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预测半导体行业的趋势

PART ONE – TOOLS FOR PREDICTING SEMICONDUCTOR TRENDS

第一部分 - 预测半导体趋势的工具

Chapter 1: LEARNING CURVES PROVIDE PREDICTABLE COST AND REVENUE PER TRANSISTOR

学习曲线提供单位晶体管的可预测成本和收入

Figure 1 is the most basic of all the predictable parameters of the semiconductor industry, even more so than Moore’s Law.  It is the learning curve for the transistor.  Since 1954, the revenue per transistor (and presumably the cost per transistor, if we had the data from the manufacturers) has followed a highly predictable learning curve.  Before Moore’s Law, the learning curve provided a guiding light for the semiconductor industry.  Texas Instruments used it for strategic advantage and shared its data with Boston Consulting Group who published a book called “Perspectives on Experience”1.  In the days of germanium and silicon discrete transistors, companies like TI could use the learning curve, for example, to predict what the unit cost would be after 100,000 units were produced, based upon the actual cost per unit of the first 1,000 units produced.  They could then price the particular transistor product at a loss initially to gain leading market share and therefore achieve higher profitability and market influence when they reached future high unit volume sales.  TI didn’t create the technology of learning curves.  It was developed in 18852 and has been used in industries like aviation, even before the transistor was invented, to predict the future cost per airplane when a certain cumulative unit volume was achieved.  TI’s unique approach for semiconductors lay in the use of the learning curve to drive a pricing strategy early in the life of a new component.

机器翻译(仅供参考):图1是半导体行业所有可预测参数中最基本的参数,甚至比摩尔定律更为重要。 这是晶体管的学习曲线。 自1954年以来,每个晶体管的收入(可能是每个晶体管的成本,如果我们有制造商的数据)遵循高度可预测的学习曲线。 在摩尔定律之前,学习曲线为半导体行业提供了指路明灯。 德州仪器将其用于战略优势,并与波士顿咨询集团分享其数据,波士顿咨询集团出版了一本名为“观察经验”的书1 。 在锗和硅分立晶体管的时代,像TI这样的公司可以利用学习曲线,根据生产的前1000个单位的每单位实际成本,预测生产100,000个单位后的单位成本。 然后,他们可能会对特定的晶体管产品进行初步定价以获得领先的市场份额,从而在未来的单位销售量到达时实现更高的盈利能力和市场影响力。 并不是TI创建了学习曲线的技术。 它发展于1885年2 ,甚至在晶体管发明之前就被用于航空等行业,以预测当达到一定累积单位体积时每架飞机的未来成本。 TI针对半导体的独特方法在于利用学习曲线来驱动半导体在新组件生命周期早期推动定价策略。

图1. 1954年至2019年晶体管的学习曲线

Figure 2 shows how the learning curve works.  The vertical axis is the logarithm of the cost per unit of anything that is produced.  The product can be a good or service; anything that benefits from the experience of doing the same thing, or making the same product, again and again.  Published learning curves typically use the revenue per unit because companies are unwilling to divulge their cost data.  The companies, however, know their costs and, over the history of the semiconductor industry, have used that data to strategically position themselves against competition.  The horizontal axis of the learning curve is the logarithm of the cumulative number of units of a product or service that have been produced throughout history. When the data is plotted, it results in a straight line with a downward slope.  Cost per unit decreases monotonically as we develop more experience, or “learning”.  Since the learning curve is a “log/log” graph, the data generates a line rapidly initially as the small number of cumulative units doubles in a short period of time.  As time goes on, movement of the straight line to the right slows since it takes longer to double the total cumulative number of units.  Every time the cumulative number of units produced doubles, the line reflects a decrease in the cost per unit by a fixed percentage.  The percentage is different for different products but tends to be similar across a broad range of products in an industry like semiconductors.

机器翻译(仅供参考):图2显示了学习曲线的工作原理。 纵轴是生成的任何单位成本的对数。 该产品可以是一个好的或服务; 任何从一次又一次地做同样事情或制作相同产品的经验中获益的东西。 公布的学习曲线通常使用每单位收入,因为公司不愿意透露其成本数据。 然而,这些公司知道它们的成本,并且在半导体行业的历史中,已经使用这些数据来战略性地将自己定位于竞争。 学习曲线的水平轴是在整个历史中生成的产品或服务的累积单位数的对数。 绘制数据时,会产生一条向下倾斜的直线。 随着我们开发更多经验或“学习”,每单位成本单调减少。 由于学习曲线是“对数/对数”图形,所以数据最初快速生成线,因为少量累积单位在短时间内加倍。 随着时间的推移,直线向右移动的速度变慢,因为需要更长的时间才能使总累计单位数增加一倍。 每增加一个单位的累计数量,该线就会反映每单位成本减少一个固定的百分比。 不同产品的百分比是不同的,但在半导体行业的各种产品中往往相似。

图2.学习曲线是每单位成本与制造累积单位的对数/对数图

More broadly, learning curves can be applied to any good or service where the cost per unit of production can be measured. We are just not as aware of the phenomenon today because the measurement applies only when cost is measured in constant currency.  A deflator must therefore be applied to the cost numbers to account for the portion of inflation that is caused by governmentally driven inflation.  In addition, the learning curve only applies in free markets. Tariffs, trade barriers, taxes and other costs must be removed before actual cost comparisons can be made.  The reason that learning curves have been so valuable in the semiconductor industry is that it is one of the few industries that has operated for over sixty years in a relatively free worldwide market, with minimal regulation and tariffs as well as a very low cost of freight between regions.

机器翻译(仅供参考):更广泛地说,学习曲线可以应用于可以测量每单位生产成本的任何商品或服务。 我们现在还没有意识到这种现象,因为只有当成本以固定货币计量时才适用。 因此,必须将平减指数应用于成本数字,以说明由政府推动的通货膨胀引起的通货膨胀部分。 此外,学习曲线仅适用于自由市场。 在进行实际成本比较之前,必须删除关税,贸易壁垒,税收和其他成本。 学习曲线在半导体行业中如此有价值的原因在于它是在相对自由的全球市场中运营六十多年的少数几个行业之一,其监管和关税最低,运费成本极低区域之间。

One of the great things about semiconductor learning curves is that they will be applicable as long as transistors, or equivalent switches, are produced. While Moore’s Law is quickly becoming obsolete, the learning curve will never be.  What will happen, however, is that the cumulative number of transistors produced will stop moving so quickly to the right on the logarithmic scale.  Then the prices will not decrease as rapidly as they have in the past. The visible effect of improved learning will diminish.  At some point, monetary inflation will be larger than the manufacturing cost reduction and transistor unit prices may actually increase with time in absolute dollars even though they are decreasing in constant currency. In the meantime, the learning curve is a useful guidepost for predicting the future. Currently, in 2019, the revenue per transistor is decreasing about 32% per year.

机器翻译(仅供参考):半导体学习曲线的一大优点是,只要生产晶体管或等效开关,它们就适用。 虽然摩尔定律很快就会过时,但学习曲线永远不会过时。 然而,将会发生的是,所产生的晶体管的累积数量将在对数刻度上如此快速地向右移动。 然后价格不会像过去那样迅速下降。 改善学习的明显效果将会减弱。 在某些时候,货币通胀将大于制造成本的降低,晶体管单位价格实际上可能会以绝对美元随时间增加,即使它们以固定汇率递减。 与此同时,学习曲线是预测未来的有用指南。 目前,在2019年,每晶体管的收入每年减少约32%。

Those who purchase microprocessor or “system on chip” (SoC) components may recognize that, in 2017, the price per transistor is decreasing at a slower rate than 32% per year. Figure 3 explains this. The 32% number applies to the total of all semiconductor components produced in 2017. However the cost per transistor is made up of different kinds of semiconductor components — memory, logic, analog, etc.  It becomes apparent from Figure 3 that the semiconductor industry is producing far more transistors in discrete memory components, particularly NAND FLASH nonvolatile memories, than in other types of semiconductors.  When the memory learning curve (consisting mostly of NAND FLASH and DRAM) is separated from the non-memory learning curve, it is evident that cost per transistor and cumulative unit volume for memory are way ahead of non-memory.  That’s okay because the learning curve doesn’t specify how the decreasing cost per transistor is achieved – only that it will happen as a function of cumulative transistors produced.

机器翻译(仅供参考):那些购买微处理器或“片上系统”(SoC)组件的人可能会认识到,在2017年,每个晶体管的价格正在以低于每年32%的速度递减。 图3解释了这一点。 32%的数量适用于2017年生产的所有半导体元件。然而,每个晶体管的成本由不同类型的半导体元件组成 - 存储器,逻辑,模拟等。从图3中可以看出,半导体工业是与其他类型的半导体相比,在分立存储器组件中产生更多的晶体管,特别是NAND FLASH非易失性存储器。 当存储器学习曲线(主要由NAND FLASH和DRAM组成)与非存储器学习曲线分离时,显然每个晶体管的成本和存储器的累积单位体积远远超过非存储器。 这没关系,因为学习曲线没有说明每个晶体管的成本是如何降低的 - 只是它会随着累积晶体管的产生而发生。

图3.存储器组件中使用的晶体管的累积单位体积比其他类型芯片中的晶体管单位体积增加得快得多。

Another aspect of interest in Figure 3 is the set of data points near the end of the curve that were generated by data from 2017 and 2018.  The data points are above the learning curve trend line. How can this happen if the learning curve is a true law of nature?  Very simply, the period from 2016 through 2018 was one of memory shortages, particularly DRAM.  Prices per transistor increased instead of decreasing because market demand exceeded supply.  Won’t this cause a long- term deviation from the learning curve?  No.  Whenever a market supply/demand imbalance occurs, the cost per transistor moves above or below the long-term trend line of the learning curve.  This is always a temporary move.  When supply and demand come back in balance, the cost per transistor will move to the other side of the learning curve.  Area generated above the learning curve will normally be compensated by a nearly equal area below the learning curve and vice versa.  This is another useful benefit of the learning curve because it allows us to predict the general trend of future prices even when short term market forces cause a perturbation.

机器翻译(仅供参考):图3中感兴趣的另一方面是靠近曲线末端的数据点集合,其由2017年和2018年的数据生成。数据点在学习曲线趋势线之上。 如果学习曲线是真正的自然法则,怎么会发生这种情况呢? 很简单,从2016年到2018年这段时间是内存短缺,特别是DRAM。 由于市场需求超过供应,每晶体管价格上涨而不是下降。 这不会导致长期偏离学习曲线吗? 不会。每当市场供需不平衡发生时,每个晶体管的成本会高于或低于学习曲线的长期趋势线。 这始终是一个暂时的举动。 当供需恢复平衡时,每个晶体管的成本将转移到学习曲线的另一侧。 在学习曲线上方生成的区域通常将由学习曲线下方的几乎相等的区域补偿,反之亦然。 这是学习曲线的另一个有用的好处,因为它允许我们预测未来价格的总体趋势,即使短期市场力量引起扰动。

While I’ve focused on transistors in this discussion of learning curves, it should be noted that we could just as easily use electrical “switches” as our unit of measure.  The same learning curve would then work for mechanical switches, vacuum tubes and transistors as seen in Figure 5 of Chapter 3.  This figure also shows another attribute of the learning curve.  In this case, the metric on the vertical axis is revenue per MIP (or millions of computer instructions per second) for various types of electrical switches.  Learning curves can be used to predict improvements in performance, reliability (in FITS), power dissipation and many other parameters that benefit from the cumulative unit volume of production experience.

机器翻译(仅供参考):虽然我在学习曲线的讨论中专注于晶体管,但应该注意的是,我们可以很容易地使用电气“开关”作为我们的测量单位。 然后,相同的学习曲线将适用于机械开关,真空管和晶体管,如第3章图5所示。该图还显示了学习曲线的另一个属性。 在这种情况下,垂直轴上的度量是各种类型的电气开关的每MIP(或每秒数百万计算机指令)的收益。 学习曲线可用于预测性能,可靠性(FITS),功耗以及许多其他参数的改进,这些参数可从累积单位生产体验中受益。

Learning curves also provide a useful tool for predicting “tipping points” for new technology adoption.  A good example is the introduction of “compression technology” in the semiconductor test industry in 2001.  In hindsight, a major innovation like this was inevitable just by examining the learning curve for the cost of testing a transistor in an integrated circuit (Figure 4). The ATE cost learning curve was not parallel to the silicon transistor learning curve and had a less steep slope.  Industry ATE cost was not decreasing fast enough.

机器翻译(仅供参考):学习曲线还为预测新技术采用的“临界点”提供了有用的工具。 一个很好的例子是2001年在半导体测试行业引入“压缩技术”。事后看来,仅仅通过检查集成电路中晶体管测试成本的学习曲线,这样的重大创新是不可避免的(图4) 。 ATE成本学习曲线与硅晶体管学习曲线不平行,斜率较小。 行业ATE成本没有快速下降。

The ATE industry should have seen that change was inevitable.  Pat Gelsinger, in his Design Automation Conference Keynote address in 1999 highlighted his prediction that “in the future, it may cost more to test a transistor than to manufacture it”.  Such a prediction would have occurred had it not been for compression technology (also called “embedded deterministic test”) which started out in 2001 with a 10X improvement in the number of “test vectors” required to achieve the same level of test and then progressed to nearly 1000X by 20183.

机器翻译(仅供参考):ATE行业应该已经看到变化是不可避免的。 Pat Gelsinger在1999年的设计自动化大会主题演讲中强调了他的预测,“在未来,测试晶体管可能比制造晶体管花费更多”。 如果没有压缩技术(也称为“嵌入式确定性测试”),这种预测就会发生,压缩技术始于2001年,实现相同测试水平所需的“测试向量”数量增加10倍,然后进展到2018年将近1000倍3 。

Figure 4.  Until 2001, reduction in the revenue per transistor of the automated test equipment industry was decreasing at a slower rate than the transistors produced by their customers, the semiconductor component industry.

机器翻译(仅供参考):图4.自2001年以来,自动测试设备行业每个晶体管的收入减少速度正在下降,其速度低于客户半导体元件行业生产的晶体管。

Introduction of “embedded deterministic test”, or test compression, in 2001 significantly reduced the number of testers required and, by 2012, reduced the revenue of the ATE industry by $25B per year.

机器翻译(仅供参考):2001年引入的“嵌入式确定性测试”或测试压缩显着减少了所需的测试人员数量,到2012年,ATE行业每年的收入减少了25亿美元。

参考文献:

1 Boston Consulting Group, “Perspectives on Experience”, 1970, Boston, MA

2https://en.wikipedia.org/wiki/Learning_curve#In_machine_learning

3Rajski, J., Tyszer, J., Kassab, M. and Mukherjee, N., “Embedded DeterministicTest”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems ( Volume: 23 , Issue: 5 , May 2004

· 2019-07-16 09:25  本新闻来源自:IC智库,版权归原创方所有

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