mpw-002 / CTM1 Contact

the CTM1 shapes of all of mpw-002

40 of 40 dies rendered.

413,541,651 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (142.1 MB in total, largest 8.8 MB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-002_CTM1_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-002_CTM1_reticle.png
the stitched reticle, full resolution
82.5 MB df5e4819505e6162f7aa02ecf63a99de657ca7f1de6cc212ea12fc7ea483c6cae0ee23d1 7c502c06875c941b3a21391b71c11d2ac7d247babe9a5c7d4b6496b59ed4fb1c
mpw-002_CTM1_reticle_overview.png
the same image scaled to fit 4096 px
1.5 MB 48a44731daa6e31707d9ce3523a6b359a252f78286112b09173dcfea82ee7ecf20aa9002 14b6f8e6cea2d2a62c436ee6119ed0bde99eb142ee8c501cd6f5213932d708fb
mpw-002_CTM1_tiles.json
where every tile belongs, and its checksum
13.3 kB fd0bc4763af5f556819439610989a4bebc5c640e04b6ad753cde44a57ba4125c38b3235c 639f3059639f2511cde06a5bae42b9b6414e01b0fa5b09bc64162334e68dcbf6
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 axi_dma_using_spinal 7576×107760, 0 3.5 MB3a007a7d48b6e45b2d448278aa98028f00bce9ce926c17ea42f82fc71a83d3e5
A2 wishbone_can 7576×107767576, 0 3.6 MBae2433e0715ba300b99cc96be022c6a80a1cfd3f8ce82a4a5d4b6a2cbf65684a
A3 Analog-Neural-Network 7576×1077615152, 0 2.4 MB3da8fc61579b482c2c08ec8b5d21c7adf89488f92a347523a6fa8f0af8f50881
A4 caravel_dsp2 7576×1077622728, 0 3.0 MB46a02cac8d088601b223fd899a444c96007b16dec7e55a41295efa3068d9dac9
A5 eFPGA_v3_caravel 7576×1077630304, 0 5.9 MB197523298bf91957c63adb93f84620cc20b840243d1f4908e4a67547174885bb
A6 opencryo_testchip 7576×1077637880, 0 2.4 MBa8d2d5eff823ad18853eff2ede00127f87a1aa565aaf52d37908be58cfc266cb
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 yiFive 7576×107760, 10776 4.4 MBf0de1bd49f07df864d2e9a0d2d155e4700e005d8bcca8833aaa06687def799d8
B2 lexicon 7576×107767576, 10776 3.9 MB7f0527dee2521bcb6bdeb440011d44e5785a5ebebad694490c02a200da9b868b
B3 ucsc_openram_test_chip_v2 7576×1077615152, 10776 3.7 MBa71b5d16201da3bc969b8cf6590b745de55c0fac99db1590506ac2ee2282add3
B4 yonga-lz4_decoder 7576×1077622728, 10776 2.9 MB42701a778082a5331146c83dfe659fd848c19040f6eb5fafddd732a938874a71
B5 Potentiometric_Digit 7576×1077630304, 10776 2.4 MB47742fdbeafd28045384834ca95a9007518a0cd4e5ebda6edcede8c056cbfa51
B6 fuserisc_ver2 7576×1077637880, 10776 5.9 MB5cec53c1db4e2b85d3bb3e4e473359bddb04d4c4c971defe1a511c6af9be8d7f
B7 amsat_txrx_ic_mpw2 7976×1077645456, 10776 2.5 MBe84a80edf5a2690c486232314491b68c93be89ef1f08902ff7c4900920d54120
C1 Comparator_3v3 7576×107760, 21552 2.4 MBc8a20c9d9f03af5ebccff1aa7aec977e2157ef797b9668af9ab2700854852809
C2 caravel_multi_encode 7576×107767576, 21552 2.8 MB6c00a4eb33a1a532d4ad66106e161920deacfc098a85f76570411a0c473d9de3
C3 kasirgalabs_uart 7576×1077615152, 21552 2.6 MB335a98dcfe8db46c0767837b14cd33c86125f08f2616cee19074772c729c90b1
C4 Automatically_genera 7576×1077622728, 21552 2.5 MB96140d41cc11337aef12e9082848ccbaca7bf404defce9e9553dc928f4f7e4dc
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 uqab 7576×1077637880, 21552 5.7 MBd1b5666289a185a85ec3acb6a69b8e4b9a3c574f916722017f80a5786220ab84
C7 caravel_azadi_soc 7976×1077645456, 21552 4.8 MB733a779a8a95e9670216e609e912aa152f4f4bf7b9d092691b7ce99d16f6d824
D1 caravel_dsc 7576×107760, 32328 3.1 MB133a35ca0e65ae92f9b338ddc1a98338317ae3512b75d9c61d89ea8db9d5c20e
D2 SOFA-Plus-eFPGA 7576×107767576, 32328 5.0 MB3114bb98b759d78dc1d4ae5d72d8b5a9ee683ed6fbc165d264991b497fd477d4
D3 caravel_user_project 7576×1077615152, 32328 2.6 MB5ca5eaa2eec7e30af373b85c04be78119266edb510bc4ee73c43f0de0d75c66d
D4 General_Purpose_Band 7576×1077622728, 32328 2.5 MB941875252a9bcce8df43615dd9027d5a935b2735aeb6022fe3397d70ff3418dc
D5 zero_to_asic 7576×1077630304, 32328 3.4 MB8f1f031ddcc04dbb4e1903a0ee209fe4cccabeff2e1dbc169d9cbd218091114c
D6 fast_gcd_for_large_i 7576×1077637880, 32328 8.8 MBa4ab9263a4a1a7f62c2c030caac70986859d675fc15194bfc499f11826d28f14
D7 Columbus 7976×1077645456, 32328 2.4 MB4f7b26c9c8a0a05ccd96d26322b8664e6a44122c20bf245fb8d27991333afc6a
E1 Space_shuttle 7576×107760, 43104 4.3 MBe6e00b293523da2e3659a11a4652ffbd4cae80f4423b75c8533f41e1d9db1b1f
E2 subservient 7576×107767576, 43104 2.9 MB62e35a4fb94f9f5c8a001d1d69804edd84f296939339eee57f1c02876ee9cb7c
E3 caravel_analog_fulgor 7576×1077615152, 43104 2.4 MBab24f473c3b4974b39a5af842e6043aeea863a1c35bae3265d85aa9a2594fde5
E4 bandgap_reference_de 7576×1077622728, 43104 2.5 MB540b13d9282044e59c685b929c4f86606047b49a40d735aab6ddc3f2ecfc5a39
E5 renml 7576×1077630304, 43104 5.6 MBc2accf9da59a499cb320f05ab8e238d54d2a2b194fc3d34026f4c79b08e634dc
E6 io_expander 7576×1077637880, 43104 2.5 MB217a7b0654378c989f1dd133f5e2f72eb24e0a28b18209ebdf8968e3c9a64221
E7 digital_pll 7976×1077645456, 43104 2.5 MBd82b1f3c6af5ba0ecb5db82e7b8da77964fc268a908e1c25017664212e7d0052
F1 zero_to_asic 7576×111760, 53880 3.4 MBabba0b9aa64d9cebba729f6bc89438f0e72fccaa330889f99f656121cc0364c0
F2 caravel_fpu 7576×111767576, 53880 2.9 MB85030983a19d8b20b218e2f556896ee3f4f049191fb4e12650fd3ff7beb6b463
F3 BrqRV_EB1 7576×1117615152, 53880 6.0 MB48422bd773ca5f6cafbaf78acd8d90f7bc13e5238f3eac9dff25d4ba4969bcf2
F4 hilas 7576×1117622728, 53880 2.4 MBb03b29fde400023ca7c15ea71edb4467e481db665a60357664ae6a02ddbb3e32
F5 vlsi-sky130-analog-c 7576×1117630304, 53880 2.4 MB1891157f72d05f944ad24be2a757382e989dbaee70b4b9d9cc32955787b24eea
F6 sha1_engine 7576×1117637880, 53880 3.1 MBfc9bffd9cf6455908e6816a32737cb4336502c765a5fc1fb7237cedcbc255544
F7 caravan_openfasoc 7976×1117645456, 53880 3.7 MBb8963f33347601ed7123a8034b04f50fe7a1bfc14335a7e1b02ee7c3e9b327ee

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About CTM1 on mpw-002

Resolved: reticle 5CS8007AC, run mpw-002, mask CTM1

Mask layer
CTM1 — Contact
Process step
107 — Metal contact mask
Drawn layer
67:44
Layer on the mask-layer GDS
35:0
Detail
CTM1 = mcon (li1->met1)
Also
Via 1 (LI/M0→M1)
Used in SKY130
yes

mpw-002 — SKY130 MPW-2

Foundry product
EF MPW#2
Reticle set
5CS8007AC
Run date
2021-06-18
Submissions closed
2021-06-18
Projects
56
Fab lot
4205819
Identification
confirmed — Lot records: EF MPW#2, lot 4205819 = 'SKY130 MPW 2'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-2 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-002/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.