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by Tim Daniels, LSI Logic
The first quarter of 2003 will herald the first 90nm prototype capability. However, much of the industry views the latest node with some trepidation given the physical technology challenges that emerged in the move to 0.13ìm.
This paper outlines the various issues found at the latest 0.13ìm node and how these are being solved by the industry and adopted for 90nm design so that time to market compared to previous generations does not become a crippling issue. It is a general paper intended to update those involved in SoC and IP design on current implementation challenges.
Topics discussed include: Voltage, power and reliability issues at the micro level and RTL Analysis, Hierarchical design and database needs at the macro level. Example methodologies used to overcome these issues are given in order to demonstrate solutions for 90nm design.
To design a System-On-Chip (SoC) in 90nm Technology designers simultaneously juggle the dual challenges of controlling both the macro level with big complexity issues and the micro level with small physical issues whilst keeping to the overall constraint of Time-to-Market (TTM) in order to get a return on investment.
The complexity at 90nm is daunting. A 10x10mm die will be able to contain huge SoC functionality. An example of a typical 100mm2 design could be:
|Logic||10M Gates (20 x 0.5Mbit blocks, inc CoreWare)|
|Memory||39Mbits (200 instances: 1,2,4 Port Sync)|
|Package||Flip-Chip 960 (with matched pairs)
|Clocks||25 (%/MHz: 50/100, 25/200, 25/400)|
|CoreWare 4 off||MIPS5Kf + peripherals,
ARM7 + peripherals,
2x40Gbit SFI5/SONET I/F,
10Gbit Ethernet/XAUI I/F,
2 Micro Level Issue
Physical issues that designers face increase dramatically below 0.18um. At 90nm the SoC designer is faced with an array of issues. Key amongst these are: power drop, instantaneous voltage drop (IVD), clock, Crosstalk, and reliability issues (electromigration, yield enhancement). All such design integrity issues must be solved for timing, area and power simultaneously to get a working overall solution.
2.1 Changing Nature of Delay
Over exact technologies the nature of delay has changed from being within the cells to being within the interconnect technology.
Fig 1: Relative cell to interconnect delay
Switching from Aluminium/SiO2 to Copper/Lowk has helped reduce this effect but in 90nm interconnect delay will dominate with approximately 75% of the overall delay [Ref 1]. Thinner tightly packed interconnect are the root cause for many of the micro level issues discussed below.
2.2 Power Drop
Fig 2: A Flip-Chip power mesh
Ensuring an adequate power mesh is one of the biggest issues. At 90nm only 1v is available in the core so for less than a 5% voltage drop only 50mv drop is allowed across the mesh. The mesh construction is highly dependent upon the number of metal layers, sub-module and memory placement and package type. LSI Logic uses in-house tools to automatically generate a correct by construction power mesh.
Instance based power estimation techniques are used to analyse IR drop to ensure requirements are met. With such little headroom for voltage variation implementation of power mesh will be key.
2.3 Instantaneous Voltage Drop
Peak dynamic power usage, already important at 130nm, will be essential at 90nm. Instantaneous voltage drop (IVD) issues will require close analysis and the insertion of local on-chip capacitors to avoid issues resulting from excessive noise on the power mesh. Areas of high power usage within the die, especially memory, PLLs and clock drivers, will have to be handled very carefully in this respect. LSI Logic uses in-house tools to pre-place on-chip capacitors close to these blocks to avoid IVD failures. In addition on-chip capacitor are also added post-placement in order to reduce effects of IVD on the die. The amount of capacitance added depends on the switching activity (frequency) of the die and the types of cells used.
Fig 3: Concept of the IVD avoidance
Another method of reducing IVD now used is by replacing standard flip-flops with slower switching versions during the physical optimisation step of timing closure where the paths have sufficient slack time to stand this. Special Flip-flops are designed for the library specifically for this purpose.
At 90nm clock delay and skew will be very difficult to control. The best flows will be based around automated useful skew techniques and will control delay through branches of the clock by adjusting delay via post-clock insertion delay cell swapping. LSI Logic uses "lsimrs", its physical optimisation tool, to insert clock trees with useful clock skew. Clock crosstalk avoidance (via signal wire isolation) is built into such tools in order that the clocks are not aggressors or victims to nearby signal nets.
A side benefit of useful clock skew will be to somewhat reduce IVD issues on the die by spreading the clock edges along different clock branches.
Fig 4: Graphical description of Crosstalk
Crosstalk already a common signal integrity issue at 180/130nm, yet often ignored in many SOC flows today, will become critical at 90nm. Crosstalk is caused when an aggressor net running parallel to another victim net causes false switching (noise) or altered timing (delay) on the victim net. Careful analysis, particularly of the delta timing caused by the delay effects, takes roughly two weeks for a 3M gate design. This directly affects layout turn-around-time.
An alternative flow that LSI Logic uses is to add crosstalk avoidance placement/optimisation tools to add margin to the wire delays calculated in the layout tools and SDF timing file (lsidelay tool) in order avoid having to run these crosstalk avoidance analysis tools at all. This does not work for all designs since those pushing timing cannot stand the extra margin. In this case these extra margins have to be overridden and the extra crosstalk analysis tools are run instead.
Fig 5: Crosstalk Avoidance Flow
Automated avoidance during routing will eliminate such issues when these tools truly come on-line but such tools are not available today.
2.6 Reliability Issues
Many of the reliability issues seen at 130nm are already addressed via tool automation and methodology changes. These include:
• Metal antennae effects - where an electron charge can build up on long nets during manufacturing and blows up the transistor connected to it. Avoided by inserting diodes or adding metal jogs to the routing to force a layer change. The latter can cause many extra vias in the layout which has it's own reliability issues if not carefully controlled.
• Metal Slotting effects – this is where wide wires cause "metal dishing" effects due to processing limitations. Avoided by splitting wide wires.
• Simultaneously Switching Outputs (SSO) – where noise is injected into the power rails from many output changes at the same time and causes false signal values. Avoided by adding power/ground pads and by I/O isolation.
• Soft Errors – Alpha particles, both naturally occurring and from lead in packaging, can cause state inversion of a flip-flop or memory element. With shrinking technology the charge induced becomes more significant. Avoided by hardened flip-flops, error correction built into the memories and by fault tolerant system architectures.
• Memory yield – With memory taking an ever-larger proportion of the die, roughly 60% in the example above, overall good die per wafer will be lower than with pure logic. Avoided by adding redundant rows/columns and using Built-In Self Repair (BISR) with the larger embedded memories.
Electromigration (EM) is a key reliability effect that will worsen in 90nm. EM is caused by decreasing metal widths and increasing current density. When overstressed metal ions tend to migrate over time eventually causing the connection to break. LSI Logic runs "lsisignalem" after placement to set routing rules to ensure that metal and via structures are robust enough to avoid the EM issues that can occur on signal nets. Post route checking is also performed to ensure that the avoidance was successful.
Fig 6: Electromigration avoidance
2.7 Timing Files
One of the "small" issues that are not under control in all flows today is that of accurate delay calculation. Metal variation at 90nm will cause a vast difference in both resistance along the wire and capacitance between the wires. The overall max/min delay numbers are a complex equation of rise time along the nodes varying with R and C, where the worst case R and C does not necessarily supply the worst case delay numbers. LSI Logic uses "lsidelay" to generate accurate golden timing information from the RC data, which may be run on multi-processor machines for speed. Generating real best and worst case numbers from extracted R/C data is a non-trivial task where over-simplified algorithms will start to fall apart in 90nm. The tool can also handle varying PVT (Process/ Voltage/ Temperature) and other factors that affect the overall timing.
2.8 Metal Stack
Another more physical issue, not under control in all processes today, is that of the manufacturability and reliability of the copper/Low-K metal stack [Ref 2]. At 0.18um LSI Logic qualified Low-K with an aluminium metal stack. The low-K dielectrics gave huge benefits in terms of reducing the effects of coupling capacitance of the interconnect. At 0.13um LSI Logic used both Low-K and a copper metal stack. Switching from aluminium to copper has been a steep learning curve for the industry but having got this under control moving to the 90nm technology node will be relatively straightforward since the same basic materials will be used in the metal stack.
3 Macro Level Issues
When dealing with the "big" complexity issues, SoC design teams are being forced to face new challenges of defining and fixing system architectures based around truly market-available IP and then integrating in-house designed blocks as needed to complete the functionality. Controlling the "big" boils down to: picking the right IP to suit the architecture (and vice versa), developing a solid software and early hardware verification strategy, performing early RTL analysis on developed code, early physical planning, a complete test strategy all coupled closely with tough project management and business skills.
3.1 Physical RTL optimisation
Physical RTL optimisation analysis is now being recognized by the industry as an important tool for SoC designers, with a variety of EDA tools becoming available. Such tools comprehend the physical implementation of the RTL and supply early feedback as to poor RTL constructs that will cause problems in layout.
Fig 7: Early RTL analysis gives project control
Good RTL architecture and coding can save many man months in project timescales. The RTL analysis tools within LSI Logic's FlexStreamTM design flow perform fast synthesis, partitioning, placement, and timing analysis of an RTL block and provide detailed information about that block.
Such a tool highlights issues in the RTL that are likely to cause problems for the layout tools later in the flow. LSI Logic rules built into the tool specifically highlight RTL constructs that have caused problems in the past. Designers armed with this knowledge can then modify the architecture and coding of the RTL to avoid such issues.
One example of typical issues is RTL that infers a huge mux'ing function, common in communication switch SOC's, which will be difficult to layout. One alternative would be to split the mux'ing function in a different way. A second example is that of a controller block that is shared between two sub-modules and is in the critical timing path for both modules. One solution to this is to duplicate the controller function locally to each.
The best RTL Analysis tools therefore provide an idea of the physical issues that have been inferred in RTL code even before floorplanning is started. They provide very fast feedback on how to optimise the architecture and coding which is linked directly back to the source RTL code, in a way that early floorplanning/placement tools simply cannot.
Early physical planning of big SoC designs is a pre-requisite. An early floorplan showing location of the high speed I/O, block and memory location quickly gives an idea of the feasibility of the physical design and goes one stage further than the RTL Analysis tools. For example, the SFI5 physical layer interface in the design example is complex - 16 differential pairs making 40Gbit/s (16X2.5Gbit/s) - and requires careful placement on the die, the package and the board. Such system level skill sets are non-trivial and highly sought after in order to drive products quickly to market with low risk. Floorplanning a 10Mgate design requires detailed routing of global signal and clock nets at this early stage in order to control time of flight and define timing and area budgets for the block. Modern tool flows, such as the FlexStream Design System, allow hierarchical design approaches for each of these sub-blocks but it is controlling timing closure early at this top level that is the key to a fast turn-around-time and eventually a successful product.
Fig 8: Typical Floorplan at 0.13um
4 Cross-Border Issues
There is a further category of issues that crosses macro and micro levels, including test, overall chip power/temperature and database size, that will challenge engineers in 90nm. Among the test issues: Traditional "full scan" stuck-at fault coverage test strategies are starting to take too long in production testing and are increasingly shown to have too many test escapes, IDDq testing is becoming less viable due to increasing transistor leakage. Silicon vendors, EDA companies and research institutes are actively working on such issues and we are likely to see fast evolving test strategies in the near future including Scan compression, Logic BIST, and transition fault coverage.
Overall chip power will become an increasing focus for SoC in 90nm because die temperature has a direct effect on failure rate and therefore the reliability of the SoC. Approaches used in battery-operated devices for years, such as slow clocking and sleep modes as well as the more usual gated clocking, grey code addressing and memory splitting will be widely used. EDA tools will have to truly consider the third axis of power (as well as time and area) within the design flow.
4.1 Database Sizes
The last cross-border challenge to be highlighted is that of file and database size. An example of controlling database size, and therefore turn-around-time, is that of the typical timing signoff flow today: SPEF files (RC data) are extracted at chip level, then SDF files are generated using the silicon vendor's golden timing engine and an STA tool will analyze this database. Final flat timing runs like this already take several days, each intermediate file taking several Gbytes of data, and running only on a machine with a 64bit operating system. Short term, key tools such as LSI Logic's delay calculator "lsidelay" that generates the SDF have been adapted to run on multi-threaded and multi-processor compute farms. Longer term the industry will adopt methodologies such as OLA library models (a library with a built in golden timing calculator supplied by the silicon vendor) and OpenAccess common databases such that extraction, delay calculation and STA analysis can be accomplished in a much more efficient manner. Using a single database into which all tools can plug will completely avoid the many intermediate files of varying formats with differing limitations being required. [Ref 1].
Fig 9: File and database issues
In general, the management task of generating and controlling a machine, software and human resource infrastructure to enable SoC design within time-to-market constraints could end up being the biggest challenge of all. This is especially true as it involves the cross-industry collaboration of silicon vendors, EDA vendors and system houses.
When looking at volume production requirements the need for lowest cost, smallest die, lowest power and fastest speed will always push SoC design teams to the leading edge of technology. Foundries are already running early 90nm silicon at an R&D level and early SPICE rules are already available. First sign-off cell libraries are now available while the first IP blocks will be available during the first half of 2003 along with prototype capability.
Whilst some may believe the industry is at its lowest ebb for years with balance sheets showing red in many industry sectors, there is already a 90nm SoC infrastructure being put in place that will yield leading edge products within the next year. For SoC designers, the need to grapple with the technology challenges of 90nm will be here sooner than many had dared hope.
1 Down to the Wire, Lavi Lev et al, Cadence
2: Failures plague 130-nanometer IC processes, Ron Wilson, EETimes
We recently looked at the origins of the integrated circuit (IC) and the calculator, which was the IC’s first killer app, but a surprise twist is that the calculator played a big part in the invention of the next world-changing marvel, the microprocessor.
There is some dispute as to which company invented the microprocessor, and we’ll talk about that further down. But who invented the first commercially available microprocessor? That honor goes to Intel for the 4004.
We pick up the tale with Robert Noyce, who had co-invented the IC while at Fairchild Semiconductor. In July 1968 he left Fairchild to co-found Intel for the purpose of manufacturing semiconductor memory chips.
While Intel was still a new startup living off of their initial $3 million in financing, and before they had a semiconductor memory product, as many start-ups do to survive they took on custom work. In April 1969, Japanese company Busicom hired them to do LSI (Large-Scale Integration) work for a family of calculators.
Busicom’s design, consisting of twelve interlinked chips, was considered a complicated one. For example, it included shift-register memory, a serial type of memory which complicates the control logic. It also used Binary Coded Decimal (BCD) arithmetic. Marcian Edward Hoff Jr — known as “Ted”, head of the Intel’s Application Research Department, felt that the design was even more complicated than a general purpose computer like the PDP-8, which had a fairly simple architecture. He felt they may not be able to meet the cost targets and so Noyce gave Hoff the go-ahead to look for ways to simplify it.
Hoff realized that one major simplification would be to replace hard-wired logic with software. He also knew that scanning a shift register would take around 100 microseconds whereas the equivalent with DRAM would take one or two microseconds. In October 1969, Hoff came up with a formal proposal for a 4-bit machine which was agreed to by Busicom.
This became the MCS-4 (Micro Computer System) project. Hoff and Stanley Mazor, also of Intel, and with help from Busicom’s Masatoshi Shima, came up with the architecture for the MCS-4 4-bit chipset which consisted of four chips:
In April 1970, Noyce hired Federico Faggin from Fairchild in order to do the chip design. At that time the block diagram and basic specification were done and included the CPU architecture and instruction set. However, the chip’s logic design and layout were supposed to have started in October 1969 and samples for all four chips were due by July 1970. But by April, that work had yet to begin. To make matters worse, the day after Faggin started work at Intel, Shima arrived from Japan to check the non-existent chip design of the 4004. Busicom was understandably upset but Faggin came up with a new schedule which would result in chip samples by December 1970.
Faggin then proceeded to work 80 hour weeks to make up for lost time. Shima stayed on to help as an engineer until Intel could hire one to take his place.
Keeping to the schedule, the 4001 ROM was ready in October and worked the first time. The 4002 DRAM had a few simple mistakes, and 4003 I/O chip also worked the first time. The first wafers for the 4004 were ready in December, but when tried, they failed to do anything. It turned out that the masking layer for the buried contacts had been left out of the processing, resulting in around 30% of the gates floating. New wafers in January 1971 passed all tests which Faggin threw at it. A few minor mistakes were later found and in March 1971 the 4004 was fully functional.
In the meantime, in October 1970, Shima was able to return to Japan where he began work on the firmware for Busicom’s calculator, which was to be loaded into the 4001 ROM chip. By the end of March 1971, Busicom had a fully working engineering prototype for their calculator. The first commercial sale was made at that time to Busicom.
Now that Intel had a microprocessor, they needed someone to write software. At the time, programmers saw prestige in working with a big computer. It was difficult enticing them to stay and work on a small microprocessor. One solution was to trade hardware, a sim board for example, to colleges in exchange for writing some support software. However, once the media started hyping the microprocessor, the college students came banging on Intel’s door.
Intel’s market was big computer companies and there was concern within Intel that computer companies would see Intel as a competitor instead of a provider of memory chips. There was also a question about how they would support the product. Some at Intel also wondered whether or not the 4004 could be used for more than just a calculator. But at one point Faggin used the 4004 itself to make a tester for the 4004, proving that there were more uses.
At the same time, cheap $150 handheld calculators were creating difficulties for Busicom’s more expensive $1000 desktop ones. They could no longer pay Intel the agreed contract price. But Busicom had exclusive rights to the MCS-4 chips. And so a fateful deal was made wherein Busicom would pay a lower price and Intel would have exclusive rights. The decision was made to sell it and a public announcement was made in November 1971.
By September 1972 you could buy a 4004 for $60 in quantities of 1 to 24. Overall, around a million were produced. To name just a few applications, it was used in: pinball machines, traffic light controllers, cash registers, bank teller terminals, blood analyzers, and gas station monitors.
Most inventions come about when the circumstances are right. This usually means the inventors weren’t the only ones who thought of it or who were working on it.
In October 1968, Lee Boysel and a few others left Fairchild Semiconductor to form Four-Phase Systems for the purpose of making computers. They showed their system at the Fall Joint Computer Conference in November 1970 and had four of them in use by customers by June 1971.
Their microprocessor, the AL1, was 8-bit, had eight registers and an arithmetic logic unit (ALU). However, instead of using it as a standalone microprocessor, they used it along with two other AL1s to make up a single 24-bit CPU. They weren’t using the AL1 as a microprocessor, they weren’t selling it as such, nor did they refer to it as a microprocessor. But as part of a 1990 patent dispute between Texas Instruments and another claimant, Lee Boysel assembled a system with an 8-bit AL1 as the sole microprocessor proving that it could work.
Garrett AiResearch developed the MP944 which was completed in 1970 for use in the F-14 Tomcat fighter jet. It also didn’t quite fit the mold. The MP944 used multiple chips working together to perform as a microprocessor.
On September 17, 1971, Texas Instruments entered the scene by issuing a press release for the TMS1802NC calculator-on-a-chip, with a basic chip design designation of TMS0100. However, this could implement features only for 4-function calculators. They did also file a patent for the microprocessor in August 1971 and were granted US patent 3,757,306 Computing systems cpu in 1973.
Another company that contracted LSI work from Intel was the Computer Terminal Corporation (CTC) in 1970 for $50,000. This was to make a single-chip CPU for their Datapoint 2200 terminal. Intel came up with the 1201. Texas Instruments was hired as a second provider and made samples of the 1201 but they were buggy.
Intel’s efforts continued but there were delays and as a result, the Datapoint 2200 shipped with discrete TTL logic instead. After a redesign by Intel, the 1201 was delivered to CTC in 1971 but by then CTC had moved on. They instead signed over all intellectual property rights to Intel in lieu of paying the $50,000. You’ve certainly heard of the 1201: it was renamed the 8008 but that’s another story.
Do you think the 4004 is ancient history? Not on Hackaday. After [Frank Buss] bought one on eBay he mounted it on a board and put together a 4001 ROM emulator to make use of it.
[Main image source: Intel C4004 by Thomas Nguyen CC BY-SA 4.0]
Are you a tall and thin engineer or maybe a short and fat one? I’m not talking about body shapes, but rather what kind of engineer are you. As unflattering as these phrases appear, they’ve been used in the past to describe two types of engineers: those with broad experiences versus certified with deeper backgrounds in their domains, respectively. Most recently, the tall-thin phrase has been used to describe engineering experience levels and skills in a data-driven society. But before one can appreciate the modern interpretation, one must first understand a bit of semantic confusion with the term tall, thin engineer.
Historical Viewpoint: At first glance, one might think of a tall, thin engineer in terms of their levels and years of experience, meaning very deep and specific. After all, deep is just another word for tall if you’re looking from the bottom-up. Apparently, that was not the perspective taken by Howard Sachs, VP and general manager of Fujitsu Microelectronics Group’s newly formed LSI division back in the late 90’s. According to Sachs, the idea of a tall, thin engineer was first promoted in the 1980’s at UC Berkeley when the first 3 micron CMOS chips (up to 100k gates) were just coming into the market. There were no real layout tools and in theory one really good engineer could do both design and layout of a chip, in essence, creating the entire chip.
This mythical person was known as a tall, thin engineer, meaning someone whose knowledge was broad and just deep enough for the existing technology to do everything required in the chip development life cycle. Manufacturing of the chip was still left to the fabs.
My Take: Personally, I’ve found Sachs interpretation of the term to be a bit misleading. To me, a tall, thin engineer seems more indicative of a specialist, i.e., someone who has a deep (or tall) understanding in a very narrow (or thin) area of technology.
I guess it really is a matter of perspective. If you view that life cycle development process in the classic way, i.e., as one long, continuous process like the early waterfall model, then a long, tall engineer would start with design, go through implementation (layout) and test to do everything needed to design and deliver the product (except manufacturing). In this case, this mythical tall thin engineer would really be a one laying down across all phases of the development process. It would really be a lazy engineer in repose.
Meanwhile, a decade before the Sachs description of a tall, thin engineer, another profession had emerged with a different perspective of the engineer who could “do it all” or, more accurately, control it all. In the late 1960s, the first comprehensive definition of a systems engineer (SE) emerged from the Department of Defense. The SE was a person with wide engineering and program management skills but who specialty or depth was more limited. Instead of going deep as a specialist might, the SE went broad across a number of technical domains and disciplines.
Image Source: Franco Recchia / Microchip Cities
Today, a systems engineer is associated with many different domains from hardware, software, network, data, systems-of-systems (SoS) and others. Regardless of the domain, a systems engineer would be considered to be a tall, thin engineer. This viewpoint is supported by a paper titled “Maturity Curve of Systems Engineering,” by Roy Alphonso de Souza of the Post Naval Graduate School (2008). In his work, de Souza explained that to be effective as an engineer must possess a series of traits that come from both academic study and practical experience. His approach was to prove this hypothesis via fuzzy logic scales and learning curves. Two variables were to be considered: years of experience and annual income.
I won’t go into the details of this document but rather highlight the definitions of a tall, thin and short-fat engineers. De Souza referenced Caltech Professor Carver Mead’s vision of a "tall, thin man, one who becomes accomplished in all aspects of chip design, from algorithm creation to layout, from concept to chip.” This person was one who possessed a broad technical skill and who could easily integrate concepts from multiple disciplines.
But another engineering type was also emerging at the time, namely, the short, fat person with a specialized set of technical skills but who couldn’t easily integrate concepts from multi-disciplines. Today, we would call this person a specialist without the unflattering and unrealistic “short, fat” reference.
But this figure is somewhat deceptive. For one thing, the descriptive skill blocks don’t line up evenly between these two groups. Secondly and most telling, the entire product life cycle is contained in the uppermost single block under the tall, thin engineer. Today, this block would be represented as a long process that included all of the stages in product development, as I mentioned above.
Now, let’s return to the implications of the tall, thin engineer for modern systems. A few years ago, the World Economic Forum in Davos determined that 7 million jobs, mainly classic office activities, will disappear over the next decade due to the fourth industrial revolution. In that same time frame, it was predicted that 2 million new jobs will be created mainly in the fields of computer science, mathematics, electronics and information technology.
You may remember that Industry 4.0 has been described as a wave of digital transformation that will reshape all of the manufacturing industry. However, this movement has often been treated incorrectly as equivalent to digitalization or complete automation. This view misinterprets the key drive of Industry 4.0, which is the generation of vasts amount of data and the capability to quickly analysis it to make faster decision. The emphasis on decision making based on data fits well into the experience of a systems engineer, who must constantly perform trade-off analysis to determine requirements and decide upon implementation strategies, e.g., between the use of hardware or software.
What kind of engineer will be needed to deal with the challenges and complexity required to handle Industry 4.0? Probably the tall thin engineer of the past but this time armed with the design and development tools of the modern age of electronics. For example, a tall thin engineer would need to use simulation and virtual prototyping tools to handle trade-off analysis of very complex issues. This engineer would probably use familiar with system modeling tools, e.g., SysML. They would also understand testing, manufacturing and production issues – at least to some extent.
Whatever the terminology, today’s needs require both the systems expert and the specialist to create, develop and build products that support Industry 4.0 and all that it encompasses.
John Blyler is a Design News senior editor, covering the electronics and advanced manufacturing spaces. With a BS in Engineering Physics and an MS in Electrical Engineering, he has years of hardware-software-network systems experience as an editor and engineer within the advanced manufacturing, IoT and semiconductor industries. John has co-authored books related to system engineering and electronics for IEEE, Wiley, and Elsevier.
When teachers use research-based practices and strategies to serve students with learning differences, they promote equity in education and help to develop the nation’s future workforce. A clearer understanding of the degree to which teachers implement such practices can inform efforts to Improve teacher preparation and training.
In 2021, the EdWeek Research Center fielded a survey to learn more about teachers’ perspectives regarding instructional practices that can help students with learning differences to succeed. In the survey, researchers defined this population to include students with specific learning disabilities (such as dyslexia) and students with other processing challenges that can impact learning (such as attention deficits). The study’s definition of this group included students who had been formally identified for special education services and those who had not been identified for such services but experienced learning challenges.
Effective approaches for teaching these students have taken on even more critical importance due to the coronavirus pandemic. When schools were forced to abruptly switch to remote learning in early 2020 to curtail the spread of virus, students of all backgrounds experienced unprecedented disruptions to their learning and their daily lives. There is widespread concern that those changes have caused many students to miss out on key academic content and to face mental health difficulties. Students with disabilities and learning differences, in particular, lost existing services and supports. The pandemic also made it more difficult for educators to identify students in need of special education services. Implementation of instructional best practices will be vital in helping them recover lost ground.
The survey examined teachers’ perspectives on effective practices, their implementation of those instructional strategies, and the broader beliefs that impact their approaches to teaching. This report outlines survey findings. The goal of the research is to provide resources that help to guide teacher training and to boost teachers’ use of effective strategies.
Dealing with student behavioral and mental health issues has been many teachers’ biggest barrier to addressing unfinished learning, according to a Khan Academy survey published July 26.
Nearly 7 in 10 teachers who participated in the survey chose “student behavioral issues” as a barrier to addressing unfinished learning, and 57 percent of teachers chose “student mental health.”
The nationally representative survey of 639 teachers, conducted last month by market research and data analytics firm YouGov for Khan Academy, explores teachers’ views on addressing unfinished learning, the use of mastery learning, providing feedback to students, and the grading system. It comes as schools prepare to continue the work of helping students catch up on unfinished learning that developed during the COVID-19 pandemic.
Another theme on the list of barriers to addressing unfinished learning is teachers’ finite time. Sixty-one percent of teachers said there are “too many demands on my time,” 53 percent said there’s “not enough flexibility or time in the school year to pause and address issues,” 41 percent said “lack of time in the school day,” and 38 percent said “lack of planning time.”
But even with all these hindrances, the survey found that during the 2021-22 school year, more than 9 in 10 teachers said they were able to identify the learning gaps that need to be addressed among their students. And 59 percent of teachers said their students mastered the content they needed to during the 2021-22 school year.
The method that is most helpful in identifying learning gaps is “working individually with students during class,” according to 78 percent of surveyed teachers. That strategy is followed closely by “classroom assessment” (74 percent), “asking students questions in class” (70 percent), and “student classwork/homework” (70 percent).
Teachers also said that the most important changes in school needed to help students catch up don’t directly deal with academics: Sixty percent said there needs to be more emotional and behavioral support and 56 percent said there needs to be more family engagement. The third most popular option is tied between having “less rigid district pacing guidelines” and “consistent small group instruction,” both supported by 52 percent of respondents.
The survey also found that an overwhelming majority (84 percent) of teachers agree that mastery learning can help address unfinished learning, but only a small majority (53 percent) use mastery learning in their classrooms. Mastery learning means knowing which skills a student has mastered and not mastered, providing feedback on what students got wrong and why, offering as many opportunities as needed for students to demonstrate mastery, and continuing to provide instruction until a skill is mastered.
More than 90 percent of respondents said it was “very” or “extremely” important to do that.
The same issues that are barriers to addressing unfinished learning are also obstacles to implementing mastery learning, according to the survey. Sixty-five percent of teachers said “lack of time” and 55 percent said “student behavioral issues.” But 49 percent of teachers also said having large class sizes is a barrier to implementing mastery learning.
More than 6 in 10 teachers said they feel like they don’t spend enough time providing feedback to students. On average, teachers spend 8.6 hours providing feedback, but the survey found that teachers would like to spend 12.2 hours on average on providing feedback.
The survey found that 84 percent of teachers use the traditional grading system, but 66 percent of teachers agree that a standards-based grading system would be better than traditional letter grades. A standards-based grading system breaks down the subject into smaller learning targets and grades students based on their mastery of those targets, instead of having an overall letter grade for the subject based on many assignments. More than 6 in 10 teachers said Ds and Fs cause students to lose motivation, and half of the respondents said that Ds and Fs discourage students from working to catch up.
Despite liking the concept of standards-based grading, 71 percent of teachers said letter grades provide an incentive for students to succeed. But students shouldn’t be receiving only letter grades, they said. About 3 in 4 teachers agreed that it’s important to provide behavioral feedback in letter grades, and 71 percent said including behavioral feedback in letter grades teaches important life skills.
[Lee Adamson] is no stranger to classic computers. He recently picked up a Heathkit H11A which, as you might remember, is actually a PDP-11 from DEC. Well, technically, it is an LSI-11 but still. Like a proper LSI-11, the computer uses the DEC QBus. Unlike a lot of computers of its day, the H11 didn’t have a lot of switches and lights, but it did have an amazing software library for its day.
[Lee] takes us through a tour of all the different cards inside the thing. It is amazing when you think of today’s laptop motherboards that pack way more into a much smaller space. He also had to fix the power supply.
We are looking forward to seeing more videos on this computer. We miss the days that your computer broke down into multiple boards plugged into a backplane. Even though the computer is a Heathkit, the CPU board came from DEC assembled. However, Heathkit had its own boards that you did build along with things like power supplies.
The power supply needed some care, as you might expect. A diode wasn’t attached properly but it wasn’t clear if it had been damaged in transit or if it had never been installed correctly. Replacing it put the power supply right and now he’s ready to see if the thing will start up.
Key to the Center’s mission is the success of our students: we support them, corporate partner companies recruit them, and they come back as alumni to engage with our programs and activities. The Farmer School of Business tagline is “Beyond Ready,” and our supply chain students live up to this expectation. Our curriculum focuses on experiential learning, analytical skills, collaboration and team development, and leadership by practical example. The content is holistically focused on logistics, strategic sourcing, operations, quality and process improvement, enterprise IT systems, and integrated supply chain management. In an environment where supply chain talent has never been more important, we are preparing some of the best!
The CSCE offers Corporate Partners priority in engaging with our students in many different ways, including: participating in our Supply Chain Executive Speaker Series, networking and recruiting events through our student-led Supply Chain Management Association, a special pre-night Networking event prior to the large Miami University Career Expo, Company-Student class projects for “mini-consulting” events, and more.
Students—our relationships with Corporate Partners ensure that Miami University Supply Chain & Operations Management students have ongoing opportunities to engage with professionals, participate in hands on experiential learning, build their network and develop relationships with leaders who will help launch their supply chain & operations management careers!
Two to three times per semester, the supply chain management faculty host top supply-chain management executives to speak to 300-400 students currently enrolled in supply chain management courses. This serves the dual purpose of allowing students to interact with top supply chain management executives and understand current SCM issues, while giving companies the exposure to Miami's SCM students. Most of the executives represent companies who actively recruit Miami supply chain students for job opportunities.
A sampling of exact speakers and companies involved includes:
The Supply Chain Management Association (SCMA) is a student-led organization that exists to provide career development opportunities for supply chain students at Miami University, although the club is open to all majors. These career development opportunities include professional speakers, tours of plants, case competitions amongst Miami students and other universities, and social events. Additionally, the club elects an executive board that allows students to hold a leadership role and connect with professors and companies.
Examples of previous facility tours include:
Each fall, the Department of Management hosts select company partners the evening before the career fair, with an informal time to meet our students (typically juniors and seniors) before the career fair. Previously invited companies include:
Each semester, students of MGT 432 are divided into groups to collaborate with companies that have a track record of supporting the supply chain program on a project that is either something they’re considering or an internal study that they have conducted in the past. Firms that have supported MGT 432 include (but not limited to):
In the past several years, we have asked our project partners to ensure that their projects are specifically relevant to purchasing and strategic sourcing topics, rather than any supply chain topic. These include provider selection decisions, consolidating/centralizing purchasing spending, category studies, project lifecycle analyses, cost of ownership estimation, and product packaging. Other supply chain subjects that have been used in the past include warehouse heatmapping, global logistics routing, 5S implementation, and more.
The Management Department sponsor SCM students and pays most of the expenses for several SCM-oriented case competitions each year. Most recently, this included:
Recent Advances in Machine Learning (ML) Technologies Extend Synopsys' QoR Leadership
MOUNTAIN VIEW, Calif., March 4, 2020 -- Synopsys, Inc. (Nasdaq: SNPS) today announced that Samsung has adopted the industry-leading IC Compiler™ II place-and-route solution, part of the Synopsys Fusion Design Platform™, for its next-generation 5nm mobile system-on-chip (SoC) production design. In order to meet the aggressive design goals of this complex SoC, Samsung employed IC Compiler II's cutting-edge machine learning technologies resulting in significant QoR and productivity boosts of up to five percent higher frequency, five percent lower leakage power and faster TAT. The rapid development of Samsung's high-volume mobile SoC marks an important milestone as the first production design at Samsung to leverage IC Compiler II's ML-implementation technologies.
"We constantly look for ground-breaking technologies from EDA vendors to enable pushing the power, performance and area (PPA) envelope for our next-generation products," said Youngmin Shin, vice president of System LSI Design Technology at Samsung Electronics. "Machine learning-driven chip design represents a paradigm shift which delivers a significant QoR and productivity leap required to tackle the mounting challenges of smaller geometries. We are extremely impressed with Synopsys for making the ML vision a reality in IC Compiler II and delivering exceptional QoR results."
ML offers opportunities to enable self-optimizing design tools that can continuously learn and Improve in customer environments, giving Synopsys a new arsenal of solutions for today's demanding semiconductor market. ML-driven capabilities in Synopsys' IC Compiler II and Fusion Compiler™ implementation solution capture design behavior at multiple stages of design evolution, offering upstream engines faster and accurate visibility into complex downstream effects - allowing designers to achieve new levels of productivity and QoR. Today's announcement is part of a multi-year initiative and strategic investment in ML technology at Synopsys' Design Group, aimed at enabling an orchestrated, self-optimizing design environment with ML, everywhere.
"Synopsys' investment into machine learning-driven implementation and signoff has opened up new avenues to further extend our PPA leadership with unsurpassed design QoR and TTR," said Sanjay Bali, vice president of marketing, Design Group at Synopsys. "Through deep collaborations with our technology-leading partners like Samsung, we are able to develop and deploy machine-learning technologies to help customers realize their ultra-complex chip designs."
Synopsys, Inc. (Nasdaq: SNPS) is the Silicon to Software™ partner for innovative companies developing the electronic products and software applications we rely on every day. As the world's 15th largest software company, Synopsys has a long history of being a global leader in electronic design automation (EDA) and semiconductor IP and is also growing its leadership in software security and quality solutions. Whether you're a system-on-chip (SoC) designer creating advanced semiconductors, or a software developer writing applications that require the highest security and quality, Synopsys has the solutions needed to deliver innovative, high-quality, secure products. Learn more at www.synopsys.com.
SUWANEE, Ga.--(BUSINESS WIRE)--Aug 1, 2022--
Future Technologies Venture, LLC, (Future Technologies) a Lead System Integrator focused on end-to-end digital transformation solutions, today announced their best results in the company’s 22-year history driven by the growing number of production projects with Private Cellular (4G/5G) technologies across multiple vertical markets.
“Our team has worked hard to create this opportunity over the last 12 years while building our Private Cellular Network and Edge practice starting in the military vertical market and then expanding into the industrial markets 6 years ago,” said Peter Cappiello, CEO. “This experience and domain expertise is a real differentiator for Future Technologies in the market based on all the production projects we have delivered over our 12 years in the Private Cellular market versus our competition still being in the Proof-of-Concept phase of development.”
Future Technologies is focused on solving their end clients’ problems through a focused approach on the USE CASES. Future Technologies evaluates their customer’s use case roadmap to include existing, new, and future use cases. Through this process, Future Technologies can help construct a network and edge roadmap to plan out those use cases with the correct connectivity maximizing the value of the customer’s existing infrastructure and recommending new infrastructure to help the customer achieve their long-term objectives.
“In these brownfields or Greenfields deployments we believe that our clients need to have a plan for the co-existence of Wi-Fi, Public Cellular, IoT, Fixed Wireless and Private Cellular with an approach of mapping the Use Cases to the right connectivity layer to provide the best solution,” comments Ian Chan, President. “The result of this best practice is a healthy network that has the use cases load balanced between the different connectivity options and optimized to meet the operational needs as no one connectivity approach solves all the requirements.”
Future Technologies has experience and domain knowledge in delivering Private Cellular Projects for Fortune 50, 100, 1,000 and 5,000 customers across several specialized vertical markets:
A key component to this approach is Future Technologies’ Innovation Center in Atlanta, GA. At this living lab environment Future Technologies demonstrates what is possible with REAL WORLD DEMONSTRATIONS. Future Technologies drives these client experience engagements by demonstrating End to End solutions with Network (Public Cellular, Private Cellular, Wi-Fi, LoRaWAN, Bluetooth), Compute (Edge, Cloud), vertical market specific use case solutions (Machine Learning, Artificial Intelligence, Computer Vision, IoT Sensors, Robotics, Connected Worker, Remote Expert) and the application layer. By creating this environment, Future Technologies can bring the total value proposition together as to the WHY and HOW with use cases and live demonstrations to architect a plan that helps client’s meet their digital transformation goals.
“We are humbled by our success in the first half with much appreciation for our customer’s trust in us, the support of our Eco-system partners and the support of our team members. We are truly excited about what the FUTURE holds for Team Future Tech,” said Peter Cappiello, CEO.
For more information on joining #TeamFutureTech, please visit: Career Opportunities — Future Technologies Venture, LLC (futuretechllc.com)
About Future Technologies Venture, LLC
Future Technologies Venture, LLC is a Lead System Integrator (LSI) specializing in the assessment, planning, design, implementation, and support of innovative communications solutions for vertical markets – DoD, Utility, Oil & Gas, Manufacturing and Transportation. Future Technologies maintains a strong concentration on emerging standards such as 5G, 4G, Private LTE, WIFI, SCADA and Automation technologies. Future Technologies is headquartered in Atlanta, GA. www.futuretechllc.com
View source version on businesswire.com:https://www.businesswire.com/news/home/20220801005204/en/
CONTACT: Bari Anderson
KEYWORD: GEORGIA UNITED STATES NORTH AMERICA
INDUSTRY KEYWORD: ELECTRONIC DESIGN AUTOMATION DATA MANAGEMENT TECHNOLOGY VOIP OTHER TECHNOLOGY TELECOMMUNICATIONS SOFTWARE
SOURCE: Future Technologies Venture, LLC
Copyright Business Wire 2022.
PUB: 08/01/2022 07:00 AM/DISC: 08/01/2022 07:02 AM
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