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Websites Can Now Spy on You Through Your Hard Drive
Thanks to the newly detailed FROST technique, telltale SSD activity can be measured in the browser using simple JavaScript.
๐บ๐ธ ๋ฏธ๊ตญ ยท IT/๊ธฐ์ ยท "MEASURED" ยท ์ด 8๊ฑด
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50.0
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50 = ์ค๋ฆฝ
100 = ๊ธ์ ์ฐ์ธ
์ต๊ทผ 7์ผ ๊ธฐ์ค 12,165๊ฑด์ ๋ถ์ํ ๊ฒฐ๊ณผ, ๋ด์ค ์ฌ๋ฆฌ์ง์๋ 50.0(๊ท ํ)์ ๋๋ค. ๊ธ์ 1๊ฑด(0.0%)ยท์ค๋ฆฝ 12,163๊ฑด(100.0%)ยท๋ถ์ 1๊ฑด(0.0%)์ด๋ฉฐ, ์ค๋ฆฝ ๋น์ค์ด ๋๋ ทํ๊ฒ ๋์ต๋๋ค. ์ฑํฅ ์ง์๋ ์ข ํฉ 19.4(์ค๋ ๊ท ํ)์ ๋๋ค.
Thanks to the newly detailed FROST technique, telltale SSD activity can be measured in the browser using simple JavaScript.
Token usage is a poor proxy for firm-wide productivity gains. Those only come with workflow redesign.
I have been an application-specific IC (ASIC) designer for almost three decades. Over that time, Iโve moved through the full academic trajectory, from graduate student to full professor; later, I transitioned to industry after an unsuccessful stint at entrepreneurship. When I made the switch to the private sector in 2019, I began focusing on a critically important aspect of the electronic industry: silicon intellectual property. As much as 80 percent of the physical area in todayโs most advanced chips is occupied by blocks that arenโt made for specific products or even designed by the consumer-facing companies that built them. Instead, chipmakers draw heavily on established silicon IP from companies like Arm, Cadence, Rambus, Synopsys, and the company I work for, Silicon Creations. Throughout my career, Iโve designed chips for very different purposes, including enabling the research program in my academic lab and expanding the IP portfolio of my company. When I joined Silicon Creations, I had no idea how differently the industry approaches IC design and encountered a steep learning curve. Initially, it seemed that much of my two decades of academic research and training did not directly translate to the role. I had to learn new skills and adopt a new mindset. Today, demand for ASICs is rapidly growing, driven by the need for specialized chips in the automotive sector, AI applications, and more. By one market estimate, the ASIC market is expected to grow from US $23.4 billion to $38.8 billion by 2033, and the semiconductor industry as a whole is projected to hit $1 trillion by 2030. The industry needs more chip designersโbut if youโre coming from an academic background as I did, there are a few things youโll need to know. Different goals lead to different strategies The differences between industry and academe begin with a divergence in purpose. In academia, my primary objective was to generate new knowledge: to propose a novel circuit technique, validate an unconventional architecture, or explore the limits of performance in a given domain. A successful chip is one that demonstrates a concept. In industry, it is not nearly enough to prove that something can work. The goal is to ensure that it works reliably, repeatedly, and at scale. Success is measured not by novelty but by whether the silicon meets specifications, yields as expected in production, and supports a competitive product delivered on schedule. This leads to a stark contrast in risk tolerance. Academic designs often deliberately push into unproven territory, where even partial success can yield valuable insight. In industry, however, we systematically minimize risk. The cost of failure makes first-time silicon success a central requirementโespecially at advanced technology nodes, where the lithography masks used to transfer circuit designs onto silicon wafers alone can cost tens of millions of dollars. As a result, industry design flows are built around eliminating uncertainty through conservative margins, extensive validation, and careful reuse of proven solutions. โAcademia explores the design space, asking what is possible, while industry exploits it, determining what is viable at scale.โ This paradigm has existed since the 1970s, when application-specific chip design was established. However, the gulf between academia and industry has expanded since the mid-2010s, when FinFET technology, a 3D architecture using vertical โfinsโ of silicon, was widely adopted in industry. System designs are also becoming increasingly modular with the advent of chiplets. This fundamentally altered the economics and complexity of ASIC development, with design costs rising by almost an order of magnitude. Initiatives like Taiwan Semiconductor Manufacturing Co.โs University FinFET Program and new government-funded chip-design hubs now let some well-resourced universities design for more advanced architectures, but the technology is still out of reach for many academics. What the industry-academia split means in practice Consider a startup developing an ASIC. Its engineering team may have deep expertise in a particular algorithm, sensor interface, or system architecture, the features that define its competitive advantage. But it is unlikely to possess world-class expertise in every supporting function. Developing each of these blocks internally would require significant time, capital, and specialized talent. Doing so could delay market entry beyond the startupโs viability. Even large semiconductor companies face similar constraints. Advanced-node development demands intense focus. Allocating a team to redesign a standard interface block that has already been implemented elsewhere may be difficult to justify when differentiation lies at the system level, such as an inference chipโs ability to speed up neural network computations. The time it takes to move a new chip from conception to market and risk mitigation, not self-sufficiency, govern most decisions about in-house development versus outsourcing. The economics of advanced IC manufacturing reinforce this reality. When the development cost of a leading-edge chip reaches hundreds of millions of dollars, minimizing risk becomes a central design imperative. In this context, silicon IP emerged as a practical solution. Similar to how software developers rely on preexisting libraries rather than writing every function from scratch, ASIC designers license predesigned, preverified silicon blocksโsuch as processor cores, memory interfaces, and security enginesโfrom highly specialized IP vendors. These blocks can then be integrated into larger, increasingly complex systems. Design scope, verification, and time horizons With the use of silicon IP, industry is able to widen the scope of its designs. Academic efforts tend to focus on block-level innovation: a new analog-to-digital converter architecture or an ultralow-noise amplifier, for instance. These designs typically abstract away many of the complexities of bringing a chip to market, such as packaging constraints, long-term reliability, and manufacturing yield. In industry, the focus shifts to system-level integration. Modern systems on chips, or SoCs, incorporate dozens or even hundreds of functional blocks. Managing signal integrity, timing, firmware interaction, and system-level validation becomes as critical as the design of any individual block. Verification philosophy also diverges sharply. In academia, the goal of verification is to demonstrate that the concept works under nominal conditions, which may not always reflect how it would perform in real applications. Even if only a fraction of fabricated chips from a multiproject wafer operates correctly, the design may still be considered a success if it validates the underlying idea. At my academic lab for instance, we used to receive 40 chips from a TSMC prototyping service and started testing them in batches of five. If the first five or 10 chips proved functional, we had already collected more than enough data for a publication. If some of them failed, we werenโt required to mention this when publishing the results. In industry, verification is exhaustive, critical, and often dominates the development schedule. Failures are measured in parts per million, and even rare anomalies are carefully analyzed and documented to identify root causes and prevent recurrence. When I started at Silicon Creations, I was surprised by the level of detail and scrutiny designs face. Differences in time horizons and economic constraints reinforce each of these contrasts. Academic projects operate on flexible timelines aligned with research and funding cycles. If I missed a deadline, I just had to wait for the next cycle. Industry projects are driven by fixed product schedules and market windows, frequently targeting costly leading-edge nodes to achieve competitive performance, power, and area efficiency. Missing a deadline can negate the value of an entire design and may have major financial consequences along the entire supply chain. In essence, academia explores the design space, asking what is possible, while industry exploits it, determining what is viable at scale. Both are indispensable, but they operate under fundamentally different definitions of success. As ASIC complexity continues to grow, understanding both perspectives will be essential for the next generation of engineers navigating the evolving semiconductor landscape. This article appears in the June 2026 print issue.
A practical introduction to phase noise concepts, explaining how oscillator instability affects RF systems and how phase noise is measured, analyzed, and reported. What Attendees will Learn What phase noise is and why it matters โ Learn how real-world oscillators differ from ideal ones, why short-term frequency instability arises, and why phase variations typically have a much greater impact than amplitude variations on system performance. How phase noise degrades system performance โ Understand the most common effects of excessive phase noise: spectral regrowth, reciprocal mixing, and constellation rotation in digital communications. How phase noise is measured and reported โ Explore the spectrum analyzer method and the cross-correlation technique, understand single sideband (SSB) phase noise plots and spot noise tables. What advanced phase noise measurements look like in practice โ Discover additional measurement types including integrated phase noise, additive (residual) phase noise, pulsed signal phase noise, and amplitude noise. Download this free whitepaper now!
Telltale SSD activity can be measured in the browser using simple JavaScript.
This sponsored article is brought to you by Master Bond. Outgassing is the release of volatile substances from a cured adhesive over time. These released materials, which may include residual solvents, unreacted monomers, or other chemical species, can deposit on nearby surfaces, causing contamination that interferes with sensitive components. What Is Outgassing and How Is It Measured? The industry standard for measuring outgassing is ASTM E595, developed by NASA. This test exposes a cured sample to 125 ยฐC at high vacuum (10โปโต to 10โปโถ torr) for 24 hours, measuring Total Mass Loss (TML) and Collected Volatile Condensable Materials (CVCM). To meet NASA low outgassing requirements, materials must exhibit less than 1 percent TML and less than 0.1 percent CVCM. Optical assemblies need contamination-free bonding and prevention of fogging the optics to maintain clarity. High-vacuum scientific equipment, semiconductor manufacturing tools, and aerospace electronics also demand low outgassing materials. Key Applications Low outgassing adhesives are essential wherever contamination could compromise performance and this is particularly relevant for space and satellite systems. Optical assemblies, including cameras, telescopes, and laser systems, need contamination-free bonding and prevention of fogging the optics to maintain clarity. High-vacuum scientific equipment, semiconductor manufacturing tools, and aerospace electronics also demand low outgassing materials. Even terrestrial optical devices benefit from reduced outgassing to ensure long-term reliability. EP30-2 is a versatile system can be used in a variety of applications in aerospace, electronic, optical and specialty OEM industries, especially when optical clarity and low outgassing are important criteria.Master Bond Ensuring Low Outgassing Performance Through Proper Handling Achieving specified outgassing performance requires attention to storage, mixing, and curing. For two-part systems, use the correct mix ratio and mix thoroughly to ensure complete reaction. Follow recommended cure schedules โ adding heat, even at modest temperatures of 150-200 ยฐF, significantly improves cross-linking and reduces outgassing. For UV-curable adhesives, ensure complete cure by using the correct lamp wavelength (typically 365 nm), adequate intensity, and proper exposure time with no shadowed areas. Troubleshooting Outgassing Issues If contamination appears on optical surfaces or outgassing test results are higher than expected, an incomplete cure might be one of the root causes. The first step is to verify that the adhesive has fully hardened to its specified Shore hardness. The next step is to consider adding or extending heat cure to improve cross-linking. Master Bond Product Recommendations Master Bond offers a range of adhesives meeting NASA low outgassing requirements. EP30-2 and EP21TCHT-1 are some examples of two-part epoxy systems that have been successfully deployed in demanding vacuum applications, including ultra-high vacuum environments. For applications requiring UV cure, Master Bond provides specialty UV formulations such as UV16 meeting ASTM E595, as well as dual-cure systems (UV plus heat) such as UV22DC80-10F for assemblies where shadows prevent complete UV exposure. These dual-cure products initiate with UV light and complete curing with heat as low as 180 ยฐF (80 ยฐC).
A comprehensive guide to error vector magnitude (EVM), the primary metric for quantifying modulation accuracy in Wi-Fi, LTE, and 5G NR systems. What Attendees will Learn What error vector magnitude is and how it is calculated โ Understand EVM as the distance between ideal and measured constellation points, learn the difference between peak and RMS normalization, and see how EVM is expressed in both percentage and decibel formats. How digital modulation works and why it matters โ Explore the fundamentals of ASK, FSK, PSK, APSK, and QAM modulation schemes, and understand why higher modulation orders increase throughput, while also demanding greater accuracy in signal transmission and reception. What causes degraded EVM in real-world systems โ Examine the four main categories of EVM contributors: amplitude effects (compression, noise, frequency response), phase effects (phase noise), I/Q imperfections (gain imbalance, quadrature error), and configuration issues. How to diagnose modulation impairments using constellation diagrams โ Learn how visual inspection of constellation diagrams can identify phase noise, amplifier compression, noise, in-band spurious signals, and I/Q modulator imperfections as root causes of degraded EVM. Download this free whitepaper now!
Quantum computing is a devilishly complex technology, with many technical hurdles impacting its development. Of these challenges two critical issues stand out: miniaturization and qubit quality. IBM has adopted the superconducting qubit road map of reaching a 1,121-qubit processor by 2023, leading to the expectation that 1,000 qubits with todayโs qubit form factor is feasible. However, current approaches will require very large chips (50 millimeters on a side, or larger) at the scale of small wafers, or the use of chiplets on multichip modules. While this approach will work, the aim is to attain a better path toward scalability. Now researchers at MIT have been able to both reduce the size of the qubits and done so in a way that reduces the interference that occurs between neighboring qubits. The MIT researchers have increased the number of superconducting qubits that can be added onto a device by a factor of 100. โWe are addressing both qubit miniaturization and quality,โ said William Oliver, the director for the Center for Quantum Engineering at MIT. โUnlike conventional transistor scaling, where only the number really matters, for qubits, large numbers are not sufficient, they must also be high-performance. Sacrificing performance for qubit number is not a useful trade in quantum computing. They must go hand in hand.โ The key to this big increase in qubit density and reduction of interference comes down to the use of two-dimensional materials, in particular the 2D insulator hexagonal boron nitride (hBN). The MIT researchers demonstrated that a few atomic monolayers of hBN can be stacked to form the insulator in the capacitors of a superconducting qubit. Just like other capacitors, the capacitors in these superconducting circuits take the form of a sandwich in which an insulator material is sandwiched between two metal plates. The big difference for these capacitors is that the superconducting circuits can operate only at extremely low temperaturesโless than 0.02 degrees above absolute zero (-273.15 ยฐC). Superconducting qubits are measured at temperatures as low as 20 millikelvin in a dilution refrigerator.Nathan Fiske/MIT In that environment, insulating materials that are available for the job, such as PE-CVD silicon oxide or silicon nitride, have quite a few defects that are too lossy for quantum computing applications. To get around these material shortcomings, most superconducting circuits use what are called coplanar capacitors. In these capacitors, the plates are positioned laterally to one another, rather than on top of one another. As a result, the intrinsic silicon substrate below the plates and to a smaller degree the vacuum above the plates serve as the capacitor dielectric. Intrinsic silicon is chemically pure and therefore has few defects, and the large size dilutes the electric field at the plate interfaces, all of which leads to a low-loss capacitor. The lateral size of each plate in this open-face design ends up being quite large (typically 100 by 100 micrometers) in order to achieve the required capacitance. In an effort to move away from the large lateral configuration, the MIT researchers embarked on a search for an insulator that has very few defects and is compatible with superconducting capacitor plates. โWe chose to study hBN because it is the most widely used insulator in 2D material research due to its cleanliness and chemical inertness,โ said colead author Joel Wang, a research scientist in the Engineering Quantum Systems group of the MIT Research Laboratory for Electronics. On either side of the hBN, the MIT researchers used the 2D superconducting material, niobium diselenide. One of the trickiest aspects of fabricating the capacitors was working with the niobium diselenide, which oxidizes in seconds when exposed to air, according to Wang. This necessitates that the assembly of the capacitor occur in a glove box filled with argon gas. While this would seemingly complicate the scaling up of the production of these capacitors, Wang doesnโt regard this as a limiting factor. โWhat determines the quality factor of the capacitor are the two interfaces between the two materials,โ said Wang. โOnce the sandwich is made, the two interfaces are โsealedโ and we donโt see any noticeable degradation over time when exposed to the atmosphere.โ This lack of degradation is because around 90 percent of the electric field is contained within the sandwich structure, so the oxidation of the outer surface of the niobium diselenide does not play a significant role anymore. This ultimately makes the capacitor footprint much smaller, and it accounts for the reduction in cross talk between the neighboring qubits. โThe main challenge for scaling up the fabrication will be the wafer-scale growth of hBN and 2D superconductors like [niobium diselenide], and how one can do wafer-scale stacking of these films,โ added Wang. Wang believes that this research has shown 2D hBN to be a good insulator candidate for superconducting qubits. He says that the groundwork the MIT team has done will serve as a road map for using other hybrid 2D materials to build superconducting circuits.