According to the latest weekly memory spot prices published by TrendForce, the spot prices of DDR5 chips have returned to a downward trend, resulting in no signs of recovery in overall spot prices. For more details, please refer to the information below:

DRAM Spot Market:
Spot prices of DDR5 chips have swung down again, and spot prices of DDR4 and DDR3 products continue to register incremental declines on a daily basis. The spot market on the whole has yet to show signs of a price rebound. Lately, some buyers have been seeking quotes for small-quantity orders, but this kind of demand does not generate a sufficient momentum to expand the overall transaction volume. Presently, most traders generally believe that spot prices are almost at the bottom, but they remain passive in stocking up because the demand outlook is quite negative. The average spot price of the mainstream chips (i.e., DDR4 1Gx8 2666MT/s) fell by 0.97% from US$1.543 last week to US$1.528 this week.

NAND Flash Spot Market:
It is now a consensus within the market that prices are no longer able to drop further amidst the manifestation of efficacy from the diminished provision among suppliers. Spot prices are now starting to stabilize on the whole despite transactions having yet to magnify accordingly. 512Gb TLC wafer has dropped by 0.49% this week, arriving at US$1.420.

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)

TrendForce’s latest research indicates that, as production cuts to DRAM and NAND Flash have not kept pace with weakening demand, the ASP of some products is expected to decline further in 2Q23. DRAM prices are projected to fall 13~18%; NAND Flash is expected to fall between 8~13%.

DRAM Spot Market

In the spot market, the trajectory of prices of DDR5 chips is going to be a highlight in the short term. Some sellers are now willing to lower quotes on DDR5 products that have been enjoying price hikes for many consecutive days. However, there have been few to no actual transactions, so TrendForce will continue to closely monitor changes in the prices of these products. As for spot prices of DDR3 and DDR4 products, they are still on a downward trajectory with no sign of easing. The average spot price of mainstream chips (i.e., DDR4 1Gx8 2666MT/s) fell by 1.33% from US$1.575 last week to US$1.554 this week.

NAND Flash Spot Market
Suppliers continue to enlarge in production cuts. Despite the insignificant increase in spot market demand, the continuously shrinking output of small-capacity wafers, as well as the attempt at price revitalization among a number of suppliers, have pulled up demand and transactions of low-priced inventory within the spot market, which led to a small price increment. However, mainstream wafer prices have yet to suspend in decline. 512Gb TLC wafer has dropped by 0.21% this week, arriving at US$1.427.

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

In the bustling tech bazaar, the iPhone 14 Pro and AirPods 3 are pioneering the tech industry by incorporating InP(Indium phosphide)-based EEL(Edge Emitting Laser). These devices are leveraging the unique attributes of long-wavelength technology for skin detection, which is a strategic move that highlights the gradual emergence of InP material in the consumer market.

Historically, data communication and telecom industries have acted as the primary fuel for the InP market, their demand for backbone network photoelectric and 400G/800G optical modules in data centers has been consistent. However, as the quality and refinement of 6-inch InP single-crystal growth technology advance, we see a reduction in production costs, thus unlocking a gateway to consumer applications.

Emerging Dual Frontiers: Consumer and Photonic Applications

Apple and other savvy smartphone OEMs are contemplating the introduction of long-wavelength InP-based EEL in their next-gen products. This would be used for physiological sensing in proximity sensors or possibly to replace the currently used 940nm GaAs-based VCSEL(Vertical Surface Emitting Laser) in 3D sensing.

Simultaneously, the evolution of autonomous driving is nudging automotive laser radars towards the 1550nm wavelength, a departure from the former 905nm. This shift promises increased detection range and improved protection for human eyes.

In the realm of photonics communication technology, a more significant growth driver stems from the trend of high-end EML(Electro-absorption Modulated Laser) replacing traditional DFBs(Distributed-feedback laser).

As next-gen data center applications are steered towards 400G/800G transmission speed solutions, EML laser chips promising high bandwidth performance and high yield will take the spotlight. They are anticipated to realize the high-speed transmission characteristics of single-wavelength 100G.

It is also worth noting that as fiber-optic access in the PON (Passive Optical Network) market gradually upgrades to the 25G/50G-PON solution, there is an evident trend towards integrated solutions combining laser chips and SOAs (Semiconductor Optical Amplifiers). This shift is driven by the increasing demands for higher transmission rates and output power, leading to the replacement of discrete DFB solutions.

Supply Chain Over-centralization: A Precursor to a Sellers’ Market?

This cornucopia of application scenarios signals tremendous market potential for InP-based components. However, one must question whether the supply chain is prepared for this windfall.

One of the concern is that the industry chain’s over-centralization might usher in a seller’s market situation.

InP substrate materials and epitaxial silicon wafers pose a high technological threshold and are primarily monopolized by few manufacturers, particularly those from Europe, the U.S. and Japan.

• The InP substrate material market is highly monopolized by Sumitomo Electric Industries, AXT, and JX NMM, which collectively account for 90% of market share in 2020.
• The epitaxy process is the crux of photonic chip production, with tech prowess directly impacting product performance and reliability. Key suppliers capable of providing InP epitaxy silicon wafers include IQE, Lumentum, and Sumitomo, among others.
• In terms of photonic chip technology, its value lies more in added functionality, necessitating process integration. This gives rise to IDM giants dominating the market. For instance, Lumentum, Sumitomo, and Mitsubishi dominate the 25G DFB laser chip market.

While the influx of newcomers from China is seen in the lower-tech optical module packaging sector, the core technologies upstream are still held firmly by international industry leaders, posing a challenging breakthrough for newcomers in the short term.

The growing interest in the market for this technology indicates that end-product manufacturers developing new applications based on InP will inevitably need to double down their efforts to ensure the stability of long-term supply. It remains to be seen whether the singularity of the supply chain will further restrict the proliferation of emerging applications in the end market.