欢迎来到得力文库 - 分享文档赚钱的网站! | 帮助中心 好文档才是您的得力助手!
得力文库 - 分享文档赚钱的网站
全部分类
  • 研究报告 >
    研究报告
    其他报告 可研报告 医学相关 环评报告 节能报告 项目建议 论证报告 机械工程 设计方案 版权保护 对策研究 可行性报告 合同效力 饲养管理 给排水 招标问题
  • 管理文献 >
    管理文献
    管理手册 管理方法 管理工具 管理制度 事务文书 其他资料 商业计划书 电力管理 电信行业策划 产品策划 家电策划 保健医疗策划 化妆品策划 建材卫浴策划 酒水策划 汽车策划 日化策划 医药品策划 策划方案 财务管理 企业管理
  • 标准材料 >
    标准材料
    石化标准 机械标准 金属冶金 电力电气 车辆标准 环境保护 医药标准 矿产资源 建筑材料 食品加工 农药化肥 道路交通 塑料橡胶
  • 技术资料 >
    技术资料
    施工组织 技术标书 技术方案 实施方案 技术总结 技术规范 国家标准 行业标准 地方标准 企业标准 其他杂项
  • 教育专区 >
    教育专区
    高考资料 高中物理 高中化学 高中数学 高中语文 小学资料 幼儿教育 初中资料 高中资料 大学资料 成人自考 家庭教育 小学奥数 单元课程 教案示例
  • 应用文书 >
    应用文书
    工作报告 毕业论文 工作计划 PPT文档 图纸下载 绩效教核 合同协议 工作总结 公文通知 策划方案 文案大全 工作总结 汇报体会 解决方案 企业文化 党政司法 经济工作 工矿企业 教育教学 城建环保 财经金融 项目管理 工作汇报 财务管理 培训材料 物流管理 excel表格 人力资源
  • 生活休闲 >
    生活休闲
    资格考试 党风建设 休闲娱乐 免费资料 生活常识 励志创业 佛教素材
  • 考试试题 >
    考试试题
    消防试题 微信营销 升学试题 高中数学 高中政治 高中地理 高中历史 初中语文 初中英语 初中物理 初中数学 初中化学 小学数学 小学语文 教师资格 会计资格 一级建造 事业单位考试 语文专题 数学专题 地理专题 模拟试题库 人教版专题 试题库答案 习题库 初中题库 高中题库 化学试题 期中期末 生物题库 物理题库 英语题库
  • pptx模板 >
    pptx模板
    企业培训 校园应用 入职培训 求职竞聘 商业计划书 党政军警 扁平风格 创意新颖 动态模版 高端商务 工作办公 节日庆典 静态模板 卡通扁平 融资路演 述职竟聘 图标系列 唯美清新 相册纪念 政府汇报 中国风格 商业管理(英) 餐饮美食
  • 工商注册 >
    工商注册
    设立变更 计量标准 广告发布 检验检测 特种设备 办事指南 医疗器械 食药局许可
  • 期刊短文 >
    期刊短文
    信息管理 煤炭资源 基因工程 互联网 农业期刊 期刊 短文 融资类 股权相关 民主制度 水产养殖 养生保健
  • 图片设计 >
    图片设计
    工程图纸
  • 换一换
    首页 得力文库 - 分享文档赚钱的网站 > 资源分类 > DOCX文档下载
     

    222页面提取自-Modern Lens Design (1).docx

    • 资源ID:1114       资源大小:102.03KB        全文页数:8页
    • 资源格式: DOCX        下载权限:游客/注册会员    下载费用:2金币 【人民币2元】
    快捷注册下载 游客一键下载
    会员登录下载
    三方登录下载: 微信快捷登录 QQ登录  
    下载资源需要2金币 【人民币2元】
    邮箱/手机:
    温馨提示:
    支付成功后,系统会自动生成账号(用户名和密码都是您填写的邮箱或者手机号),方便下次登录下载和查询订单;
    支付方式: 微信支付    支付宝   
    验证码:   换一换

     
    友情提示
    2、PDF文件下载后,可能会被浏览器默认打开,此种情况可以点击浏览器菜单,保存网页到桌面,既可以正常下载了。
    3、本站不支持迅雷下载,请使用电脑自带的IE浏览器,或者360浏览器、谷歌浏览器下载即可。
    4、本站资源下载后的文档和图纸-无水印,预览文档经过压缩,下载后原文更清晰   

    222页面提取自-Modern Lens Design (1).docx

    Chapter 2 Automatic Lens Design; Managing the Lens Design Program 2.1 The Merit Function What is usually referred to as automatic lens design is, of course, nothing of the sort. The computer programs which are so described are actually optimization programs which drive an optical design to a local optimum, as defined by a merit function which is not a true merit function, but actually a defect function. In spite of the preceding disclaimers, we will use these commonly accepted terms in the discussions which follow. Broadly speaMng, the merit function can be described as a combination or function of calculated characteristics, which is intended to completely describe, with a single number, the value or quality of a given lens design. This is obviously an exceedingly difficult thing to do. The typical merit function is the sum of the squares of many image defects; usually these image defects are uated for three locations in the field of view unless the system covers a very large or a very small angular field. The squares of the defects are used so that a negative value of one defect does not offset a positive value of some other defect. The defects may be of many different kinds; usually most are related to the quality of the image. However, any characteristic which can be calculated may be assigned a target value and its departure from that target regarded as a defect. Some less elaborate programs utilize the third-order Seidel aberrations; these provide a rapid and efficient way of adjusting a design. These cannot be regarded as optimizing the image quality, but they do work well in correcting ordinary lenses. Another type of merit function traces a large number of 3 4 Chapter Two rays from an object point. The radial distance of the image plane intersection of the ray from the centroid of all the ray intersections is then the image defect. Thus the merit function is effectively the sum of the root-mean-square rms spot sizes for several field angles. This type of merit function, while inefficient in that it requires many rays to be traced, has the advantage that it is both versatile and in some ways relatively foolproof. Some merit functions calculate the values of the classical aberrations, and convert or weight them into their equivalent wavefront deations. See ulary Sec. F-12 for the conversion factors for several common aberrations. This approach is very efficient as regards computing time, but requires careful design of the merit function. Still another type of merit function uses the variance of the wavefront to define the defect items. The merit function used in the various David Grey programs is of this t3rpe, and is certainly one of the best of the commercially available merit functions in producing a good balance of the aberrations. Characteristics which do not relate to image quality can also be controlled by the lens design program. Specific construction parameters, such as radii, thicknesses, spaces, and the like, as well as focal length, working distance, magnification, numerical aperture, required clear apertures, etc., can be controlled. Some programs include such items in the merit function along with the image defects. There are two drawbacks which somewhat offset the neat simplicity of this approach. One is that if the first-order characteristics which are targeted are not initially close to the target values, the program may correct the image aberrations without controlling these first-order characteristics; the result may be, for example, a well-corrected lens with the wrong focal length or numerical aperture. The program often finds this to be a local optimum and is unable to move away from it. The other drawback is that the inclusion of these items in the merit function has the effect of slowing the process of improving the image quality. An alternative approach is to use a system of constraints outside the merit function. Note also that many of these items can be controlled by features which are included in almost all programs, namely angle-solves and height-solves. These algebraically solve for a radius or space to produce a desired ray slope or height. In any case, the merit function is a summation of suitably weighted defect items which, it is hoped, describes in a single number the worth of the system. The smaller the value of the merit function, the better the lens. The numerical value of the merit function depends on the construction of the optical system; it is a function of the construction parameters which are designated as variables. Without getting into the details of the mathematics involved, we can realize that the merit function is an n-dimensional space, where n is the number of the vari Automatic Lens Design 5 able constructional parameters in the optical system. The task of the design program is to find a location in this space i.e., a lens prescription or a solution vector which minimizes the size of the merit function. In general, for a lens of reasonable complexity there will be many such locations in a typical merit function space. The automatic design program will simply drive the lens design to the nearest minimum in the merit function. 2.2 Optimization The lens design program typically operates this way Each variable parameter is changed one at a time by a small increment whose size is chosen as a compromise between a large value to get good numerical accuracy and a small value to get the local differential. The change produced in every item in the merit function is calculated. The result is a matrix of the partial derivatives of the defect items with respect to the parameters. Since there are usually many more defect items than variable parameters, the solution is a classical least- squares solution. It is based on the assumption that the relationships between the defect items and the variable parameters are linear. Since this is usually a false assumption, an ordinary least-squares solution will often produce an unrealizable lens or one which may in fact be worse than the starting design. The damped least-squares solution, in effect, adds the weighted squares of the parameter changes to the merit function, heavily penalizing any large changes and thus limiting the size of the changes in the solution. The mathematics of this process are described in Spencer, “A Flexible Automatic Lens Correc- tion Program,n Applied Optics, vol. 2, 1963, pp. 1257-1264, and by Smith in W. Driscoll ed., Handbook of Optics, McGraw-Hill, New York, 1978. If the changes are small, the nonlinearity will not ruin the process, and the solution, although an approximate one, will be an improvement on the starting design. Continued repetition of the process will eventually drive the design to the nearest local optimum. One can visualize the situation by assuming that there are only two variable parameters. Then the merit function space can be compared to a landscape where latitude and longitude correspond to the variables and the elevation represents the value of the merit function. Thus the starting lens design is represented by a particular location in the landscape and the optimization routine will move the lens design downhill until a minimum elevation is found. Since there may be many depressions in the terrain of the landscape, this optimum may not be the best there is; it is a local optimum and there can be no as- surance except in very simple systems that we have found a global 6 Chapter Two optimum in the merit function. This simple topological analogy helps to understand the dominant limitations of the optimization process the program finds the nearest minimum in the merit function, and that minimum is uniquely determined by the design coordinates at which the process is begun. The landscape analogy is easy for the human mind to comprehend; when it is extended to a 10- or 20- dimension space, one can realize only that it is apt to be an extremely complex neighborhood. 2.3 Local Minima Figure 2.1 shows a contour map of a hypothetical two-variable merit function, with three significant local minima at points Ay By and C; there are also three other minima at D, E, and F. It is immediately apparent that if we begin an optimization at point Z, the minimum at point B is the only one which the routine can find. A start at Y on the ridge at the lower left will go to the minimum at C. However, a start Figure 2.1 Topography of a hypothetical two-variable merit function, with three significant minima A, B, C and three trivial minima D, Ey F. The minimum to which a design program will go depends on the point at which the optimization process is started. Starting points X, Y} and Z each lead to a different design minimum; other starting points can lead to one of the trivial minima. Automatic Lens Design 7 at X} which is only a short distance away from Y} will find the best minimum of the three, at point A. If we had even a vague knowledge of the topography of the merit function, we could easily choose a starting point in the lower right quadrant of the map which would guarantee finding point A. Note also that a modest change in any of the three starting points could cause the program to stagnate in one of the trivial minima at D3 Et or F. It is this sort of minimum from which one can escape by “jolting” the design, as described below. The fact that the automatic design program is severely limited and can find only the nearest optimum emphasizes the need for a knowledge of lens design, in order that one can select a starting design which is close to a good optimum. This is the only way that an automatic program can systematically find a good design. If the program is started out near a poor local optimum, the result is a poor design. The mathematics of the damped least-squares solution involves the inversion of a matrix. In spite of the damping action, the process can be slowed or aborted by either of the following conditions 1 A variable which does not change or which produces only a very small change in the merit function items. 2 Two variables which have the same, nearly the same, or scaled effects on the items of the merit function. Fortunately, these conditions are rarely met exactly, and they can be easily avoided. If the program settles into an unsatisfactory optimum such as those at D, E} and F in Fig. 2.1 it can often be jolted out of it by manually introducing a significant change in one or more parameters. The trick is to make a change which is in the direction of a better design . Again, a knowledge of lens designs is virtually a necessity. Sometimes simply freezing a variable to a desirable can be sufficient to force a move into a better neighborhood. The difficulty is that too big a change may cause rays to miss surfaces or to encounter total internal reflection, and the optimization process may break down. Con- versely, too small a change may not be sufficient to allow the design to escape from a poor local optimum. Also, one should remember that if the program is one which adjusts optimizes the damping factor, the factor is usually made quite small near an optimum, because the program is taking small steps and the situation looks quite linear; after the system is jolted, it is probably in a highly nonlinear region and a big damping factor may be needed to prevent a breakdown. A manual increase of the damping factor can often avoid this problem. Another often-encountered problem is a design which persists in moving to an obviously undesirable when you know that there is a mudi better, very different onethe one that you want. Freezing the of one part of the lens for a few cycles of optimization will often allow the rest of the lens to settle into the neighborhood of the 8 Chapter Two desired optimum. For example, if one were to try to convert a Cooke triplet into a split front crown , the process might produce either a which is like the original triplet with a narrow airspaced crack in the front crown, or a with rather wild meniscus elements. A technique which will usually avoid these unfortunate local optima in this case is to freeze the front element to a plano-convex by fixing the second surface to a plane for a few qycles of optimization. Again, one must know which lens s are the good ones. 2.3 Types of Merit Functions Many programs allow the user to define the merit function. This can be a valuable feature because it is almost impossible to design a truly universal merit function. As an example, consider the design of a simple Fraunhofer telescope objective a merit function which controls the spherical and chromatic aberrations of the axial marginal ray and the coma of the oblique ray bundle plus the focal length is all that is necessary. If the design complexity is increased by allowing the airspace to vary and/or adding another element, the merit function may then profitably include entries which will control zonal spherical, spherochromatism, and/or fifth-order coma. But as long as the lens is thin and in contact with the aperture stop, it would be foolish to include in the merit function entries to control field curvature and astigmatism. There is simply no way that a thin stop-in-contact lens can have any control over the inherent large negative astigmatism; the presence of a target for this aberration in the merit function will simply slow down the solution process. It would be ridiculous to use a merit function of the type required for a photographic objective to design an ordinary telescope objective. Indeed, an attempt to correct the field curvature may lead to a eomprdmise design with a severely undercorrected axial spherical aberration which, in combination with coma, may fool the computer program into thiiiking that it has found a useful optimum. There are many design tasks in this category, where the require- ments are effectively limited in number and a simple, equally limited merit function is clearly the best choice. In such cases, it is usually obvious that some specific state of correction will yield the best results; there is no need to balance the correction of one aberration against another. More often, however, the situation is not so simple; compromises and balances are required and a more complex, suitably weighted merit function is necessary. This can be a delicate and somewhat tricky matter. For example, in the design of a lens with a significant aperture and field, there is almost always a poor local optimum in Automatic Lens Design 9 which 1 the spherical aberration is left quite undereorrected,( 2 a compromise focus is chosen well inside the paraxial focus, 3 the Petzval field is made inward-curving, and 4 overcorrected oblique spherical aberration is introduced to “balance” the design. A program which relies on the rms spot radius for its merit function is very likely to fall into this trap. A better d

    注意事项

    本文(222页面提取自-Modern Lens Design (1).docx)为本站会员(admin)主动上传,得力文库 - 分享文档赚钱的网站仅提供信息存储空间,仅对用户上传内容的表现方式做保护处理,对上载内容本身不做任何修改或编辑。 若此文所含内容侵犯了您的版权或隐私,请立即通知得力文库 - 分享文档赚钱的网站(发送邮件至8877119@qq.com或直接QQ联系客服),我们立即给予删除!

    温馨提示:如果因为网速或其他原因下载失败请重新下载,重复下载不扣分。




    关于得利文库 - 版权申诉 - 免责声明 - 上传会员权益 - 联系我们

    工信部备案号:黑ICP备15003705号-8 |经营许可证:黑B2-20190332号 |营业执照:91230400333293403D|公安局备案号:备案中

    © 2017-2019 www.deliwenku.com 得利文库. All Rights Reserved 黑龙江转换宝科技有限公司  


    收起
    展开