.2, are now hurrying to develop SoC products that are capable of running these generative AI base models.
Here’s the tricky part though: models are constantly evolving at a pace far exceeding the SoC development cycle – with updates like GPT occurring every six months. This gap between hardware iterations and new AI model advancements might only get wider, making the rapid expansion of computational requirements the major pain point that hardware solution providers need to address.

Top-tier OEMs pioneering Add-on AI Accelerators

It’s clear that in this race for AI computational power, the past reliance on SoCs is being challenged. Top-tier smartphone OEMs are no longer merely depending on standard products from SoC suppliers. Instead, they’re aggressively adopting AI accelerator chips to fill the computational gap.

The approaches of integrating and add-on AI accelerator were first seen in 2017:

• Integrated: This strategy is represented by Huawei’s Kirin970 and Apple’s A11 Bionic, which incorporated an AI engine within SoC.
• Add-on: Initially implemented by Google Pixel 2, which used a custom Pixel Visual Core chip alongside Snapdragon 835. It wasn’t until the 2021 Pixel 6 series, which introduced Google’s self-developed Tensor SoC, that the acceleration unit was directly integrated into the Tensor.

Clearly, OEMs with self-developing SoC+ capabilities usually embed their models into AI accelerators at the design stage. This hardware-software synergy supplies the required computing power for specific AI scenarios.

New Strategic Models on the Rise

For OEMs without self-development capabilities, the hefty cost of SoC development keeps them reliant on chip manufacturers’ SoC iterations. Yet, they’re also applying new strategies within the supply chain to keep pace with swift changes.

Here’s the interesting part – brands are leveraging simpler specialized chips to boost AI-enabled applications, making standalone ICs like ISPs(Image Signal Processors) pivotal for new features of photography and display. Meanwhile, we’re also seeing potential advancements in the field of productivity tools – from voice assistants to photo editing – where the implementation of small-scale ASICs is seriously being considered to fulfill computational demands.

From Xiaomi’s collaboration with Altek and Vivo’s joint effort with Novatek to develop ISPs, the future looks bright for ASIC development, opening up opportunities for small-scale IC design and IP service providers.

Responding to the trend, SoC leader MediaTek is embracing an open 5G architecture strategy for market expansion through licensing and custom services. However, there’s speculation about OEMs possibly replacing MediaTek’s standard IP with self-developed ones for deeper product differentiation.

Looking at this, it’s clear that the battle of AI chips continues with no winning strategy for speeding up smartphone AI chip product iteration.

Considering the substantial resources required for chip development and the saturation of the smartphone market, maintaining chip-related strategies adds a layer of uncertainty for OEMs.With Oppo’s move to discontinue its chip R&D, other brands like Vivo and Xiaomi are likely reconsidering their game plans. The future, therefore, warrants close watch.

Read more:

AI Sparks a Revolution Up In the Cloud

Under the grand banner of China’s domestic substitution policy, the wave of locally produced chips is swiftly spreading to the realm of Power Management ICs (PMICs).

Over the past three years, the number of fundraisings for Chinese PMIC manufacturers has shot up. We’ve seen an increase from 18 rounds in 2020 and 19 rounds in 2021 to a whopping 24 rounds in 2022 – a substantial leap from the figures in 2018 and 2019.

Looking at the number of IPO last year, 23 Chinese automotive-grade chip companies went public, with another 25 poised to follow suit. Among these 48 automotive chip firms, 12 boast PMICs, making it the largest product sector in these investments.

New Energy Vehicles Fuel China’s PMIC Market

Both the data points signal a golden era for Chinese PMIC industry, with the new energy vehicles(NEV)emerging as a key driving force.

Compared to traditional vehicles with internal combustion engines, NEV requires a greater number of PMICs, like DC/DC converters, to manage voltage conversions. This, in turn, propels overall PMIC growth. From 2021, automotive PMICs have entered a phase of rapid growth. TrendForce forecasts that the scale of automotive PMICs will reach $7.65 billion by 2023, marking a year-on-year growth of 4.2%.

Government’s subsidy incentives and a booming domestic demand for NEV are the primary reasons for nudging the Chinese semiconductor industry to embrace PMICs more quickly. This trend aligns perfectly with the growth trajectory of China’s power semiconductors.

Chinese Manufacturers Plant Flags in Automotive PMICs

Over the past year, several domestic PMIC manufacturers, including SG Micro, Etek, Shanghai Belling, and Halo Micro, have rolled out automotive-grade PMICs. Some of these chips have even entered mass production and are being adopted by domestic vehicle bands.

Foundries are equally keen to seize the golden opportunity. For instance, GTA Semiconductor has successfully raised over 10 billion yuan in recent years. The company has earmarked a portion of the funds specifically for the R&D of automotive-grade PMIC.

However, the opportunities come with their fair share of challenges. New entrants must navigate stringent automotive certifications, ensure product resilience across extreme temperature ranges from -40°C to 125°C, guarantee a product lifespan exceeding ten years, and manage prolonged validation cycles. These demanding requirements significantly raise the entry barriers for newcomers.

On a global scale, international IDM giants like Infineon, NXP, TI, and Renesas are well entrenched in the PMIC sector, boasting a diverse range of products. In contrast, Chinese PMICs supply chain are just off the starting blocks of the race. To gain trust from customers, expand their product portfolio, and penetrate the global market, they are bound to confront a succession of hurdles, which will persistently scrutinise the enduring R&D capabilities and business strategies of each manufacturer.

According to a recent report by TrendForce, automotive applications are expected to become the main growth driver of the CIS market, with its share of terminal applications projected to increase from around 9% in 2023 to approximately 15% in 2026.

As self-driving systems become more widespread, the demand for CIS in the automotive industry will continue to increase. Trendforce estimates that the average number of CIS used per car will increase from 3-4 in 2022 to 6-7 in 2026, with the overall market size growing from $1.8 billion in 2023 to over $3 billion in 2026 at a CAGR of over 20%.

On the other hand, due to the stagnation in the number of camera modules, the growth of the smartphone CIS market is expected to be in the low single digits. By 2026, CIS terminal applications in smartphones are predicted to decrease from 63% in 2023 to 51%, while automotive applications are projected to increase from 9% to 15%.

The report offers the following insights into the CIS market for smartphones:

• 10.5% decline in smartphone production in 2022: Due to factors such as war, pandemics, inflation, and inventory adjustments, global smartphone production in 2022 declined by 10.5% to 1.193 billion units, resulting in a 6.7% decline in the shipment of camera modules to 4.46 billion units and a 4% decline in the overall CIS market size to $18.9 billion.
• 5% growth in the CIS market size in 2023: The report predicts that the global smartphone production will increase by 0.9% to 1.203 billion units in 2023, with the shipment of camera modules reaching 4.62 billion units, up 3.6% YoY, driving the overall CIS market size to grow by 5% to $19.8 billion.
• Stagnation in the number of smartphone camera modules: From 2021 onwards, the proportion of shipments of smartphones with four and more rear camera modules has remained at 21%, slightly lower than the 23% in 2020, and is expected to remain at the same level in 2023.
• The trend towards large size and high-resolution CIS: In 2021, Samsung launched the world’s first 200-megapixel CIS, the ISOCELL HP1, while Sony introduced the IMX989 in 2022, marking the official entry of high-end smartphones into the one-inch era.

Additionally, driven by new features such as night photography, it is anticipated that the image quality of smartphone cameras may surpass that of single-lens reflex cameras (SLR) by 2024, resulting in the increase of market size and ASP of CIS.