A new technical paper titled “Mechanically-flexible wafer-scale integrated-photonics fabrication platform” was published by researchers at MIT and New York Center for Research, Economic Advancement, Technology, Engineering, and Science (NY CREATES).

Abstract
“The field of integrated photonics has advanced rapidly due to wafer-scale fabrication, with integrated-photonics platforms and fabrication processes being demonstrated at both infrared and visible wavelengths. However, these demonstrations have primarily focused on fabrication processes on silicon substrates that result in rigid photonic wafers and chips, which limit the potential application spaces. There are many application areas that would benefit from mechanically-flexible integrated-photonics wafers, such as wearable healthcare monitors and pliable displays. Although there have been demonstrations of mechanically-flexible photonics fabrication, they have been limited to fabrication processes on the individual device or chip scale, which limits scalability. In this paper, we propose, develop, and experimentally characterize the first 300-mm wafer-scale platform and fabrication process that results in mechanically-flexible photonic wafers and chips. First, we develop and describe the 300-mm wafer-scale CMOS-compatible flexible platform and fabrication process. Next, we experimentally demonstrate key optical functionality at visible wavelengths, including chip coupling, waveguide routing, and passive devices. Then, we perform a bend-durability study to characterize the mechanical flexibility of the photonic chips, demonstrating bending a single chip 2000 times down to a bend diameter of 0.5 inch with no degradation in the optical performance. Finally, we experimentally characterize polarization-rotation effects induced by bending the flexible photonic chips. This work will enable the field of integrated photonics to advance into new application areas that require flexible photonic chips.”

Find the technical paper here. Published May 2024. Find MIT’s news release here.

Notaros, M., Dyer, T., Garcia Coleto, A. et al. Mechanically-flexible wafer-scale integrated-photonics fabrication platform. Sci Rep 14, 10623 (2024). https://doi.org/10.1038/s41598-024-61055-w.

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Okinawa Institute of Science and Technology proposed a new EUV litho technology using only four reflective mirrors and a new method of illumination optics that it claims will use 1/10 the power and cost half as much as existing EUV technology from ASML.

Applied Materials may not receive expected U.S. funding to build a $4 billion research facility in Sunnyvale, CA, due to internal government disagreements over how to fund chip R&D, according to Bloomberg.

SEMI published a position paper this week cautioning the European Union against imposing additional export controls to allow companies, encouraging them to  be “as free as possible in their investment decisions to avoid losing their agility and relevance across global markets.” SEMI’s recommendations on outbound investments are in response to the European Economic Security Strategy and emphasize the need for a transparent and predictable regulatory framework.

The U.S. may restrict China’s access to HBM chips and the equipment needed to make them, reports Bloomberg. Today those chips are manufactured by two Korean-based companies, Samsung and SK hynix, but U.S.-based Micron expects to begin shipping 12-high stacks of HBM3E in 2025, and is currently working on HBM4.

Synopsys executive chair and founder Dr. Aart de Geus was named the winner of the Semiconductor Industry Association’s Robert N. Noyce Award. De Geus was selected due to his contributions to EDA technology over a career spanning more than four decades.

The top three foundries plan to implement high-NA EUV lithography as early as 2025 for the 18 angstrom generation, but the replacement of single exposure high-NA (0.55) over double patterning with standard EUV (NA = 0.33) depends on whether it provides better results at a reasonable cost per wafer.

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Global
In-Depth
Market Reports and Earnings
Education and Training
Security
Product News
Research
Events and Further Reading

Global

Belgium-based Imec released part 2 of its chiplets series, addressing testing strategies and standardization efforts, as well as guidelines and research “towards efficient ESD protection strategies for advanced 3D systems-on-chip.”

Also in Belgium, BelGan, maker of GaN chips, filed for bankruptcy according to the Brussels Times.

TSMC‘s Dresden, Germany, plant will break ground this month.

The UK will dole out more than £100 million (~US $128 million) in funding to develop five new quantum research hubs in Glasgow, Edinburgh, Birmingham, Oxford, and London.

MassPhoton is opening Hong Kong‘s first ultra-high vacuum GaN epitaxial wafer pilot line and will establish a GaN research center.

Infineon completed the sale of its manufacturing sites in the Philippines and South Korea to ASE.

Israel-based RAAAM Memory Technologies received a €5.25 million grant from the European Innovation Council (EIC) to support the development and commercialization of its innovative memory solutions. This funding will enable RAAAM to advance its research in high-performance and energy-efficient memory technologies, accelerating their integration into various applications and markets.

In-Depth

Semiconductor Engineering published its Automotive, Security and Pervasive Computing newsletter this week, featuring these top stories and video:

• Chip Security Now Depends On Widening Supply Chain
• Defining Chip Threat Models To Determining Security Risks
• Post-Quantum Computing Threatens Fundamental Transport Protocols

And:

• Where Power Savings Really Count
• Making Electronics More Efficient (video)

Market Reports and Earnings

The semiconductor equipment industry is on a positive trajectory in 2024, with moderate revenue growth observed in Q2 after a subdued Q1, according to a new report from Yole Group. Wafer Fab Equipment revenue is projected to grow by 1.3% year-on-year, despite a 12% drop in Q1. Test equipment lead times are normalizing, improving order conditions. Key areas driving growth include memory and logic capital expenditures and high-bandwidth memory demand.

Worldwide silicon wafer shipments increased by 7% in Q2 2024, according to SEMI‘s latest report. This growth is attributed to robust demand from multiple semiconductor sectors, driven by advancements in AI, 5G, and automotive technologies.

The RF GaN market is projected to grow to US $2 billion by 2029, a 10% CAGR, according to Yole Group.

Counterpoint released their Q2 smartphone top 10 report.

Renesas completed their acquisition of EDA firm Altium, best known for its EDA platform and freeware CircuitMaker package.

It’s earnings season and here are recently released financials in the chip industry:

AMD  Advantest   Amkor   Ansys  Arteris   Arm   ASE   ASM   ASML
Cadence  IBM   Intel   Lam Research   Lattice   Nordson   NXP   Onsemi 
Qualcomm   Rambus  Samsung    SK Hynix   STMicro   Teradyne    TI  
Tower  TSMC    UMC  Western Digital

Industry stock price impacts are here.

Education and Training

Rochester Institute of Technology is leading a new pilot program to prepare community college students in areas such as cleanroom operations, new materials, simulation, and testing processes, with the intent of eventual transfer into RIT’s microelectronic engineering program.

Purdue University inked a deal with three research institutions — University of Piraeus, Technical University of Crete, and King’s College London —to develop joint research programs for semiconductors, AI and other critical technology fields.

The European Chips Skills Academy formed the Educational Leaders Board to help bridge the talent gap in Europe’s microelectronics sector.  The Board includes representatives from universities, vocational training providers, educators and research institutions who collaborate on strategic initiatives to strengthen university networks and build academic expertise through ECSA training programs.

Security

The Cybersecurity and Infrastructure Security Agency (CISA) is encouraging Apple users to review and apply this week’s recent security updates.

Microsoft Azure experienced a nearly 10 hour DDoS attack this week, leading to global service disruption for many customers.  “While the initial trigger event was a Distributed Denial-of-Service (DDoS) attack, which activated our DDoS protection mechanisms, initial investigations suggest that an error in the implementation of our defenses amplified the impact of the attack rather than mitigating it,” stated Microsoft in a release.

NIST published:

• “Recommendations For Increasing U.S. Participation and Leadership in Standards Development,” a report outlining cybersecurity recommendations and mitigation strategies.
• Final guidance documents and software to help improve the “safety, security and trustworthiness of AI systems.”
• Cloud Computing Forensic Reference Architecture guide.

Delta Air Lines plans to seek damages after losing $500 million in lost revenue due to security company CrowdStrike‘s software update debacle.  And shareholders are also angry.

Recent security research:

• Physically Secure Logic Locking With Nanomagnet Logic (UT Dallas)
• WBP: Training-time Backdoor Attacks through HW-based Weight Bit Poisoning (UCF)
• S-Tune: SOT-MTJ Manufacturing Parameters Tuning for Secure Next Generation of Computing ( U. of Arizona, UCF)
• Diffie Hellman Picture Show: Key Exchange Stories from Commercial VoWiFi Deployments (CISPA, SBA Research, U. of Vienna)

Product News

Lam Research introduced a new version of its cryogenic etch technology designed to enhance the manufacturing of 3D NAND for AI applications. This technology allows for the precise etching of high aspect ratio features, crucial for creating 1,000-layer 3D NAND.

Fig.1: 3D NAND etch. Source: Lam Research

Alphawave Semi launched its Universal Chiplet Interconnect Express Die-toDie IP. The subsystem offers 8 Tbps/mm bandwidth density and supports operation at 24 Gbps for D2D connectivity.

Infineon introduced a new MCU series for industrial and consumer motor controls, as well as power conversion system applications. The company also unveiled its new GoolGaN Drive product family of integrated single switches and half-bridges with integrated drivers.

Rambus released its DDR5 Client Clock Driver for next-gen, high-performance desktops and notebooks. The chips include Gen1 to Gen4 RCDs, power management ICs, Serial Presence Detect Hubs, and temperature sensors for leading-edge servers.

SK hynix introduced its new GDDR7 graphics DRAM. The product has an operating speed of 32Gbps, can process 1.5TB of data per second and has a 50% power efficiency improvement compared to the previous generation.

Intel launched its new Lunar Lake Ultra processors. The long awaited chips will be included in more than 80 laptop designs and has more than 40 NPU tera operations per second as well as over 60 GPU TOPS delivering more than 100 platform TOPS.

Brewer Science achieved recertification as a Certified B Corporation, reaffirming its commitment to sustainable and ethical business practices.

Panasonic adopted Siemens’ Teamcenter X cloud product lifecycle management solution, citing Teamcenter X’s Mendix low-code platform, improved operational efficiency and flexibility for its choice.

Keysight validated its 5G NR FR1 1024-QAM demodulation test cases for the first time. The 5G NR radio access technology supports eMBB and was validated on the 3GPP TS 38.521-4 test specification.

Research

In a 47-page deep-dive report, the Center for Security and Emerging Technology delved into all of the scientific breakthroughs from 1980 to present that brought EUV lithography to commercialization, including lessons learned for the next emerging technologies.

Researchers at the Paul Scherrer Institute developed a high-performance X-ray tomography technique using burst ptychography, achieving a resolution of 4nm. This method allows for non-destructive imaging of integrated circuits, providing detailed views of nanostructures in materials like silicon and metals.

MIT signed a four-year agreement with the Novo Nordisk Foundation Quantum Computing Programme at University of Copenhagen, focused on accelerating quantum computing hardware research.

MIT’s Research Laboratory of Electronics (RLE) developed a mechanically flexible wafer-scale integrated photonics fabrication platform. This enables the creation of flexible photonic circuits that maintain high performance while being bendable and stretchable. It offers significant potential for integrating photonic circuits into various flexible substrate applications in wearable technology, medical devices, and flexible electronics.

The Naval Research Lab identified a new class of semiconductor nanocrystals with bright ground-state excitons, emphasizing an important advancement in optoelectronics.

Researchers from National University of Singapore developed a novel method, known as tension-driven CHARM3D,  to fabricate 3D self-healing circuits, enabling the 3D printing of free-standing metallic structures without the need for support materials and external pressure.

Find more research in our Technical Papers library.

Events and Further Reading

Find upcoming chip industry events here, including:

Upcoming webinars are here, including topics such as quantum safe cryptography, analytics for high-volume manufacturing, and mastering EMC simulations for electronic design.

Find Semiconductor Engineering’s latest newsletters here:

Automotive, Security and Pervasive Computing
Systems and Design
Low Power-High Performance
Test, Measurement and Analytics
Manufacturing, Packaging and Materials

The post Chip Industry Week in Review appeared first on Semiconductor Engineering.

A new technical paper titled “Harnessing Heterogeneity for Targeted Attacks on 3-D ICs” was published by Drexel University.

Abstract
“As 3-D integrated circuits (ICs) increasingly pervade the microelectronics industry, the integration of heterogeneous components presents a unique challenge from a security perspective. To this end, an attack on a victim die of a multi-tiered heterogeneous 3-D IC is proposed and evaluated. By utilizing on-chip inductive circuits and transistors with low voltage threshold (LVT), a die based on CMOS technology is proposed that includes a sensor to monitor the electromagnetic (EM) emissions from the normal function of a victim die, without requiring physical probing. The adversarial circuit is self-powered through the use of thermocouples that supply the generated current to circuits that sense EM emissions. Therefore, the integration of disparate technologies in a single 3-D circuit allows for a stealthy, wireless, and non-invasive side-channel attack. A thin-film thermo-electric generator (TEG) is developed that produces a 115 mV voltage source, which is amplified 5 × through a voltage booster to provide power to the adversarial circuit. An on-chip inductor is also developed as a component of a sensing array, which detects changes to the magnetic field induced by the computational activity of the victim die. In addition, the challenges associated with detecting and mitigating such attacks are discussed, highlighting the limitations of existing security mechanisms in addressing the multifaceted nature of vulnerabilities due to the heterogeneity of 3-D ICs.”

Find the technical paper here. Published June 2024.

Alec Aversa and Ioannis Savidis. 2024. Harnessing Heterogeneity for Targeted Attacks on 3-D ICs. In Proceedings of the Great Lakes Symposium on VLSI 2024 (GLSVLSI ’24). Association for Computing Machinery, New York, NY, USA, 246–251. https://doi.org/10.1145/3649476.3660385.

The post Heterogeneity Of 3DICs As A Security Vulnerability appeared first on Semiconductor Engineering.

A new technical paper titled “A Hybrid ECO Detailed Placement Flow for Improved Reduction of Dynamic IR Drop” was published by researchers at UC San Diego.

Abstract:

“With advanced semiconductor technology progressing well into sub-7nm scale, voltage drop has become an increasingly challenging issue. As a result, there has been extensive research focused on predicting and mitigating dynamic IR drops, leading to the development of IR drop engineering change order (ECO) flows – often integrated with modern commercial EDA tools. However, these tools encounter QoR limitations while mitigating IR drop. To address this, we propose a hybrid ECO detailed placement approach that is integrated with existing commercial EDA flows, to mitigate excessive peak current demands within power and ground rails. Our proposed hybrid approach effectively optimizes peak current levels within a specified “clip”– complementing and enhancing commercial EDA dynamic IR-driven ECO detailed placements. In particular, we: (i) order instances in a netlist in decreasing order of worst voltage drop; (ii) extract a clip around each instance; and (iii) solve an integer linear programming (ILP) problem to optimize instance placements. Our approach optimizes dynamic voltage drops (DVD) across ten designs by up to 15.3% compared to original conventional flows, with similar timing quality and 55.1% less runtime.”

Find the technical paper here. Published June 2024.

Andrew B. Kahng, Bodhisatta Pramanik, and Mingyu Woo. 2024. A Hybrid ECO Detailed Placement Flow for Improved Reduction of Dynamic IR Drop. In Proceedings of the Great Lakes Symposium on VLSI 2024 (GLSVLSI ’24). Association for Computing Machinery, New York, NY, USA, 390–396. https://doi.org/10.1145/3649476.3658727.

The post A Hybrid ECO Detailed Placement Flow for Mitigating Dynamic IR Drop (UC San Diego) appeared first on Semiconductor Engineering.

A new technical paper titled “An Evaluation of Continual Learning for Advanced Node Semiconductor Defect Inspection” was published by Imec and SCREEN SPE Germany.

Abstract

“Deep learning-based semiconductor defect inspection has gained traction in recent years, offering a powerful and versatile approach that provides high accuracy, adaptability, and efficiency in detecting and classifying nano-scale defects. However, semiconductor manufacturing processes are continually evolving, leading to the emergence of new types of defects over time. This presents a significant challenge for conventional supervised defect detectors, as they may suffer from catastrophic forgetting when trained on new defect datasets, potentially compromising performance on previously learned tasks. An alternative approach involves the constant storage of previously trained datasets alongside pre-trained model versions, which can be utilized for (re-)training from scratch or fine-tuning whenever encountering a new defect dataset. However, adhering to such a storage template is impractical in terms of size, particularly when considering High-Volume Manufacturing (HVM). Additionally, semiconductor defect datasets, especially those encompassing stochastic defects, are often limited and expensive to obtain, thus lacking sufficient representation of the entire universal set of defectivity. This work introduces a task-agnostic, meta-learning approach aimed at addressing this challenge, which enables the incremental addition of new defect classes and scales to create a more robust and generalized model for semiconductor defect inspection. We have benchmarked our approach using real resist-wafer SEM (Scanning Electron Microscopy) datasets for two process steps, ADI and AEI, demonstrating its superior performance compared to conventional supervised training methods.”

Find the technical paper here.  Published July 2024 (preprint).

Prasad, Amit, Bappaditya Dey, Victor Blanco, and Sandip Halder. “An Evaluation of Continual Learning for Advanced Node Semiconductor Defect Inspection.” arXiv preprint arXiv:2407.12724 (2024).

The post Classification and Localization of Semiconductor Defect Classes in Aggressive Pitches (imec, Screen) appeared first on Semiconductor Engineering.

A new technical paper titled “Survey of Deep Learning Accelerators for Edge and Emerging Computing” was published by researchers at University of Dayton and the Air Force Research Laboratory.

Abstract

“The unprecedented progress in artificial intelligence (AI), particularly in deep learning algorithms with ubiquitous internet connected smart devices, has created a high demand for AI computing on the edge devices. This review studied commercially available edge processors, and the processors that are still in industrial research stages. We categorized state-of-the-art edge processors based on the underlying architecture, such as dataflow, neuromorphic, and processing in-memory (PIM) architecture. The processors are analyzed based on their performance, chip area, energy efficiency, and application domains. The supported programming frameworks, model compression, data precision, and the CMOS fabrication process technology are discussed. Currently, most commercial edge processors utilize dataflow architectures. However, emerging non-von Neumann computing architectures have attracted the attention of the industry in recent years. Neuromorphic processors are highly efficient for performing computation with fewer synaptic operations, and several neuromorphic processors offer online training for secured and personalized AI applications. This review found that the PIM processors show significant energy efficiency and consume less power compared to dataflow and neuromorphic processors. A future direction of the industry could be to implement state-of-the-art deep learning algorithms in emerging non-von Neumann computing paradigms for low-power computing on edge devices.”

Find the technical paper here. Published July 2024.

Alam, Shahanur, Chris Yakopcic, Qing Wu, Mark Barnell, Simon Khan, and Tarek M. Taha. 2024. “Survey of Deep Learning Accelerators for Edge and Emerging Computing” Electronics 13, no. 15: 2988. https://doi.org/10.3390/electronics13152988.

The post Survey of Energy Efficient PIM Processors appeared first on Semiconductor Engineering.

A new technical paper titled “MINT: Securely Mitigating Rowhammer with a Minimalist In-DRAM Tracker” was published by researchers at Georgia Tech, Google, and Nvidia.

Abstract
“This paper investigates secure low-cost in-DRAM trackers for mitigating Rowhammer (RH). In-DRAM solutions have the advantage that they can solve the RH problem within the DRAM chip, without relying on other parts of the system. However, in-DRAM mitigation suffers from two key challenges: First, the mitigations are synchronized with refresh, which means we cannot mitigate at arbitrary times. Second, the SRAM area available for aggressor tracking is severely limited, to only a few bytes. Existing low-cost in-DRAM trackers (such as TRR) have been broken by well-crafted access patterns, whereas prior counter-based schemes require impractical overheads of hundreds or thousands of entries per bank. The goal of our paper is to develop an ultra low-cost secure in-DRAM tracker.

Our solution is based on a simple observation: if only one row can be mitigated at refresh, then we should ideally need to track only one row. We propose a Minimalist In-DRAM Tracker (MINT), which provides secure mitigation with just a single entry. At each refresh, MINT probabilistically decides which activation in the upcoming interval will be selected for mitigation at the next refresh. MINT provides guaranteed protection against classic single and double-sided attacks. We also derive the minimum RH threshold (MinTRH) tolerated by MINT across all patterns. MINT has a MinTRH of 1482 which can be lowered to 356 with RFM. The MinTRH of MINT is lower than a prior counter-based design with 677 entries per bank, and is within 2x of the MinTRH of an idealized design that stores one-counter-per-row. We also analyze the impact of refresh postponement on the MinTRH of low-cost in-DRAM trackers, and propose an efficient solution to make such trackers compatible with refresh postponement.”

Find the technical paper here. Preprint published July 2024.

Qureshi, Moinuddin, Salman Qazi, and Aamer Jaleel. “MINT: Securely Mitigating Rowhammer with a Minimalist In-DRAM Tracker.” arXiv preprint arXiv:2407.16038 (2024).

The post Secure Low-Cost In-DRAM Trackers For Mitigating Rowhammer (Georgia Tech, Google, Nvidia) appeared first on Semiconductor Engineering.

A new technical paper titled “Experimental demonstration of magnetic tunnel junction-based computational random-access memory” was published by researchers at University of Minnesota and University of Arizona, Tucson.

Abstract

“The conventional computing paradigm struggles to fulfill the rapidly growing demands from emerging applications, especially those for machine intelligence because much of the power and energy is consumed by constant data transfers between logic and memory modules. A new paradigm, called “computational random-access memory (CRAM),” has emerged to address this fundamental limitation. CRAM performs logic operations directly using the memory cells themselves, without having the data ever leave the memory. The energy and performance benefits of CRAM for both conventional and emerging applications have been well established by prior numerical studies. However, there is a lack of experimental demonstration and study of CRAM to evaluate its computational accuracy, which is a realistic and application-critical metric for its technological feasibility and competitiveness. In this work, a CRAM array based on magnetic tunnel junctions (MTJs) is experimentally demonstrated. First, basic memory operations, as well as 2-, 3-, and 5-input logic operations, are studied. Then, a 1-bit full adder with two different designs is demonstrated. Based on the experimental results, a suite of models has been developed to characterize the accuracy of CRAM computation. Scalar addition, multiplication, and matrix multiplication, which are essential building blocks for many conventional and machine intelligence applications, are evaluated and show promising accuracy performance. With the confirmation of MTJ-based CRAM’s accuracy, there is a strong case that this technology will have a significant impact on power- and energy-demanding applications of machine intelligence.”

Find the technical paper here. Published July 2024.  Find the University of Minnesota’s news release here.

Lv, Y., Zink, B.R., Bloom, R.P. et al. Experimental demonstration of magnetic tunnel junction-based computational random-access memory. npj Unconv. Comput. 1, 3 (2024). https://doi.org/10.1038/s44335-024-00003-3.

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In today’s technology-driven landscape in which reducing TCO is top of mind, robust data protection is not merely an option but a necessity. As data, both personal and business-specific, is continuously exchanged, stored, and moved across various platforms and devices, the demand for a secure means of data aggregation and trust enhancement is escalating. Traditional data protection strategies of protecting data at rest and data in motion need to be complemented with protection of data in use. This is where the role of inline memory encryption (IME) becomes critical. It acts as a shield for data in use, thus underpinning confidential computing and ensuring data remains encrypted even when in use. This blog post will guide you through a practical approach to inline memory encryption and confidential computing for enhanced data security.

Understanding inline memory encryption and confidential computing

Inline memory encryption offers a smart answer to data security concerns, encrypting data for storage and decoding only during computation. This is the core concept of confidential computing. As modern applications on personal devices increasingly leverage cloud systems and services, data privacy and security become critical. Confidential computing and zero trust are suggested as solutions, providing data in use protection through hardware-based trusted execution environments. This strategy reduces trust reliance within any compute environment and shrinks hackers’ attack surface.

The security model of confidential computing demands data in use protection, in addition to traditional data at rest and data in motion protection. This usually pertains to data stored in off-chip memory such as DDR memory, regardless of it being volatile or non-volatile memory. Memory encryption suggests data should be encrypted before storage into memory, in either inline or look aside form. The performance demands of modern-day memories for encryption to align with the memory path have given rise to the term inline memory encryption.

There are multiple off-chip memory technologies, and modern NVMS and DDR memory performance requirements make inline encryption the most sensible solution. The XTS algorithm using AES or SM4 ciphers and the GCM algorithm are the commonly used cryptographic algorithms for memory encryption. While the XTS algorithm encrypts data solely for confidentiality, the GCM algorithm provides data encryption and data authentication, but requires extra memory space for metadata storage.

Inline memory encryption is utilized in many systems. When considering inline cipher performance for DDR, the memory performance required for different technologies is considered. For instance, LPDDR5 typically necessitates a data path bandwidth of 25 gigabytes per second. An AES operation involves 10 to 14 rounds of encryption rounds, implying that these 14 rounds must operate at the memory’s required bandwidth. This is achievable with correct pipelining in the crypto engine. Other considerations include minimizing read path latency, support for narrow bursts, memory specific features such as the number of outstanding transactions, data-hazard protection between READ and WRITE paths, and so on. Furthermore, side channel attack protections and data path integrity, a critical factor for robustness in advanced technology nodes, are additional concerns to be taken care without prohibitive PPA overhead.

Ensuring data security in AI and computational storage

The importance of data security is not limited to traditional computing areas but also extends to AI inference and training, which heavily rely on user data. Given the privacy issues and regulatory demands tied to user data, it’s essential to guarantee the encryption of this data, preventing unauthorized access. This necessitates the application of a trusted execution environment and data encryption whenever it’s transported outside this environment. With the advent of new algorithms that call for data sharing and model refinement among multiple parties, it’s crucial to maintain data privacy by implementing appropriate encryption algorithms.

Equally important is the rapidly evolving field of computational storage. The advent of new applications and increasing demands are pushing the boundaries of conventional storage architecture. Solutions such as flexible and composable storage, software-defined storage, and memory duplication and compression algorithms are being introduced to tackle these challenges. Yet, these solutions introduce security vulnerabilities as storage devices operate on raw disk data. To counter this, storage accelerators must be equipped with encryption and decryption capabilities and should manage operations at the storage nodes.

As our computing landscape continues to evolve, we need to address the escalating demand for robust data protection. Inline memory encryption emerges as a key solution, offering data in use protection for confidential computing, securing both personal and business data.

Rambus Inline Memory Encryption IP provide scalable, versatile, and high-performance inline memory encryption solutions that cater to a wide range of application requirements. The ICE-IP-338 is a FIPS certified inline cipher engine supporting AES and SM4, as well as XTS GCM modes of operation. Building on the ICE-IP-338 IP, the ICE-IP-339 provides essential AXI4 operation support, simplifying system integration for XTS operation, delivering confidentiality protection. The IME-IP-340 IP extends basic AXI4 support to narrow data access granularity, as well as AES GCM operations, delivering confidentiality and authentication. Finally, the most recent offering, the Rambus IME-IP-341 guarantees memory encryption with AES-XTS, while supporting the Arm v9 architecture specifications.

For more information, check out my recent IME webinar now available to watch on-demand.

The post A Practical Approach To Inline Memory Encryption And Confidential Computing For Enhanced Data Security appeared first on Semiconductor Engineering.

In the automotive industry, AI-enabled automotive devices and systems are dramatically transforming the way SoCs are designed, making high-quality and reliable die-to-die and chip-to-chip connectivity non-negotiable. This article explains how interface IP for die-to-die connectivity, display, and storage can support new developments in automotive SoCs for the most advanced innovations such as centralized zonal architecture and integrated ADAS and IVI applications.

AI-integrated ADAS SoCs

The automotive industry is adopting a new electronic/electric (EE) architecture where a centralized compute module executes multiple applications such as ADAS and in-vehicle infotainment (IVI). With the advent of EVs and more advanced features in the car, the new centralized zonal architecture will help minimize complexity, maximize scalability, and facilitate faster decision-making time. This new architecture is demanding a new set of SoCs on advanced process technologies with very high performance. More traditional monolithic SoCs for single functions like ADAS are giving way to multi-die designs where various dies are connected in a single package and placed in a system to perform a function in the car. While such multi-die designs are gaining adoption, semiconductor companies must remain cost-conscious as these ADAS SoCs will be manufactured at high volumes for a myriad of safety levels. One example is the automated driving central compute system. The system can include modules for the sensor interface, safety management, memory control and interfaces, and dies for CPU, GPU, and AI accelerator, which are then connected via a die-to-die interface such as the Universal Chiplet Interconnect Express (UCIe). Figure 1 illustrates how semiconductor companies can develop SoCs for such systems using multi-die designs. For a base ADAS or IVI SoC, the requirement might just be the CPU die for a level 2 functional safety. A GPU die can be added to the base CPU die for a base ADAS or premium IVI function at a level 2+ driving automation. To allow more compute power for AI workloads, an NPU die can be added to the base CPU or the base CPU and GPU dies for level 3/3+ functional safety. None of these scalable scenarios are possible without a solution for die-to-die connectivity.

Fig. 1: A simplified view of automotive systems using multi-die designs.

The adoption of UCIe for automotive SoCs

The industry has come together to define, develop, and deploy the UCIe standard, a universal interconnect at the package-level. In a recent news release, the UCIe Consortium announced “updates to the standard with additional enhancements for automotive usages – such as predictive failure analysis and health monitoring – and enabling lower-cost packaging implementations.” Figure 2 shows three use cases for UCIe. The first use case is for low-latency and coherency where two Network on a Chip (NoC) are connected via UCIe. This use case is mainly for applications requiring ADAS computing power. The second automotive use case is when memory and IO are split into two separate dies and are then connected to the compute die via CXL and UCIe streaming protocols. The third automotive use case is very similar to what is seen in HPC applications where a companion AI accelerator die is connected to the main CPU die via UCIe.

Fig. 2: Examples of common and new use cases for UCIe in automotive applications.

To enable such automotive use cases, UCIe offers several advantages, all of which are supported by the Synopsys UCIe IP:

• Latency optimized architecture: Flit-Aware Die-to-Die Interface (FDI) or Raw Die-to-Die Interface (RDI) operate with local 2GHz system clock. Transmitter and receiver FIFOs accommodate phase mismatch between clock domains. There is no clock domain crossing (CDC) between the PHY and Adapter layers for minimum latency. The reference clock has the same frequency for the two dies.
• Power-optimized architecture: The transmitter provides the CMOS driver without source termination. IT offers programmable drive strength without a Feed-Forward Equalizer (FFE). The receiver provides a continuous-time linear equalizer (CTLE) without VGA and decision feedback equalizer (DFE), clock forwarding without Clock and Data Recovery (CDR), and optional receiver termination.
• Reliability and test: Signal integrity monitors track the performance of the interconnect through the chip’s lifecycle. This can monitor inaccessible paths in the multi-die package, test and repair the PHY, and execute real time reporting for preventative maintenance.

Synopsys UCIe IP is integrated with Synopsys 3DIC Compiler, a unified exploration-to-signoff platform. The combination eases package design and provides a complete set of IP deliverables, automated UCIe routing for better quality of results, and reference interposer design for faster integration.

Fig. 3: Synopsys 3DIC Compiler.

New automotive SoC design trends for IVI applications

OEMs are attracting consumers by providing the utmost in cockpit experience with high-resolution, 4K, pillar-to-pillar displays. Multi-Stream Transport (MTR) enables a daisy-chained display topology using a single port, which consists of a single GPU, one DP TX controller, and PHY, to display images on multiple screens in the car. This revision clarifies the components involved and maintains the original meaning. This daisy-chained set up simplifies the display wiring in the car. Figure 4 illustrates how connectivity in the SoC can enable multi-display environments in the car. Row 1: Multiple image sources from the application processor are fed into the daisy-chained display set up via the DisplayPort (DP) MTR interface. Row 2: Multiple image sources from the application processor are fed to the daisy-chained display set up but also to the left or right mirrors, all via the DP MTR interface. Row 3: The same set up in row 2 can be executed via the MIPI DSI or embedded DP MTR interfaces, depending on display size and power requirements.

An alternate use case is USB/DP. A single USB port can be used for silicon lifecycle management, sentry mode, test, debug, and firmware download. USB can be used to avoid the need for very large numbers of test pings, speed up test by exceeding GPIO test pin data rates, repeat manufacturing test in-system and in-field, access PVT monitors, and debug.

Fig. 4: Examples of display connectivity in software-defined vehicles.

ISO/SAE 21434 automotive cybersecurity

ISO/SAE 21434 Automotive Cybersecurity is being adopted by industry leaders as mandated by the UNECE R155 regulation. Starting in July 2024, automotive OEMs must comply with the UNECE R155 automotive cybersecurity regulation for all new vehicles in Europe, Japan, and Korea.

Automotive suppliers must develop processes that meet the automotive cybersecurity requirements of ISO/SAE 21434, addressing the cybersecurity perspective in the engineering of electrical and electronic (E/E) systems. Adopting this methodology involves embracing a cybersecurity culture which includes developing security plans, setting security goals, conducting engineering reviews and implementing mitigation strategies.

The industry is expected to move towards enabling cybersecurity risk-managed products to mitigate the risks associated with advancement in connectivity for software-defined vehicles. As a result, automotive IP needs to be ready to support these advancements.

Synopsys ARC HS4xFS Processor IP has achieved ISO/SAE 21434 cybersecurity certification by SGS-TṺV Saar, meeting stringent automotive regulatory requirements designed to protect connected vehicles from malicious cyberattacks. In addition, Synopsys has achieved certification of its IP development process to the ISO/SAE 21434 standard to help ensure its IP products are developed with a security-first mindset through every phase of the product development lifecycle.

Conclusion

The transformation to software-defined vehicles marks a significant shift in the automotive industry, bringing together highly integrated systems and AI to create safer and more efficient vehicles while addressing sophisticated user needs and vendor serviceability. New trends in the automotive industry are presenting opportunities for innovations in ADAS and IVI SoC designs. Centralized zonal architecture, multi-die design, daisy-chained displays, and integration of ADAS/IVI functions in a single SoC are among some of the key trends that the automotive industry is tracking. Synopsys is at the forefront of automotive SoC innovations with a portfolio of silicon-proven automotive IP for the highest levels of functional safety, security, quality, and reliability. The IP portfolio is developed and assessed specifically for ISO 26262 random hardware faults and ASIL D systematic. To minimize cybersecurity risks, Synopsys is developing IP products as per the ISO/SAE 21434 standard to provide automotive SoC developers a safe, reliable, and future proof solution.

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